diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 63856a2..0000000 --- a/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -.env -*.pyc -__pycache__/ -.venv/ -venv/ diff --git a/README.md b/README.md deleted file mode 100644 index af4419a..0000000 --- a/README.md +++ /dev/null @@ -1,143 +0,0 @@ -# 作战体系数据清洗与标准化系统 - -## 项目简介 -本项目用于处理异质作战体系节点的原始JSON数据。系统能够自动识别并修复数据中的噪声、缺失值、异常值，并将关键效能指标标准化至 [0, 1] 区间，最终输出符合建模要求的高质量数据集。 - -## 目录结构 -``` -project/ -├── config/ -│ ├── config.yaml # 配置文件（推荐，支持注释） -│ └── config.json # 配置文件备选 -├── data/ -│ ├── raw_data_sample.json # 原始输入数据（JSON 模式） -│ └── cleaned_data_final.json # 清洗后的输出数据 -├── src/ -│ ├── cleaner.py # 清洗运行程序 -│ └── data_loader.py # 数据加载（JSON/达梦） -├── report/ -│ └── detailed_cleaning_report.json # 详细清洗报告 -├── scripts/ -│ └── inspect_dm_tables.py # 达梦表结构查看（部署时用） -├── .env.example # 环境变量示例（含 DM_PASSWORD） -└── README.md # 项目说明文档 -``` - -## 清洗流程说明 - -程序按以下顺序依次执行五步清洗操作： - -### 1. 去重 (clean_duplicates) -- **依据**：按 `TARGET_ID` 去重，同一目标只保留一条记录 -- **策略**：若存在 `CREATED_TIME` 字段，按创建时间倒序排序后保留最新一条；否则保留第一条 -- **目的**：消除重复录入的目标节点 - -### 2. 缺失值填充 (handle_missing_values) -- **范围**：对所有数值型列（除 ID 外）进行处理 -- **策略**： - - 先按 `ROLE_ID` 分组，用同类型单位的均值填充该组内缺失值 - - 若分组后仍有缺失（如该组仅有一条记录），用全列均值兜底填充 -- **目的**：保证数值字段完整，便于后续建模 - -### 3. 异常值纠正 (correct_outliers) -- **涉及字段**：`TARGET_RECOGNITION_CAPABILITY`、`STRIKE_ACCURACY`、`ANTI_JAMMING_CAPABILITY`、`ENVIRONMENT_ADAPTABILITY`、`MOBILITY` -- **规则**： - - 若值 < 0：取绝对值 - - 若值 > 1：截断为 1.0 -- **目的**：将效能指标统一限制在 [0, 1] 区间内 - -### 4. 噪声平滑 (apply_kalman_filter) -- **对象**：`COMMUNICATION_RANGE`（通信范围）字段 -- **方法**：使用窗口大小为 3 的移动平均进行平滑（与卡尔曼滤波效果类似的时序平滑） -- **结果**：数值保留 2 位小数 -- **目的**：削弱测量噪声对连续数值的影响 - -### 5. 标准化与输出 -- 坐标精度统一为 2 位小数 -- 时间戳统一为 ISO-8601 格式 -- 输出清洗后 JSON 及详细报告（含各步骤的统计与示例） - -## 数据源配置 - -程序支持两种数据源，通过 `config/config.yaml` 或 `config/config.json` 中的 `source` 切换： - -| source | 适用场景 | 说明 | -|--------|--------------|------| -| `json` | 开发、无数据库 | 从 `data/raw_data_sample.json` 读取 | -| `db` | 生产、有达梦库 | 从达梦数据库指定表读取 | - -### 开发阶段（当前设备无达梦） - -保持 `source: json`，直接使用本地 JSON 文件即可。 - -### 生产阶段（部署到有达梦的设备） - -`OBJECTIVE_INSTANCE` 表结构与 `raw_data_sample.json` 一致，配置中已设为 `db.table: OBJECTIVE_INSTANCE`，直接查询即可。 - -1. **修改配置**：`source: db`，端口 `5080`、表 `OBJECTIVE_INSTANCE` 已预设； -2. **设置密码**：`export DM_PASSWORD=你的密码`； -3. **运行清洗**：`python src/cleaner.py`。 - -若需自定义查询（如加 WHERE 条件），可清空 `table`，在 `db.query` 中写完整 SQL。 - -### 密码注入与安全说明 - -**⚠️ 安全原则：切勿将达梦密码写入配置文件或源代码。** - -达梦模式通过环境变量 `DM_PASSWORD` 注入密码，支持以下方式： - -| 方式 | 说明 | -|------|------| -| **终端** | `export DM_PASSWORD=你的密码`（Linux/Mac）或 `set DM_PASSWORD=你的密码`（Windows CMD） | -| **PyCharm** | Run → Edit Configurations → Environment variables → 添加 `DM_PASSWORD=你的密码` | -| **.env 文件** | 复制 `.env.example` 为 `.env`，填入 `DM_PASSWORD=你的密码`，并用 `python-dotenv` 加载（需自行集成） | - -`inspect_dm_tables.py` 支持两种密码来源：优先环境变量 `DM_PASSWORD`，其次脚本内 `LOCAL_PASSWORD`。若使用 `LOCAL_PASSWORD`，务必执行 `git update-index --assume-unchanged scripts/inspect_dm_tables.py` 避免误提交。 - -配置文件（`config.yaml` / `config.json`）中仅配置 `host`、`port`、`user`、`schema`、`table` 等，**不包含 password**。本项目源代码中不包含任何硬编码密码。 - -## 快速开始 - -### 1. 环境依赖 -```bash -pip install -r requirements.txt -``` - -核心依赖：`numpy`、`pandas`、`PyYAML`。达梦模式需额外安装 `dmPython`。 - -### 2. 运行清洗 -在项目根目录下执行： - -```bash -python src/cleaner.py -``` - -默认从 JSON 读取，处理后生成： -- `data/cleaned_data_final.json`（清洗后的数据） -- `report/detailed_cleaning_report.json`（详细清洗报告） - -## 输出结果示例 - -**清洗前 (Raw):** -```json -{ - "MOBILITY": -0.5, - "STRIKE_ACCURACY": 1.5, - "COMMUNICATION_RANGE": 102.8116 -} -``` - -**清洗后 (Cleaned):** -```json -{ - "MOBILITY": 0.5, - "STRIKE_ACCURACY": 1.0, - "COMMUNICATION_RANGE": 102.81 -} -``` - -## 清洗报告说明 - -`detailed_cleaning_report.json` 中包含： -- **summary**：总记录数、最终记录数、去重数量 -- **details**：各字段缺失值填充数量、异常值修正数量及示例、噪声平滑处理记录、标准化说明 diff --git a/config/config.json b/config/config.json deleted file mode 100644 index be492a7..0000000 --- a/config/config.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "source": "json", - "json": { - "input_file": "data/raw_data_sample.json", - "output_file": "data/cleaned_data_final.json", - "report_file": "report/detailed_cleaning_report.json" - }, - "db": { - "host": "127.0.0.1", - "port": 5080, - "user": "SYSDBA", - "schema": "SYSDBA", - "table": "OBJECTIVE_INSTANCE", - "query": null - } -} diff --git a/config/config.yaml b/config/config.yaml deleted file mode 100644 index 1656374..0000000 --- a/config/config.yaml +++ /dev/null @@ -1,21 +0,0 @@ -# 数据清洗系统配置文件 -# 通过 source 切换：json（开发） / db（生产-达梦） - -source: json # 开发用 json；部署达梦时改为 db（或直接改 config.json） - -# JSON 文件模式（source=json 时生效） -json: - input_file: data/raw_data_sample.json - output_file: data/cleaned_data_final.json - report_file: report/detailed_cleaning_report.json - -# 达梦数据库模式（source=db 时生效） -# 密码通过环境变量 DM_PASSWORD 传入，切勿写入配置文件 -db: - host: 127.0.0.1 - port: 5080 - user: SYSDBA - schema: SYSDBA - # OBJECTIVE_INSTANCE 表结构与 raw_data_sample.json 一致，直接查询即可 - table: OBJECTIVE_INSTANCE - query: null diff --git a/.env.example b/final/.env.example similarity index 88% rename from .env.example rename to final/.env.example index 5798432..be7d6d7 100644 --- a/.env.example +++ b/final/.env.example @@ -1,4 +1,4 @@ # 达梦数据库密码（仅 source=db 时需要） # 复制为 .env 后填入真实密码，或将下面一行加入 ~/.bashrc 等 # export DM_PASSWORD=你的密码 -DM_PASSWORD= +DM_PASSWORD=Dm508508 diff --git a/final/.idea/.gitignore b/final/.idea/.gitignore new file mode 100644 index 0000000..eaf91e2 --- /dev/null +++ b/final/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/final/.idea/227.iml b/final/.idea/227.iml new file mode 100644 index 0000000..069558b --- /dev/null +++ b/final/.idea/227.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/final/.idea/inspectionProfiles/Project_Default.xml b/final/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..046067d --- /dev/null +++ b/final/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,38 @@ + + + + \ No newline at end of file diff --git a/final/.idea/inspectionProfiles/profiles_settings.xml b/final/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/final/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/final/.idea/misc.xml b/final/.idea/misc.xml new file mode 100644 index 0000000..b1e0d2b --- /dev/null +++ b/final/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/final/.idea/modules.xml b/final/.idea/modules.xml new file mode 100644 index 0000000..d2fbae1 --- /dev/null +++ b/final/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/final/1125暂时属性需求(1).xlsx b/final/1125暂时属性需求(1).xlsx new file mode 100644 index 0000000..9872bdf Binary files /dev/null and b/final/1125暂时属性需求(1).xlsx differ diff --git a/final/README.md b/final/README.md new file mode 100644 index 0000000..df98ce1 --- /dev/null +++ b/final/README.md @@ -0,0 +1,331 @@ +# 作战体系数据清洗与网络效能评估系统 + +## 项目简介 + +本系统包含两大功能模块： + +1. **数据清洗**：从 JSON 文件或达梦数据库读取实体属性表、三元组，进行去重、缺失值填充、异常值修正、噪声平滑等清洗；并按 **1125 属性需求** 做 **内容与格式的双重对齐**（格式校验 + 内容校验 + 原始字段→规范 schema 转换），**输出仅含 xlsx 定义字段**的 `nodes.json`，不保留原始其他字段。 +2. **网络关系与效能评估**：基于节点与三元组数据，计算关系强度、过滤低置信边，评估任务网络效能，生成评价报告。 + +--- + +## 目录结构 + +``` +final/ +├── config/ +│ ├── config.yaml # 主配置文件（推荐） +│ └── config.json # 备选配置 +├── data/ +│ ├── raw_data_sample.json # 原始实体数据（JSON 模式） +│ ├── nodes.json # 清洗后实体（关系/网络计算用） +│ └── triples.json # 清洗后三元组（来自 run_all） +├── report/ +│ ├── detailed_cleaning_report.json # 实体清洗报告 +│ └── triplet_cleaning_report.json # 三元组清洗报告 +├── results/ +│ ├── filtered_triples.json # 过滤后三元组 +│ ├── triples.csv # 三元组表格 +│ └── evaluation_report.txt # 效能评价报告 +├── src/ +│ ├── main_227.py # 主程序（清洗 + 关系 + 网络评估） +│ ├── cleaner.py # 实体清洗 +│ ├── attribute_schema.py # 1125 属性规范：格式/内容校验 + 原始→规范转换 +│ ├── data_loader.py # 数据加载（JSON/达梦） +│ └── triplet_cleaner.py # 三元组清洗 +├── scripts/ +│ ├── run_all.py # 一键运行（仅清洗） +│ ├── export_triplets.py # 单独导出三元组 +│ ├── inspect_dm_tables.py # 达梦表结构查看 +│ ├── f_relation1125.py # 关系强度计算 +│ └── f_net1125.py # 任务网络评估 +├── .env.example +└── README.md +``` + +--- + +## 快速开始 + +### 1. 环境依赖 + +```bash +pip install -r requirements.txt +# 达梦模式需额外：pip install dmPython +``` + +### 2. 运行方式 + +| 命令 | 功能 | +|------|------| +| `python src/main_227.py` | **完整流程**：实体清洗 → 关系强度计算 → 网络效能评估 | +| `python scripts/run_all.py` | **仅清洗**：实体 + 三元组 读取、清洗、导出 | + +### 3. 开发模式（无达梦数据库） + +在 `config/config.yaml` 或 `config/config.json` 中设置： + +```yaml +source: json +``` + +然后运行： + +```bash +python src/main_227.py +``` + +程序会从 `data/raw_data_sample.json` 读取实体数据并清洗；关系/网络计算则从 `data/nodes.json` 和 `data/triples.json` 读取。 + +--- + +## 配置说明 + +### 必须修改的配置 + +根据你的环境，在 `config/config.yaml` 或 `config/config.json` 中修改以下项： + +| 配置项 | 说明 | 示例 | +|--------|------|------| +| `source` | 数据源：`json`（开发）或 `db`（达梦） | `json` | +| `db.host` | 达梦库地址（source=db 时） | `127.0.0.1` | +| `db.port` | 达梦端口 | `5080` | +| `db.user` | 达梦用户 | `SYSDBA` | +| `db.table` | 实体表名 | `OBJECTIVE_INSTANCE` | +| `db.triplet_table` | 三元组表名 | `RELATION_INSTANCE` | + +### 达梦密码（切勿写入配置） + +达梦模式需设置环境变量： + +```bash +export DM_PASSWORD=你的密码 +``` + +或在 PyCharm：Run → Edit Configurations → Environment variables → 添加 `DM_PASSWORD=你的密码`。 + +### 可选配置 + +| 配置项 | 说明 | 默认 | +|--------|------|------| +| `json.input_file` | 实体 JSON 路径 | `data/raw_data_sample.json` | +| `json.output_file` | 清洗后实体输出 | `data/nodes.json` | +| `triplet.enabled` | 是否处理三元组 | `true` | +| `triplet.input_file` | JSON 模式下三元组路径 | `null` | +| `triplet.output_file` | 三元组输出 | `data/triples.json` | +| `triplet.clean.deduplicate` | 三元组去重 | `true` | +| `triplet.clean.filter_orphans` | 过滤悬空引用 | `false` | +| `eval.nodes_file` | main_227 读取的节点文件 | `data/nodes.json` | +| `eval.triples_file` | main_227 读取的三元组文件 | `data/triples.json`（与 triplet.output_file 一致） | + +### 配置优先级 + +`config.json` 优先于 `config.yaml`；修改其一即可。 + +--- + +## 数据流程 + +### main_227.py 流程 + +``` +1. 实体清洗 + - 从 JSON 或达梦读 raw_data_sample / OBJECTIVE_INSTANCE + - 去重、缺失值填充、异常值修正、噪声平滑 + - 输出 nodes.json +2. 关系与网络评估 + - 按配置读取 nodes（eval.nodes_file / json.output_file）、triples（eval.triples_file = triplet.output_file） + - 计算 5 类关系强度（情报保障、指挥控制、状态反馈、平台部署、协同作战） + - 按阈值过滤关系 + - 评估 5 类任务网络效能 + - 输出 results/ 下报告与表格 +``` + +### run_all.py 流程 + +``` +1. 实体清洗 → data/nodes.json +2. 三元组清洗 → data/triples.json（格式：head, head_type, relation, tail, tail_type） +``` + +--- + +## 输出文件说明 + +| 文件 | 来源 | 说明 | +|------|------|------| +| `data/nodes.json` | 清洗 | 清洗后实体（`{nodes: {id: obj}, global_params: {mtbf_max}}`，见下文） | +| `data/triples.json` | run_all | 清洗后三元组（head/head_type/relation/tail/tail_type） | +| `report/detailed_cleaning_report.json` | 清洗 | 实体清洗统计（含 attribute_alignment：格式/内容对齐情况） | +| `report/triplet_cleaning_report.json` | run_all | 三元组清洗统计 | +| `results/filtered_triples.json` | main_227 | 过滤后三元组（含强度） | +| `results/triples.csv` | main_227 | 三元组表格 | +| `results/evaluation_report.txt` | main_227 | 效能评价报告 | + +--- + +## 常见问题 + +**Q: 运行报错「找不到文件 raw_data_sample.json」？** +A: 将 `source` 改为 `json`，并确保 `data/raw_data_sample.json` 存在；或检查 `json.input_file` 路径。 + +**Q: 达梦模式连接失败？** +A: 检查 `DM_PASSWORD` 环境变量、`db.host`/`db.port`，以及 dmPython 与达梦客户端是否正确安装。 + +**Q: main_227 中 nodes.json、triples.json 从哪来？** +A: `nodes.json`、`triples.json` 均由清洗步骤生成（run_all 或 main_227 前半段），格式需包含 `head`、`relation`、`tail` 等字段。 + +**Q: 如何查看达梦表结构？** +A: 运行 `python scripts/inspect_dm_tables.py`（需设置 `DM_PASSWORD`）。 + +**Q: nodes.json 为何没有 X、Y、ROLE_ID 等原始字段？** +A: 输出完全对齐 1125 xlsx 规范，仅保留 schema 定义的 12 个字段；原始字段在转换阶段已映射为 `spatial`、`org_unit` 等规范结构。 + +--- + +## 数据清洗详细逻辑 + +### 清洗流程概览 + +``` +原始数据 → 去重 → 缺失值填充 → 异常值修正 → 噪声平滑 → 1125 Schema 转换 → 关系计算前净化 → nodes.json +``` + +| 步骤 | 说明 | +|------|------| +| **去重** | 按 `TARGET_ID` 去重，优先保留 `CREATED_TIME` 最新的记录 | +| **缺失值填充** | 数值型字段按 `ROLE_ID` 分组均值填充，兜底用全局均值 | +| **异常值修正** | 对 `TARGET_RECOGNITION_CAPABILITY`、`STRIKE_ACCURACY`、`ANTI_JAMMING_CAPABILITY`、`ENVIRONMENT_ADAPTABILITY`、`MOBILITY` 等 [0,1] 字段：负值取绝对值、>1 截断为 1 | +| **噪声平滑** | 对 `COMMUNICATION_RANGE` 做 3 点移动平均 | +| **1125 Schema 转换** | 见下文字段映射，输出仅含规范字段 | +| **关系计算前净化** | 见下文「关系计算前净化」 | + +### 关系计算前净化（sanitize_node_for_relation_calc） + +下游 `f_relation1125` 及 `utils.py` 会对节点属性做数值运算（如欧氏距离、安全等级差、历史成功率平均），若字段为 `None` 会触发 `NoneType` 运算错误。因此在写入 `nodes.json` 前，对每条节点做最终净化，确保参与运算的字段**永不为 `None`**： + +| 净化项 | 处理方式 | +|--------|----------| +| `spatial.position` | 必须为 `[float, float]`，含 `None`/`NaN` 的元素替换为 `0.0` | +| `spatial.effective_radius` | 若为 `None`，设为 `1.0` | +| `temporal.*` | `time_window` 缺省 `24.0`，其余缺省 `1.0` | +| `security_level` | 若为 `None` 或越界，设为 `3`（1–5 范围） | +| `history_success` | 若为 `None`，设为 `0.8`（0–1 范围） | + +该步骤在 `attribute_schema.sanitize_node_for_relation_calc()` 中实现，由 `cleaner` 在写入前对每个节点调用。 + +### 输出字段说明 + +**nodes.json 完全对齐 xlsx 规范**，不保留原始其他字段。每条记录仅包含： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `TARGET_ID` | 字符串 | 节点唯一标识（三元组 head/tail 引用所需） | +| `function_vector` | 字典 | 7 维功能能力 [0,1] | +| `spatial` | 字典 | 空间位置与有效半径 | +| `temporal` | 字典 | 时间相关属性 | +| `performance` | 字典 | 核心性能、生存能力、MTBF | +| `protocol_list` | 列表 | 支持协议（字符串列表） | +| `format_list` | 列表 | 支持格式（字符串列表） | +| `interface_list` | 列表 | 支持接口（字符串列表） | +| `security_level` | 整数 | 安全等级 [1,5]，缺省 3（关系计算用） | +| `org_unit` | 字符串/null | 组织隶属 | +| `nation` | 字符串/null | 国家代码 | +| `history_success` | 浮点数 | 历史成功率 [0,1]，缺省 0.8（关系计算用） | + +**nodes.json 整体结构**：`{ "nodes": { "TARGET_ID": {...}, ... }, "global_params": { "mtbf_max": number } }`，兼容 main_227 / f_relation1125 关系计算。 + +**单条节点记录示例：** + +```json +{ + "TARGET_ID": "FS-俄-情报侦-132", + "function_vector": { + "f_IC": 0.9273, "f_IA": 0.7896, "f_CS": 0.2584, "f_IT": 0.5187, + "f_DP": 0.9148, "f_CPS": 0.5619, "f_CC": 0.7822 + }, + "spatial": { "position": [79.36, 52.11], "effective_radius": 94.96 }, + "temporal": { "response_time": 23.3891, "cycle_time": 3.7, "data_age": 1.48, "time_window": 3.7 }, + "performance": { "core_performance": 0.9831, "survivability": 0.4094, "mtbf": 0.7202 }, + "protocol_list": [], "format_list": [], "interface_list": [], + "security_level": 3, "org_unit": "师级", "nation": "俄", "history_success": 0.8 +} +``` + +--- + +## 属性对齐与字段转换（1125 规范） + +依据 `1125暂时属性需求(1).xlsx`，对每条记录做 **内容与格式的双重对齐**： + +- **格式校验**：各字段类型与结构（dict/list/scalar） +- **内容校验**：取值范围（如 [0,1]、[1,5]） +- **转换**：原始扁平字段 → 规范 schema + +清洗报告中 `details.attribute_alignment` 记录格式/内容错误统计及示例。 + +### 原始字段 → 目标 Schema 映射 + +#### function_vector（功能向量） + +每个维度取 [0,1] 浮点数，从原始能力字段推导： + +| 目标维度 | 原始字段（按优先级） | 转换方式 | +|----------|----------------------|----------| +| f_IC 情报收集 | DETECTION_ACCURACY, TARGET_RECOGNITION_CAPABILITY | 取第一个有效值，clamp 到 [0,1] | +| f_IA 信息分析 | INFORMATION_FUSION_CAPABILITY, DETECTION_ACCURACY | 同上 | +| f_CS 协同作战 | MOBILITY, ENVIRONMENT_ADAPTABILITY | 同上 | +| f_IT 信息传输 | ANTI_JAMMING_CAPABILITY | 同上 | +| f_DP 数据处理 | PROCESSING_CAPACITY, INFORMATION_FUSION_CAPABILITY | **固定参考值**：见下表 | +| f_CPS 指挥控制 | DECISION_RESPONSE_TIME | **固定参考值**：见下表 | +| f_CC 综合保障 | SUPPORT_CAPACITY, LOAD_CAPACITY | **固定参考值**：见下表 | + +**固定参考值转换公式**（原始值不在 [0,1] 时使用）： + +| 维度 | 公式 | 参数说明 | +|------|------|----------| +| **f_CPS** | `1 / (1 + t/30)` | t = 响应时间（秒），响应越快能力越高 | +| **f_DP** | `log10(1+PC) / log10(10001)` | PC = PROCESSING_CAPACITY；无则用 INFORMATION_FUSION_CAPABILITY clamp [0,1] | +| **f_CC** | `0.6×support_norm + 0.4×load_norm` | support_norm = log10(1+SUPPORT)/log10(10001)，load_norm = LOAD/100；仅一个有时取该值 | + +#### spatial（空间属性） + +| 目标字段 | 原始字段（按优先级） | 转换 | +|----------|----------------------|------| +| position | `position`（列表 [x,y]）、`X`/`x`+`Y`/`y`、`POSITION_X`/`POSITION_Y`、`LONGITUDE`/`LATITUDE` | `[round(x,2), round(y,2)]`，`None`/`NaN` 替为 `0.0`，兼容达梦多种列名 | +| effective_radius | COMMUNICATION_RANGE | 原值保留 4 位小数，缺省 `1.0` | + +#### temporal（时间属性） + +| 目标字段 | 原始字段 | 转换 | +|----------|----------|------| +| response_time | DECISION_RESPONSE_TIME | 原值 | +| cycle_time | REFRESH_RATE | 原值 | +| data_age | TRANSMISSION_DELAY | 原值（近似数据新鲜度） | +| time_window | REFRESH_RATE | 原值 | + +#### performance（性能属性） + +| 目标字段 | 原始字段（按优先级） | 转换 | +|----------|----------------------|------| +| core_performance | STRIKE_ACCURACY, DETECTION_ACCURACY, MOBILITY | 取第一个有效值，clamp [0,1] | +| survivability | ENVIRONMENT_ADAPTABILITY, ANTI_JAMMING_CAPABILITY | 同上 | +| mtbf | RELIABILITY, SYSTEM_RELIABILITY | 取第一个有效值 | + +#### 其他标量字段 + +| 目标字段 | 原始字段 | 转换 | +|----------|----------|------| +| protocol_list | PROTOCOL | 字符串→列表，空则 `[]` | +| format_list | FORMAT | 同上 | +| interface_list | INTERFACE | 同上 | +| security_level | security_level, SECURITY_LEVEL | 整数 [1,5]，缺省 `3`（避免关系计算时 `None` 运算错误） | +| org_unit | LEVEL, ROLE_ID | 取第一个非空字符串 | +| nation | COUNTRY_REGION | 原值 | +| history_success | history_success, HISTORY_SUCCESS | clamp [0,1]，缺省 `0.8`（同上） | + +### 缺失与空值 + +- 无可映射原始值时，多数目标字段为 `null`（列表型为 `[]`） +- `security_level`、`history_success`、`spatial.position` 等参与关系运算的字段**永不为 `null`**：缺省分别为 `3`、`0.8`、`[0,0]`，并在「关系计算前净化」阶段再次保证 +- 可选字段（如 `org_unit`、`nation`）缺失不影响记录通过校验 diff --git a/final/config/config.json b/final/config/config.json new file mode 100644 index 0000000..aabd80f --- /dev/null +++ b/final/config/config.json @@ -0,0 +1,86 @@ +{ + "source": "json", + "json": { + "input_file": "data/raw_data_sample.json", + "output_file": "data/nodes.json", + "report_file": "report/detailed_cleaning_report.json" + }, + "triplet": { + "enabled": true, + "input_file": null, + "output_file": "data/triples.json", + "report_file": "report/triplet_cleaning_report.json", + "clean": { + "deduplicate": true, + "remove_null_core": true, + "normalize_relation_type": true, + "filter_orphans": false + } + }, + "db": { + "host": "127.0.0.1", + "port": 5080, + "user": "SYSDBA", + "schema": "SYSDBA", + "table": "OBJECTIVE_INSTANCE", + "query": null, + "triplet_table": "RELATION_INSTANCE", + "triplet_query": null + }, + "formal": { + "enabled": false, + "user_id": "ADMIN", + "task_id": "TASK_FULL_KB", + "source_sys_type": "关系推理后的体系", + "target_sys_type": "形式化后的体系", + "source_query": null, + "user_column": "USER_ID", + "task_column": "TASK_ID", + "sys_type_column": "SYS_TYPE", + "skip_sys_type_validation": false, + "entity_id_column": "TARGET_ID", + "objective_table": "OBJECTIVE_INSTANCE", + "triplet_table": "RELATION_INSTANCE", + "triplet_primary_key": "RELATION_INSTANCE_ID", + "triplet_omit_primary_key_on_insert": true, + "triple_batch_column": "BATCH_ID", + "attribute_table": "METRIC_VALUE_INSTANCE", + "metric_id_column": "METRIC_ID", + "metric_value_column": "METRIC_VALUE", + "metric_insert_static": null, + "formal_result_table": "RELATION_INSTANCE", + "analysis_table": "RES_FORMAL_ANALYSIS", + "analysis_insert_columns": null, + "analysis_static_columns": null, + "rules_summary": null, + "required_attrs": [ + "f_IC", + "f_IA", + "f_CS", + "f_IT", + "f_DP", + "f_CPS", + "f_CC", + "position", + "effective_radius", + "response_time", + "cycle_time", + "data_age", + "time_window", + "core_performance", + "survivability", + "mtbf", + "protocol_list", + "format_list", + "interface_list", + "security_level", + "org_unit", + "nation", + "history_success" + ] + }, + "eval": { + "nodes_file": "data/nodes.json", + "triples_file": "data/triples.json" + } +} \ No newline at end of file diff --git a/final/config/config.yaml b/final/config/config.yaml new file mode 100644 index 0000000..cc356e7 --- /dev/null +++ b/final/config/config.yaml @@ -0,0 +1,96 @@ +# 数据清洗系统配置文件 +# 通过 source 切换：json（开发） / db（生产-达梦） + +source: json # 开发用 json；部署达梦时改为 db（或直接改 config.json） + +# JSON 文件模式（source=json 时生效） +json: + input_file: data/raw_data_sample.json + output_file: data/nodes.json + report_file: report/detailed_cleaning_report.json + +# 网络关系/效能评估读取路径（main_227 在清洗后加载） +eval: + nodes_file: data/nodes.json # 与 json.output_file 一致（清洗输出） + triples_file: data/triples.json # 与 triplet.output_file 一致（run_all 输出） + +# 三元组（run_all 时一并处理；db 模式从库读，json 模式可配 input_file） +triplet: + enabled: true # false 可关闭三元组处理 + input_file: null # json 模式下可选，如 data/triplets_raw.json + output_file: data/triples.json # 与 eval.triples_file 一致，便于 main_227 直接读取 + report_file: report/triplet_cleaning_report.json + # 清洗配置：clean: false 关闭清洗；true 使用默认；或自定义子项 + clean: + deduplicate: true # 按 (HEAD_ID, RELATION_TYPE, TAIL_ID) 去重 + remove_null_core: true # 移除 HEAD_ID/TAIL_ID/RELATION_TYPE 为空的记录 + normalize_relation_type: true # 规范化 RELATION_TYPE（去空格） + filter_orphans: false # 过滤悬空引用（需加载实体表，稍慢） + +# 达梦数据库模式（source=db 时生效） +# 密码通过环境变量 DM_PASSWORD 传入，切勿写入配置文件 +db: + host: 127.0.0.1 + port: 5080 + user: SYSDBA + schema: SYSDBA + # OBJECTIVE_INSTANCE 表结构与 raw_data_sample.json 一致，直接查询即可 + table: OBJECTIVE_INSTANCE + query: null + # 三元组表（RELATION_INSTANCE: HEAD_ID, RELATION_TYPE, TAIL_ID） + triplet_table: RELATION_INSTANCE + triplet_query: null # 可选：自定义 SQL + +# 模块1.3 形式化入库流程（source=db 时生效） +formal: + enabled: false + user_id: ADMIN + task_id: TASK_FULL_KB + source_sys_type: 关系推理后的体系 + target_sys_type: 形式化后的体系 + # 推荐：显式 SQL 只读「关系推理后的体系」，列名与库一致时最稳（为 null 则用下方列名拼 WHERE） + source_query: null + user_column: USER_ID + task_column: TASK_ID + sys_type_column: SYS_TYPE + skip_sys_type_validation: false + entity_id_column: TARGET_ID + objective_table: OBJECTIVE_INSTANCE + triplet_table: RELATION_INSTANCE + triplet_primary_key: RELATION_INSTANCE_ID + triplet_omit_primary_key_on_insert: true + triple_batch_column: BATCH_ID + attribute_table: METRIC_VALUE_INSTANCE + metric_id_column: METRIC_ID + metric_value_column: METRIC_VALUE + metric_insert_static: null + formal_result_table: RELATION_INSTANCE + analysis_table: RES_FORMAL_ANALYSIS + # 若自动匹配列失败，在此列出 RES_FORMAL_ANALYSIS 的实际列名（顺序与 VALUES 一致） + analysis_insert_columns: null + analysis_static_columns: null + rules_summary: null + required_attrs: + - f_IC + - f_IA + - f_CS + - f_IT + - f_DP + - f_CPS + - f_CC + - position + - effective_radius + - response_time + - cycle_time + - data_age + - time_window + - core_performance + - survivability + - mtbf + - protocol_list + - format_list + - interface_list + - security_level + - org_unit + - nation + - history_success diff --git a/final/data/cleaned_data_final.json b/final/data/cleaned_data_final.json new file mode 100644 index 0000000..0f9ce44 --- /dev/null +++ b/final/data/cleaned_data_final.json @@ -0,0 +1,7002 @@ +[ + { + "ID": 132, + "TARGET_ID": "FS-俄-情报侦-132", + "TARGET_NAME": "侦察机师级", + "COUNTRY_REGION": "俄", + "LEVEL": "师级", + "ROLE_ID": "情报侦察", + "TASK_ID": "后勤支援任务", + "X": 79.36, + "Y": 52.11, + "COMMUNICATION_RANGE": 94.96, + "WEIGHT": 53.152702702702705, + "AREA": 169.51351351351352, + "MOBILITY": 0.25838704606599217, + "ENVIRONMENT_ADAPTABILITY": 0.40944779235306994, + "ANTI_JAMMING_CAPABILITY": 0.5187126139085138, + "DETECTION_RANGE": 430.99590881572885, + "DETECTION_ACCURACY": 0.9273183360354136, + "REFRESH_RATE": 3.7, + "TRANSMISSION_DELAY": 1.48, + "RELIABILITY": 0.7201663499950727, + "DECISION_RESPONSE_TIME": 23.389132166554926, + "PROCESSING_CAPACITY": 4561.451612903225, + "INFORMATION_FUSION_CAPABILITY": 0.789593701487015, + "SYSTEM_RELIABILITY": 0.7923055432252173, + "STRIKE_RANGE": 55.122584025030505, + "STRIKE_ACCURACY": 0.9830599905917308, + "AMMUNITION_RESERVE": 26.824324324324323, + "TARGET_RECOGNITION_CAPABILITY": 0.7732718347097838, + "LOAD_CAPACITY": 62.146341463414636, + "PLATFORM_AREA": 24187.45, + "SUPPORT_RANGE": 133.48048780487807, + "SUPPORT_CAPACITY": 3609.55737704918, + "CREATED_TIME": "2025-11-25 10:20:40.773806" + }, + { + "ID": 42, + "TARGET_ID": "FS-法-后勤保-042", + "TARGET_NAME": "医疗站旅级", + "COUNTRY_REGION": "法", + "LEVEL": "旅级", + "ROLE_ID": "后勤保障", + "TASK_ID": "侦察监视任务", + "X": 19.54, + "Y": 39.4, + "COMMUNICATION_RANGE": 60.23, + "WEIGHT": 53.152702702702705, + "AREA": 169.51351351351352, + "MOBILITY": 0.5336058782162458, + "ENVIRONMENT_ADAPTABILITY": 0.6611669374560885, + "ANTI_JAMMING_CAPABILITY": 0.43985141347577034, + "DETECTION_RANGE": 311.43915388058707, + "DETECTION_ACCURACY": 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"core_performance": 0.9831, + "survivability": 0.4866, + "mtbf": 1.0945 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "旅级", + "nation": "中", + "history_success": 0.8 + }, + "FS-美-平台部-085": { + "TARGET_ID": "FS-美-平台部-085", + "function_vector": { + "f_IC": 0.9222, + "f_IA": 0.7896, + "f_CS": 0.0, + "f_IT": 0.984, + "f_DP": 0.9148, + "f_CPS": 0.5619, + "f_CC": 0.8458 + }, + "spatial": { + "position": [ + 167.55, + 33.59 + ], + "effective_radius": 142.57 + }, + "temporal": { + "response_time": 23.3891, + "cycle_time": 5.15, + "data_age": 0.8832, + "time_window": 5.15 + }, + "performance": { + "core_performance": 0.9831, + "survivability": 0.6839, + "mtbf": 0.7841 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "旅级", + "nation": "美", + "history_success": 0.8 + }, + "FS-法-平台部-142": { + "TARGET_ID": "FS-法-平台部-142", + "function_vector": { + "f_IC": 0.9222, + "f_IA": 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0.7896, + "f_CS": 0.7417, + "f_IT": 0.7472, + "f_DP": 0.9148, + "f_CPS": 0.5619, + "f_CC": 0.7822 + }, + "spatial": { + "position": [ + 18.23, + 63.14 + ], + "effective_radius": 148.96 + }, + "temporal": { + "response_time": 23.3891, + "cycle_time": 5.15, + "data_age": 0.8832, + "time_window": 5.15 + }, + "performance": { + "core_performance": 0.5414, + "survivability": 0.6958, + "mtbf": 0.7841 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "军级", + "nation": "中", + "history_success": 0.8 + }, + "FS-中-协同打-026": { + "TARGET_ID": "FS-中-协同打-026", + "function_vector": { + "f_IC": 0.9222, + "f_IA": 0.7896, + "f_CS": 0.7853, + "f_IT": 0.4311, + "f_DP": 0.9148, + "f_CPS": 0.5619, + "f_CC": 0.7822 + }, + "spatial": { + "position": [ + 123.4, + -54.24 + ], + "effective_radius": 154.01 + }, + "temporal": { + "response_time": 23.3891, + "cycle_time": 5.15, + "data_age": 0.8832, + "time_window": 5.15 + }, + "performance": { + "core_performance": 0.6259, + "survivability": 0.6265, + "mtbf": 0.7841 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "营级", + "nation": "中", + "history_success": 0.8 + }, + "FS-中-指挥控-126": { + "TARGET_ID": "FS-中-指挥控-126", + "function_vector": { + "f_IC": 0.9222, + "f_IA": 1.0, + "f_CS": 0.1984, + "f_IT": 0.66, + "f_DP": 0.5233, + "f_CPS": 0.6608, + "f_CC": 0.7822 + }, + "spatial": { + "position": [ + 166.65, + -43.17 + ], + "effective_radius": 89.26 + }, + "temporal": { + "response_time": 15.4, + "cycle_time": 5.15, + "data_age": 0.44, + "time_window": 5.15 + }, + "performance": { + "core_performance": 0.9831, + "survivability": 0.681, + "mtbf": 0.7841 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "营级", + "nation": "中", + "history_success": 0.8 + }, + "FS-法-后勤保-118": { + "TARGET_ID": "FS-法-后勤保-118", + "function_vector": { + "f_IC": 0.9222, + "f_IA": 0.7896, + "f_CS": 0.5815, + "f_IT": 0.4777, + "f_DP": 0.9148, + "f_CPS": 0.5619, + "f_CC": 0.7902 + }, + "spatial": { + "position": [ + -94.3, + -30.68 + ], + "effective_radius": 89.52 + }, + "temporal": { + "response_time": 23.3891, + "cycle_time": 5.15, + "data_age": 0.8832, + "time_window": 5.15 + }, + "performance": { + "core_performance": 0.9831, + "survivability": 0.7855, + "mtbf": 0.7841 + }, + "protocol_list": [], + "format_list": [], + "interface_list": [], + "security_level": 3, + "org_unit": "营级", + "nation": "法", + "history_success": 0.8 + } + }, + "global_params": { + "mtbf_max": 1.0945 + } +} \ No newline at end of file diff --git a/data/raw_data_sample.json b/final/data/raw_data_sample.json similarity index 100% rename from data/raw_data_sample.json rename to final/data/raw_data_sample.json diff --git a/final/data/triples.json b/final/data/triples.json new file mode 100644 index 0000000..937a208 --- /dev/null +++ b/final/data/triples.json @@ -0,0 +1,60 @@ +{ + "triples": [ + { + "head": "CMD001", + "head_type": "指挥控制", + "relation": "指挥控制", + "tail": "DEF001", + "tail_type": "信息获取" + }, + { + "head": "CMD001", + "head_type": "指挥控制", + "relation": "指挥控制", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "DEF001", + "head_type": "信息获取", + "relation": "状态反馈", + "tail": "CMD001", + "tail_type": "指挥控制" + }, + { + "head": "DEF001", + "head_type": "信息获取", + "relation": "情报保障", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "STK001", + "head_type": "协同打击", + "relation": "状态反馈", + "tail": "CMD001", + "tail_type": "指挥控制" + }, + { + "head": "STK001", + "head_type": "协同打击", + "relation": "协同作战", + "tail": "STK002", + "tail_type": "协同打击" + }, + { + "head": "LOG001", + "head_type": "综合保障", + "relation": "平台部署", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "MED001", + "head_type": "综合保障", + "relation": "平台部署", + "tail": "TRN001", + "tail_type": "部署平台" + } + ] +} \ No newline at end of file diff --git a/final/data/triplets.json b/final/data/triplets.json new file mode 100644 index 0000000..5508f4b --- /dev/null +++ b/final/data/triplets.json @@ -0,0 +1,58 @@ +[ + { + "head": "CMD001", + "head_type": "指挥控制", + "relation": "指挥控制", + "tail": "DEF001", + "tail_type": "信息获取" + }, + { + "head": "CMD001", + "head_type": "指挥控制", + "relation": "指挥控制", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "DEF001", + "head_type": "信息获取", + "relation": "状态反馈", + "tail": "CMD001", + "tail_type": "指挥控制" + }, + { + "head": "DEF001", + "head_type": "信息获取", + "relation": "情报保障", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "STK001", + "head_type": "协同打击", + "relation": "状态反馈", + "tail": "CMD001", + "tail_type": "指挥控制" + }, + { + "head": "STK001", + "head_type": "协同打击", + "relation": "协同作战", + "tail": "STK002", + "tail_type": "协同打击" + }, + { + "head": "LOG001", + "head_type": "综合保障", + "relation": "平台部署", + "tail": "STK001", + "tail_type": "协同打击" + }, + { + "head": "MED001", + "head_type": "综合保障", + "relation": "平台部署", + "tail": "TRN001", + "tail_type": "部署平台" + } +] \ No newline at end of file diff --git a/final/report/detailed_cleaning_report.json b/final/report/detailed_cleaning_report.json new file mode 100644 index 0000000..19d2bd1 --- /dev/null +++ b/final/report/detailed_cleaning_report.json @@ -0,0 +1,94 @@ +{ + "timestamp": "2026-04-23 12:20:15", + "summary": { + "total_records": 210, + "final_records": 200, + "duplicates_removed": 10 + }, + "details": { + "missing_values_fixed": { + "X": 2, + "Y": 5, + "WEIGHT": 126, + "AREA": 126, + "DETECTION_RANGE": 166, + "DETECTION_ACCURACY": 166, + "REFRESH_RATE": 166, + "TRANSMISSION_DELAY": 135, + "RELIABILITY": 166, + "DECISION_RESPONSE_TIME": 169, + "PROCESSING_CAPACITY": 169, + "INFORMATION_FUSION_CAPABILITY": 169, + "SYSTEM_RELIABILITY": 169, + "STRIKE_RANGE": 126, + "STRIKE_ACCURACY": 126, + "AMMUNITION_RESERVE": 126, + "TARGET_RECOGNITION_CAPABILITY": 126, + "LOAD_CAPACITY": 159, + "PLATFORM_AREA": 180, + "SUPPORT_RANGE": 159, + "SUPPORT_CAPACITY": 139 + }, + "outliers_corrected": { + "count": 59, + "examples": [ + { + "id": "FS-法-协同打-144", + "field": "TARGET_RECOGNITION_CAPABILITY", + "original": 1.1645873020580773, + "corrected": 1.0, + "reason": "Value out of range [0, 1]" + }, + { + "id": "FS-法-协同打-144", + "field": "STRIKE_ACCURACY", + "original": 1.2863907411837974, + "corrected": 1.0, + "reason": "Value out of range [0, 1]" + }, + { + "id": "FS-美-火力打-156", + "field": "TARGET_RECOGNITION_CAPABILITY", + "original": -0.8869593801185574, + "corrected": 0.8869593801185574, + "reason": "Value out of range [0, 1]" + }, + { + "id": "FS-俄-协同打-061", + "field": "TARGET_RECOGNITION_CAPABILITY", + "original": 1.1931823083468704, + "corrected": 1.0, + "reason": "Value out of range [0, 1]" + }, + { + "id": "FS-美-协同打-121", + "field": "STRIKE_ACCURACY", + "original": 1.439367437550393, + "corrected": 1.0, + "reason": "Value out of range [0, 1]" + } + ] + }, + "noise_reduction": { + "method": "Kalman Filter", + "fields_processed": [ + "COMMUNICATION_RANGE" + ], + "total_smoothed": 200 + }, + "standardization": [ + "Coordinates normalized to 2 decimal places", + "Timestamps formatted to ISO-8601", + "Attributes aligned to 1125 schema (format + content)" + ], + "attribute_alignment": { + "enabled": true, + "format_errors_by_field": {}, + "content_errors_by_field": {}, + "records_with_errors": 0, + "aligned_count": 200, + "transformed_count": 200, + "samples": [] + } + } +} \ No newline at end of file diff --git a/final/report/triplet_cleaning_report.json b/final/report/triplet_cleaning_report.json new file mode 100644 index 0000000..a161065 --- /dev/null +++ b/final/report/triplet_cleaning_report.json @@ -0,0 +1,20 @@ +{ + "timestamp": "2026-02-27 19:45:51", + "original_count": 8, + "deduplicate": { + "enabled": true, + "removed": 0 + }, + "filter_orphans": { + "enabled": false, + "removed": 0 + }, + "remove_null_core": { + "enabled": true, + "removed": 0 + }, + "normalize_relation_type": { + "enabled": true + }, + "final_count": 8 +} \ No newline at end of file diff --git a/final/requirements.txt b/final/requirements.txt new file mode 100644 index 0000000..d30ea45 --- /dev/null +++ b/final/requirements.txt @@ -0,0 +1,5 @@ +numpy>=1.20.0 +pandas>=1.3.0 +PyYAML>=5.4 +openpyxl>=3.0.0 +# 达梦模式需额外：pip install dmPython diff --git a/final/results/evaluation_report.txt b/final/results/evaluation_report.txt new file mode 100644 index 0000000..a0508f3 --- /dev/null +++ b/final/results/evaluation_report.txt @@ -0,0 +1,83 @@ +============================================================ +网络关系与效能评价报告 +============================================================ + +1. 基本信息 +---------------------------------------- +过滤阈值: 0.15 【关系强度阈值】 +总三元组数量: 8 +分别有： +有效计算三元组: 0 +跳过三元组: 8 +通过过滤三元组: 0 +通过率: 0.0% + +2. 计算公式 +边关系计算公式 +-------------------------------------------------- +情报保障关系 L_IS(i,j) = w_f·M_F^IS + w_s·M_S^IS + w_t·M_T^IS + w_p·M_P^IS + w_i·M_I^IS +指挥控制关系 L_CC(i,j) = w_f·M_F^CC + w_s·M_S^CC + w_t·M_T^CC + w_p·M_P^CC + w_i·M_I^CC +状态反馈关系 L_SF(i,j) = w_f·M_F^SF + w_s·M_S^SF + w_t·M_T^SF + w_p·M_P^SF + w_i·M_I^SF +平台部署关系 L_PD(i,j) = w_f·M_F^PD + w_s·M_S^PD + w_t·M_T^PD + w_p·M_P^PD + w_i·M_I^PD +协同作战关系 L_CO(i,j) = w_f·M_F^CO + w_s·M_S^CO + w_t·M_T^CO + w_p·M_P^CO + w_i·M_I^CO +【符号说明】 + M_F^IS 情报保障功能匹配项 + M_S^IS 情报保障空间匹配项 + M_T^IS 情报保障时间匹配项 + M_P^IS 情报保障性能匹配项 + M_I^IS 情报保障交互匹配项 + +任务网络效能计算公式 +-------------------------------------------------- +综合防御 P_defense = α1·(∑f_IC/N_IC) + α2·(∑f_IA/N_IA) + α3·(∑L_IS/N_IS) + α4·(∑t_resp/N) + α5·(∑L_CC/N_CC) +火力打击 P_fire = β1·(∑f_CS/N_CS) + β2·(∑f_IA/N_IA) + β3·(∑L_CO/N_CO) + β4·(∑p_core·f_CS/N_CS) + β5·(∑L_IS/N_IS) +后勤保障 P_logistics = γ1·(∑f_CPS/N_CPS) + γ2·(∑f_DP/N_DP) + γ3·(∑L_PD/N_PD) + γ4·(∑p_rel/N) + γ5·(∑f_IT/N_IT) +医疗救援 P_medical = δ1·(∑f_CPS/N_CPS) + δ2·(∑t_resp/N) + δ3·(∑f_IT/N_IT) + δ4·(∑L_SF/N_SF) + δ5·(∑fresh_i/N) +紧急疏散 P_evacuation = ε1·(∑f_CC/N_CC) + ε2·(∑f_IT/N_IT) + ε3·(∑L_CC/N_CC) + ε4·(∑f_CPS/N_CPS) + ε5·(∑t_resp/N) +【符号说明】 + N_IC 信息对抗节点数量 + ∑f_IC/N_IC 信息对抗节点平均能力 + ∑L_IS/N_IS 情报保障关系平均强度 + ∑t_resp/N 系统平均响应速度 + ∑L_CC/N_CC 指挥控制关系平均强度 + ∑f_CS/N_CS 协同打击节点平均能力 + ∑f_IA/N_IA 信息获取节点平均能力 + ∑L_CO/N_CO 协同作战关系平均强度 + ∑p_core·f_CS/N_CS 打击节点性能加权能力 + ∑f_CPS/N_CPS 综合保障节点平均能力 + ∑f_DP/N_DP 部署平台节点平均能力 + ∑L_PD/N_PD 平台部署关系平均强度 + ∑p_rel/N 系统平均可靠性 + ∑f_IT/N_IT 信息传输节点平均能力 + ∑L_SF/N_SF 状态反馈关系平均强度 + ∑fresh_i/N 系统平均数据新鲜度 + α1...α5 综合防御权重 [0.30,0.25,0.20,0.15,0.10] + β1...β5 火力打击权重 [0.35,0.25,0.20,0.10,0.10] + γ1...γ5 后勤保障权重 [0.30,0.25,0.20,0.15,0.10] + δ1...δ5 医疗救援权重 [0.35,0.25,0.20,0.10,0.10] + ε1...ε5 紧急疏散权重 [0.30,0.25,0.20,0.15,0.10] + +3. 关系类型统计 +---------------------------------------- +【被过滤掉的关系明细】 +---------------------------------------- +4. 所属任务网络评估 +---------------------------------------- +任务网络概率：综合防御 21.2% 火力打击 17.5% 后勤保障 20.2% 医疗救援 19.7% 紧急疏散 21.4% +最有可能的任务网络：紧急疏散 可能性为：21.4 % +5. 最终紧急疏散任务网络的五个维度评价 +功能维度 (Function)： +功能维度反映了网络中节点的功能匹配程度。得分: 0.917 +高于参考值，表示节点在功能上高度互补，能够有效协同工作。 +空间系维度 (Relationship)： +空间维度反映了节点之间的连接强度。得分: 0.000 +低于参考值，表示节点之间的连接较弱，信息传递效率较低。 +性能维度 (Performance)： +性能维度反映了节点的性能指标，如响应时间和可靠性。得分: 0.935 +高于参考值，表示节点在性能上表现出色，能够稳定运行。 +时间维度 (Temporal)： +时间维度反映了节点的时间同步性和响应能力。得分: 0.126 +低于参考值，表示节点在时间同步性上存在偏差，响应速度较慢。 +交互维度 (Interaction)： +交互维度反映了节点之间的协议、格式和安全兼容性。得分: 0.000 +低于参考值，表示节点在交互上存在兼容性问题，通信安全性较低。 \ No newline at end of file diff --git a/final/results/filtered_triples.json b/final/results/filtered_triples.json new file mode 100644 index 0000000..0d42048 --- /dev/null +++ b/final/results/filtered_triples.json @@ -0,0 +1,3 @@ +{ + "triples": [] +} \ No newline at end of file diff --git a/final/results/triples.csv b/final/results/triples.csv new file mode 100644 index 0000000..e02abfc --- /dev/null +++ b/final/results/triples.csv @@ -0,0 +1 @@ + diff --git a/final/results/triples.xlsx b/final/results/triples.xlsx new file mode 100644 index 0000000..f843a04 Binary files /dev/null and b/final/results/triples.xlsx differ diff --git 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0000000..f1838d4 Binary files /dev/null and b/final/scripts/__pycache__/utils.cpython-313.pyc differ diff --git a/final/scripts/__pycache__/utils.cpython-38.pyc b/final/scripts/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000..6247acf Binary files /dev/null and b/final/scripts/__pycache__/utils.cpython-38.pyc differ diff --git a/final/scripts/export_triplets.py b/final/scripts/export_triplets.py new file mode 100644 index 0000000..9133da9 --- /dev/null +++ b/final/scripts/export_triplets.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +""" +从达梦数据库导出三元组数据到 JSON 文件 +三元组来源：RELATION_INSTANCE 表 (HEAD_ID, RELATION_TYPE, TAIL_ID) +支持可选清洗：去重、过滤悬空引用、移除空值、规范化关系类型 +用法: python scripts/export_triplets.py +需设置环境变量 DM_PASSWORD，且 config 中 source=db +""" +import json +import os +import sys + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_project_root = os.path.dirname(_script_dir) +sys.path.insert(0, _project_root) + + +def _json_serializer(obj): + """处理 Timestamp、datetime、numpy 等不可直接 JSON 序列化的类型""" + if hasattr(obj, 'isoformat'): + return obj.isoformat() + if hasattr(obj, 'item'): + return obj.item() + if isinstance(obj, float) and (obj != obj or obj == float('inf') or obj == float('-inf')): + return None + raise TypeError(f'Object of type {type(obj).__name__} is not JSON serializable') + + +def load_config(project_root: str) -> dict: + """加载 config""" + config_dir = os.path.join(project_root, 'config') + json_path = os.path.join(config_dir, 'config.json') + yaml_path = os.path.join(config_dir, 'config.yaml') + if os.path.exists(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + return json.load(f) + if os.path.exists(yaml_path): + try: + import yaml + with open(yaml_path, 'r', encoding='utf-8') as f: + return yaml.safe_load(f) + except ImportError: + pass + return {'source': 'json', 'db': {}, 'triplet': {}} + + +def main(): + from src.data_loader import load_triplets_from_dm, load_from_dm + from src.triplet_cleaner import clean_triplets, transform_triplet_output_format + + config = load_config(_project_root) + if config.get('source') != 'db': + print("错误：三元组导出仅支持达梦模式，请将 config 中 source 改为 db") + sys.exit(1) + + tc = config.get('triplet', {}) + output_file = tc.get('output_file', 'data/triples.json') + report_file = tc.get('report_file', 'report/triplet_cleaning_report.json') + output_path = output_file if os.path.isabs(output_file) else os.path.join(_project_root, output_file) + report_path = report_file if os.path.isabs(report_file) else os.path.join(_project_root, report_file) + os.makedirs(os.path.dirname(output_path), exist_ok=True) + os.makedirs(os.path.dirname(report_path), exist_ok=True) + + # 清洗配置（clean: false 则跳过；否则按子项配置） + clean_cfg = tc.get('clean', True) + do_clean = False + if clean_cfg is True: + do_clean = {'deduplicate': True, 'filter_orphans': False, 'remove_null_core': True, 'normalize_relation_type': True} + elif isinstance(clean_cfg, dict): + do_clean = clean_cfg + + deduplicate = do_clean.get('deduplicate', True) if do_clean else False + filter_orphans = do_clean.get('filter_orphans', False) if do_clean else False + remove_null_core = do_clean.get('remove_null_core', True) if do_clean else False + normalize_relation_type = do_clean.get('normalize_relation_type', True) if do_clean else False + + print("正在从达梦读取三元组 (RELATION_INSTANCE)...") + triplets = load_triplets_from_dm(config, _project_root) + print(f"读取到 {len(triplets)} 条三元组") + + valid_ids = None + if filter_orphans: + print("正在加载实体表以校验悬空引用...") + entities = load_from_dm(config, _project_root) + valid_ids = {str(e.get('TARGET_ID', '')).strip() for e in entities if e.get('TARGET_ID')} + print(f"有效实体数: {len(valid_ids)}") + + if do_clean and (deduplicate or filter_orphans or remove_null_core or normalize_relation_type): + print("正在执行三元组清洗...") + triplets, report = clean_triplets( + triplets, + valid_ids=valid_ids, + deduplicate=deduplicate, + filter_orphans=filter_orphans, + remove_null_core=remove_null_core, + normalize_relation_type=normalize_relation_type, + ) + with open(report_path, 'w', encoding='utf-8') as f: + json.dump(report, f, ensure_ascii=False, indent=2, default=_json_serializer) + print(f"清洗完成：{report['original_count']} -> {report['final_count']}，报告: {report_path}") + + # 转为输出格式：head, head_type, relation, tail, tail_type + entities = load_from_dm(config, _project_root) + id_to_role = {str(e.get('TARGET_ID', '')).strip(): str(e.get('ROLE_ID', '')).strip() for e in entities if e.get('TARGET_ID')} + triplets = transform_triplet_output_format(triplets, id_to_role) + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(triplets, f, ensure_ascii=False, indent=2, default=_json_serializer) + print(f"已导出至 {output_path}") + + +if __name__ == "__main__": + main() diff --git a/final/scripts/f_net1125.py b/final/scripts/f_net1125.py new file mode 100644 index 0000000..87aaadb --- /dev/null +++ b/final/scripts/f_net1125.py @@ -0,0 +1,112 @@ +# f_net1125.py ← 边关系已算完，只算任务网络 +from __future__ import annotations +import numpy as np +from typing import Dict, List, Tuple + +NodeID = str +TaskScore = float # 网络效能得分 [0,1] + + +class TaskNetworkEvaluator: + """ + 输入： + nodes - 节点数据（含 function_vector / performance / temporal） + relations - 已算好的全部边关系 + 格式：relations[("IS","DEF001","CMD001")] = 0.87 + """ + def __init__(self, + nodes: Dict[NodeID, Dict], + relations: Dict[Tuple[str, NodeID, NodeID], float], + weights: Dict[str, Dict[str, float]] = None): + self.nodes = nodes + self.rels = relations + self.w = weights + + # ---------- 工具 ---------- + def _avg(self, scores: List[float]) -> float: + return float(np.mean(scores)) if scores else 0.0 + + def _collect_func(self, key: str) -> List[float]: + """所有节点指定功能值""" + return [n["function_vector"][key] for n in self.nodes.values()] + + def _collect_rel(self, tag: str) -> List[float]: + """预存关系强度列表""" + return [v for k, v in self.rels.items() if k[0] == tag] + + def _collect_resp(self) -> List[float]: + """响应时间得分""" + return [np.exp(-0.1 * n["temporal"]["response_time"]) for n in self.nodes.values()] + + # ---------- 2.1 综合防御 ---------- + def eval_defense(self) -> TaskScore: + w = self.w["defense"] + f_ic = self._avg(self._collect_func("f_IC")) + f_ia = self._avg(self._collect_func("f_IA")) + l_is = self._avg(self._collect_rel("IS")) + t_resp = self._avg(self._collect_resp()) + l_cc = self._avg(self._collect_rel("CC")) + return w["w1"] * f_ic + w["w2"] * f_ia + w["w3"] * l_is + w["w4"] * t_resp + w["w5"] * l_cc + + # ---------- 2.2 火力打击 ---------- + def eval_fire(self) -> TaskScore: + w = self.w["fire"] + f_cs = self._avg(self._collect_func("f_CS")) + f_ia = self._avg(self._collect_func("f_IA")) + l_co = self._avg(self._collect_rel("CO")) + perf_cs = self._avg([n["performance"]["core_performance"] * n["function_vector"]["f_CS"] + for n in self.nodes.values()]) + l_is = self._avg(self._collect_rel("IS")) + return w["w1"] * f_cs + w["w2"] * f_ia + w["w3"] * l_co + w["w4"] * perf_cs + w["w5"] * l_is + + # ---------- 2.3 后勤保障 ---------- + def eval_logistics(self) -> TaskScore: + w = self.w["logistics"] + f_cps = self._avg(self._collect_func("f_CPS")) + f_dp = self._avg(self._collect_func("f_DP")) + l_pd = self._avg(self._collect_rel("PD")) + p_rel = self._avg([n["performance"]["mtbf"] / 1000 for n in self.nodes.values()]) + f_it = self._avg(self._collect_func("f_IT")) + return w["w1"] * f_cps + w["w2"] * f_dp + w["w3"] * l_pd + w["w4"] * p_rel + w["w5"] * f_it + + # ---------- 2.4 医疗救援 ---------- + def eval_medical(self) -> TaskScore: + w = self.w["medical"] + f_cps = self._avg(self._collect_func("f_CPS")) + t_resp = self._avg(self._collect_resp()) + f_it = self._avg(self._collect_func("f_IT")) + l_sf = self._avg(self._collect_rel("SF")) + fresh = self._avg([np.exp(-0.1 * n["temporal"]["data_age"]) for n in self.nodes.values()]) + return w["w1"] * f_cps + w["w2"] * t_resp + w["w3"] * f_it + w["w4"] * l_sf + w["w5"] * fresh + + # ---------- 2.5 紧急疏散 ---------- + def eval_evacuation(self) -> TaskScore: + w = self.w["evacuation"] + f_cc = self._avg(self._collect_func("f_CC")) + f_it = self._avg(self._collect_func("f_IT")) + l_cc = self._avg(self._collect_rel("CC")) + f_cps = self._avg(self._collect_func("f_CPS")) + t_resp = self._avg(self._collect_resp()) + return w["w1"] * f_cc + w["w2"] * f_it + w["w3"] * l_cc + w["w4"] * f_cps + w["w5"] * t_resp + + # ---------- 一键评估 ---------- + def eval_all(self) -> Dict[str, TaskScore]: + results = { + "defense": self.eval_defense(), + "fire": self.eval_fire(), + "logistics": self.eval_logistics(), + "medical": self.eval_medical(), + "evacuation": self.eval_evacuation(), + } + + # 计算五个维度的分数 + dimensions = { + "function": self._avg(self._collect_func("f_IC")), + "relationship": self._avg(self._collect_rel("IS")), + "performance": self._avg([n["performance"]["core_performance"] for n in self.nodes.values()]), + "temporal": self._avg(self._collect_resp()), + "interaction": self._avg(self._collect_rel("SF")) + } + + results["dimensions"] = dimensions + return results \ No newline at end of file diff --git a/final/scripts/f_relation1125.py b/final/scripts/f_relation1125.py new file mode 100644 index 0000000..5d9fbc1 --- /dev/null +++ b/final/scripts/f_relation1125.py @@ -0,0 +1,395 @@ +import numpy as np +from typing import Dict +from .utils import NetworkUtils + + +class RelationCalculator: + """关系强度计算器""" + + def __init__(self, nodes_data: Dict, cfg): + """ + 初始化 + :param nodes_data: 节点数据 + :param auxiliary_data: 辅助数据（接口标准、角色匹配等） + """ + self.nodes_data = nodes_data + self.utils = NetworkUtils() + self.cfg = cfg + + def _weights(self, rel: str): + return self.cfg["W_REL"][rel] + + def _lambda(self, rel: str): + return self.cfg["LAMBDA"][rel] + + def calculate_IS_relation(self, i: str, j: str) -> float: + """ + 计算情报保障关系强度 + L_IS(i,j) = w_f·M_IS_F + w_s·M_IS_S + w_t·M_IS_T + w_p·M_IS_P + w_i·M_IS_I + """ + # 权重参数（根据1.3.pdf） + w = self._weights("IS") + # 提取节点数据 + node_i = self.nodes_data['nodes'][i] + node_j = self.nodes_data['nodes'][j] + + # 1. 功能维度 M_IS_F + f_i_IA = node_i['function_vector']['f_IA'] + f_j_avg = ( + node_j['function_vector']['f_CC'] + + node_j['function_vector']['f_IC'] + + node_j['function_vector']['f_CS'] + + node_j['function_vector']['f_CPS'] + ) / 4 + + std_ij = self.utils.get_interface_standard(node_i, node_j) + r_ij = self.utils.get_role_match(node_i, node_j) + M_IS_F = f_i_IA * f_j_avg * std_ij * r_ij + + # 2. 空间维度 M_IS_S + d_ij = self.utils.calculate_distance( + node_i['spatial']['position'], + node_j['spatial']['position'] + ) + lambda_IS = self._lambda("IS") + R_match = self.utils.calculate_range_match( + node_i['spatial']['effective_radius'], + node_j['spatial']['effective_radius'], + d_ij + ) + + s_area = self.utils.get_area_relation(node_i, node_j) + M_IS_S = np.exp(-d_ij / lambda_IS) * R_match * s_area + + # 3. 时间维度 + alpha = self.cfg["TIME"]["alpha"] + beta = self.cfg["TIME"]["beta"] + gamma = self.cfg["TIME"]["gamma"] + + fresh_i = np.exp(-gamma * node_i['temporal']['data_age']) + t_i_resp = np.exp(-alpha * node_i['temporal']['response_time']) + + w_ij = self.utils.calculate_time_window_overlap(node_i, node_j) + + M_IS_T = fresh_i * t_i_resp * w_ij + + # 4. 性能维度 M_IS_P + p_i_core = node_i['performance']['core_performance'] + mtbf_max = self.nodes_data['global_params']['mtbf_max'] + p_i_rel = node_i['performance']['mtbf'] / mtbf_max + M_IS_P = p_i_core * p_i_rel + + # 5. 交互维度 M_IS_I + prot_ij = self.utils.get_protocol_compatibility(node_i, node_j) + fmt_ij = self.utils.get_format_compatibility(node_i, node_j) + sec_ij = self.utils.get_security_compatibility(node_i, node_j) + hist_ij = self.utils.get_interaction_history(node_i, node_j) + M_IS_I = ((prot_ij + fmt_ij + sec_ij) / 3) * hist_ij + + # 综合计算 + L_IS = (w["w_f"] * M_IS_F + w["w_s"] * M_IS_S + w["w_t"] * M_IS_T + + w["w_p"] * M_IS_P + w["w_i"] * M_IS_I) + + return L_IS + + def calculate_CC_relation(self, i: str, j: str) -> float: + """ + 计算指挥控制关系强度 + L_CC(i,j) = w_f·M_CC_F + w_s·M_CC_S + w_t·M_CC_T + w_p·M_CC_P + w_i·M_CC_I + """ + # 权重参数 + w = self._weights("CC") + lambda_CC = self._lambda("CC")# 根据公式，指挥控制距离敏感性更高 + + node_i = self.nodes_data['nodes'][i] + node_j = self.nodes_data['nodes'][j] + + # 1. 功能维度 M_CC_F + f_i_CC = node_i['function_vector']['f_CC'] + f_j_sum = sum([ + node_j['function_vector']['f_IA'], + node_j['function_vector']['f_IT'], + node_j['function_vector']['f_IC'], + node_j['function_vector']['f_CS'], + node_j['function_vector']['f_DP'], + node_j['function_vector']['f_CPS'] + ]) + f_j_avg = f_j_sum / 6 + + std_ij = self.utils.get_interface_standard(node_i, node_j) + r_ij = self.utils.get_role_match(node_i, node_j) + M_CC_F = f_i_CC * f_j_avg * std_ij * r_ij + + # 2. 空间维度 M_CC_S + d_ij = self.utils.calculate_distance( + node_i['spatial']['position'], + node_j['spatial']['position'] + ) + + R_match = self.utils.calculate_range_match( + node_i['spatial']['effective_radius'], + node_j['spatial']['effective_radius'], + d_ij + ) + + s_area = self.utils.get_area_relation(node_i, node_j) + M_CC_S = np.exp(-d_ij / lambda_CC) * R_match * s_area + + # 3. 时间维度 M_CC_T + alpha = self.cfg["TIME"]["alpha"] + beta = self.cfg["TIME"]["beta"] + gamma = self.cfg["TIME"]["gamma"] + + t_i_resp = np.exp(-alpha * node_i['temporal']['response_time']) + t_i_cycle = np.exp(-beta * node_i['temporal']['cycle_time']) + + w_ij = self.utils.calculate_time_window_overlap(node_i, node_j) + + M_CC_T = t_i_resp * t_i_cycle * w_ij + + # 4. 性能维度 M_CC_P + p_i_core = node_i['performance']['core_performance'] + p_i_surv = node_i['performance']['survivability'] + mtbf_max = self.nodes_data['global_params']['mtbf_max'] + p_i_rel = node_i['performance']['mtbf'] / mtbf_max + M_CC_P = p_i_core * p_i_surv * p_i_rel + + # 5. 交互维度 M_CC_I + prot_ij = self.utils.get_protocol_compatibility(node_i, node_j) + sec_ij = self.utils.get_security_compatibility(node_i, node_j) + org_ij = self.utils.get_organization_relation(node_i, node_j) + hist_ij = self.utils.get_interaction_history(node_i, node_j) + M_CC_I = ((prot_ij + sec_ij + org_ij) / 3) * hist_ij + + # 综合计算 + L_CC = (w["w_f"] * M_CC_F + w["w_s"] * M_CC_S + w["w_t"] * M_CC_T + + w["w_p"] * M_CC_P + w["w_i"] * M_CC_I) + + return L_CC + + def calculate_SF_relation(self, i: str, j: str) -> float: + """ + 计算状态反馈关系强度 + L_SF(i,j) = w_f·M_SF_F + w_s·M_SF_S + w_t·M_SF_T + w_p·M_SF_P + w_i·M_SF_I + """ + # 权重参数 + w = self._weights("SF") + lambda_SF = self._lambda("SF") + + node_i = self.nodes_data['nodes'][i] + node_j = self.nodes_data['nodes'][j] + + # 1. 功能维度 M_SF_F + f_i_sum = sum([ + node_i['function_vector']['f_IA'], + node_i['function_vector']['f_IT'], + node_i['function_vector']['f_IC'], + node_i['function_vector']['f_CS'], + node_i['function_vector']['f_DP'], + node_i['function_vector']['f_CPS'] + ]) + f_i_avg = f_i_sum / 6 + f_j_CC = node_j['function_vector']['f_CC'] + + std_ij = self.utils.get_interface_standard(node_i, node_j) + r_ij = self.utils.get_role_match(node_i, node_j) + M_SF_F = f_i_avg * f_j_CC * std_ij * r_ij + + # 2. 空间维度 M_SF_S + d_ij = self.utils.calculate_distance( + node_i['spatial']['position'], + node_j['spatial']['position'] + ) + + R_match = self.utils.calculate_range_match( + node_i['spatial']['effective_radius'], + node_j['spatial']['effective_radius'], + d_ij + ) + + s_area = self.utils.get_area_relation(node_i, node_j) + M_SF_S = np.exp(-d_ij / lambda_SF) * R_match * s_area + + # 3. 时间维度 M_SF_T（状态反馈对时间敏感） + alpha = self.cfg["TIME"]["alpha"] + beta = self.cfg["TIME"]["beta"] + gamma = self.cfg["TIME"]["gamma"] + + fresh_i = np.exp(-gamma * node_i['temporal']['data_age']) + t_i_resp = np.exp(-alpha * node_i['temporal']['response_time']) + + w_ij = self.utils.calculate_time_window_overlap(node_i, node_j) + + M_SF_T = fresh_i * t_i_resp * w_ij + + # 4. 性能维度 M_SF_P + p_i_core = node_i['performance']['core_performance'] + mtbf_max = self.nodes_data['global_params']['mtbf_max'] + p_i_rel = node_i['performance']['mtbf'] / mtbf_max + M_SF_P = p_i_core * p_i_rel + + # 5. 交互维度 M_SF_I + prot_ij = self.utils.get_protocol_compatibility(node_i, node_j) + fmt_ij = self.utils.get_format_compatibility(node_i, node_j) + sec_ij = self.utils.get_security_compatibility(node_i, node_j) + hist_ij = self.utils.get_interaction_history(node_i, node_j) + M_SF_I = ((prot_ij + fmt_ij + sec_ij) / 3) * hist_ij + + # 综合计算 + L_SF = (w["w_f"] * M_SF_F + w["w_s"] * M_SF_S + w["w_t"] * M_SF_T + + w["w_p"] * M_SF_P + w["w_i"] * M_SF_I) + + return L_SF + + def calculate_PD_relation(self, i: str, j: str) -> float: + """ + 计算平台部署关系强度 + L_PD(i,j) = w_f·M_PD_F + w_s·M_PD_S + w_t·M_PD_T + w_p·M_PD_P + w_i·M_PD_I + """ + # 权重参数 + w = self._weights("PD") + lambda_PD = self._lambda("PD") + + node_i = self.nodes_data['nodes'][i] + node_j = self.nodes_data['nodes'][j] + + # 1. 功能维度 M_PD_F + f_i_DP = node_i['function_vector']['f_DP'] + f_J_sum = sum([ + node_j['function_vector']['f_IA'], + node_j['function_vector']['f_IT'], + node_j['function_vector']['f_IC'], + node_j['function_vector']['f_CS'], + ]) + f_J_avg = f_J_sum / 4 + + # 修复：直接传递节点数据 + std_ij = self.utils.get_interface_standard(node_i, node_j) + r_ij = self.utils.get_role_match(node_i, node_j) + M_PD_F = f_i_DP * f_J_avg * std_ij * r_ij + + # 2. 空间维度 M_PD_S（平台部署对空间要求高） + d_ij = self.utils.calculate_distance( + node_i['spatial']['position'], + node_j['spatial']['position'] + ) + + R_match = self.utils.calculate_range_match( + node_i['spatial']['effective_radius'], + node_j['spatial']['effective_radius'], + d_ij + ) + # 修复：直接传递节点数据 + s_area = self.utils.get_area_relation(node_i, node_j) + if d_ij <= self.cfg["PD_SGM"]: + M_PD_S = 1 + else: + M_PD_S = np.exp(-d_ij / lambda_PD) * R_match * s_area + + # 3. 时间维度 M_PD_T + alpha = self.cfg["TIME"]["alpha"] + beta = self.cfg["TIME"]["beta"] + gamma = self.cfg["TIME"]["gamma"] + + t_i_resp = np.exp(-alpha * node_i['temporal']['response_time']) + t_i_cycle = np.exp(-beta * node_i['temporal']['cycle_time']) + + w_ij = self.utils.calculate_time_window_overlap(node_i, node_j) + + M_PD_T = t_i_resp * t_i_cycle * w_ij + + # 4. 性能维度 M_PD_P + p_i_core = node_i['performance']['core_performance'] + # 部署平台的生存能力 + p_i_surv = node_i['performance']['survivability'] + M_PD_P = p_i_core * p_i_surv + + # 5. 交互维度 M_PD_I + prot_ij = self.utils.get_protocol_compatibility(node_i, node_j) + fmt_ij = self.utils.get_format_compatibility(node_i, node_j) + # 注意：这里std_ij已经在上面计算过了 + M_PD_I = (prot_ij + fmt_ij + std_ij) / 3 + + # 综合计算 + L_PD = (w["w_f"] * M_PD_F + w["w_s"] * M_PD_S + w["w_t"] * M_PD_T + + w["w_p"] * M_PD_P + w["w_i"] * M_PD_I) + + return L_PD + + def calculate_CO_relation(self, i: str, j: str) -> float: + """ + 计算协同作战关系强度 + L_CO(i,j) = w_f·M_CO_F + w_s·M_CO_S + w_t·M_CO_T + w_p·M_CO_P + w_i·M_CO_I + """ + # 权重参数 + w = self._weights("CO") + lambda_CO = self._lambda("CO") + node_i = self.nodes_data['nodes'][i] + node_j = self.nodes_data['nodes'][j] + + # 1. 功能维度 M_CO_F + # 协同作战需要多个功能匹配 + f_i_avg = sum(node_i['function_vector'].values()) / len(node_i['function_vector']) + f_j_avg = sum(node_j['function_vector'].values()) / len(node_j['function_vector']) + + std_ij = self.utils.get_interface_standard(node_i, node_j) + r_ij = self.utils.get_role_match(node_i, node_j) + M_CO_F = f_i_avg * f_j_avg * std_ij * r_ij + + # 2. 空间维度 M_CO_S + d_ij = self.utils.calculate_distance( + node_i['spatial']['position'], + node_j['spatial']['position'] + ) + + R_match = self.utils.calculate_range_match( + node_i['spatial']['effective_radius'], + node_j['spatial']['effective_radius'], + d_ij + ) + + s_area = self.utils.get_area_relation(node_i, node_j) + M_CO_S = np.exp(-d_ij / lambda_CO) * R_match * s_area + + # 3. 时间维度 M_CO_T（协同需要时间同步） + alpha = self.cfg["TIME"]["alpha"] + beta = self.cfg["TIME"]["beta"] + gamma = self.cfg["TIME"]["gamma"] + + t_i_resp = np.exp(-alpha * node_i['temporal']['response_time']) + t_j_resp = np.exp(-alpha * node_j['temporal']['response_time']) + + t_i_cycle = np.exp(-beta * node_i['temporal']['cycle_time']) + t_j_cycle = np.exp(-beta * node_j['temporal']['cycle_time']) + + w_ij = self.utils.calculate_time_window_overlap(node_i, node_j) + + M_CO_T = (t_i_resp + t_j_resp) * (t_i_cycle + t_j_cycle) * w_ij * 0.25 + + # 4. 性能维度 M_CO_P + p_i_core = node_i['performance']['core_performance'] + p_j_core = node_j['performance']['core_performance'] + + mtbf_max = self.nodes_data['global_params']['mtbf_max'] + p_i_rel = node_i['performance']['mtbf'] / mtbf_max + p_j_rel = node_j['performance']['mtbf'] / mtbf_max + + p_i_surv = node_i['performance']['survivability'] + p_j_surv = node_j['performance']['survivability'] + # 协同性能取平均 + M_CO_P = ((p_i_core + p_j_core) / 2) * ((p_i_rel + p_j_rel) / 2) * ((p_i_surv + p_j_surv) / 2) + + # 5. 交互维度 M_CO_I + prot_ij = self.utils.get_protocol_compatibility(node_i, node_j) + fmt_ij = self.utils.get_format_compatibility(node_i, node_j) + sec_ij = self.utils.get_security_compatibility(node_i, node_j) + org_ij = self.utils.get_organization_relation(node_i, node_j) + hist_ij = self.utils.get_interaction_history(node_i, node_j) + M_CO_I = ((prot_ij + fmt_ij + sec_ij + org_ij) / 4) * hist_ij + + # 综合计算 + L_CO = (w["w_f"] * M_CO_F + w["w_s"] * M_CO_S + w["w_t"] * M_CO_T + + w["w_p"] * M_CO_P + w["w_i"] * M_CO_I) + + + return L_CO \ No newline at end of file diff --git a/final/scripts/inspect_dm_tables.py b/final/scripts/inspect_dm_tables.py new file mode 100644 index 0000000..29553a4 --- /dev/null +++ b/final/scripts/inspect_dm_tables.py @@ -0,0 +1,241 @@ +#!/usr/bin/env python3 +""" +在达梦数据库机器上运行，查看各表结构，用于编写 config 中的 db.query +用法: python scripts/inspect_dm_tables.py + +密码获取顺序：环境变量 DM_PASS > 下方 LOCAL_PASSWORD +若使用 LOCAL_PASSWORD，切勿提交到 git！可执行：git update-index --assume-unchanged scripts/inspect_dm_tables.py +""" +import os +import sys + +# 可选：在此直接填写密码（仅限本机调试，切勿提交到版本库！） +LOCAL_PASSWORD = None # 例如: "你的密码" + +# 支持从项目根或 scripts 目录运行 +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_project_root = os.path.dirname(_script_dir) +XLSX_PATH = os.path.join(_project_root, "1125暂时属性需求(1).xlsx") +if _project_root not in sys.path: + sys.path.insert(0, _project_root) + +try: + import dmPython +except ImportError: + print("请先安装: pip install dmPython") + sys.exit(1) + +CONFIG = { + "user": os.getenv("DM_USER", "SYSDBA"), + "password": os.getenv("DM_PASS") or LOCAL_PASSWORD, + "server": os.getenv("DM_HOST", "127.0.0.1"), + "port": int(os.getenv("DM_PORT", "9080")), + "schema": "SYSDBA", +} + +TABLES = [ + "OBJECTIVE_INSTANCE", + "METRIC_VALUE_INSTANCE", + "METRIC_PROFILE", + "ROLE_PROFILE", + "TASK_PROFILE", + "RELATION_INSTANCE", + "RES_FORMAL_ANALYSIS", +] + + +def _load_required_attrs(xlsx_path: str) -> list: + """从 1125暂时属性需求(1).xlsx 解析需求属性列表（一条=一个属性）""" + if not os.path.exists(xlsx_path): + print(f" (未找到 {xlsx_path}，使用内置属性列表)") + return _DEFAULT_REQ_ATTRS + try: + import pandas as pd + df = pd.read_excel(xlsx_path, header=None) + attrs = [] + for _, row in df.iterrows(): + v0 = row[0] + v2 = str(row[2]) if len(row) > 2 and pd.notna(row[2]) else "" + if pd.notna(v0) and str(v0).strip() and str(v0) != "属性代码名称": + attrs.append(str(v0).strip()) + if ":" in v2: + sub = v2.split(":")[0].strip() + if sub and sub not in attrs: + attrs.append(sub) + return attrs if attrs else _DEFAULT_REQ_ATTRS + except Exception as e: + print(f" (解析 xlsx 失败: {e}，使用内置属性列表)") + return _DEFAULT_REQ_ATTRS + + +# xlsx 解析失败时的兜底属性列表（来自 1125暂时属性需求） +_DEFAULT_REQ_ATTRS = [ + "f_IC", "f_IA", "f_CS", "f_IT", "f_DP", "f_CPS", "f_CC", + "position", "effective_radius", + "response_time", "cycle_time", "data_age", "time_window", + "core_performance", "survivability", "mtbf", + "protocol_list", "format_list", "interface_list", + "security_level", "org_unit", "nation", "history_success", +] + + +def main(): + if not CONFIG["password"]: + print("请设置环境变量 DM_PASS 或在脚本中填写 LOCAL_PASSWORD") + sys.exit(1) + + try: + conn = dmPython.connect( + user=CONFIG["user"], + password=CONFIG["password"], + server=CONFIG["server"], + port=CONFIG["port"], + schema=CONFIG["schema"], + ) + except Exception as e: + print(f"连接失败: {e}") + sys.exit(1) + + cursor = conn.cursor() + print("=" * 60) + print("达梦表结构（用于编写 config/db.query）") + print("=" * 60) + + for table in TABLES: + try: + cursor.execute(f'SELECT * FROM "{CONFIG["schema"]}"."{table}" WHERE 1=0') + cols = [d[0] for d in cursor.description] + print(f"\n【{table}】 列: {cols}") + except Exception as e: + print(f"\n【{table}】 查询失败: {e}") + + # 专门查询 METRIC_VALUE_INSTANCE 的字段详情（含完整属性） + mvi_table = "METRIC_VALUE_INSTANCE" + print("\n" + "=" * 80) + print(f"【{mvi_table}】 字段详情（完整属性）") + print("=" * 80) + try: + # 查询 DBA_TAB_COLUMNS 全部常用属性（达梦兼容） + cursor.execute( + """ + SELECT COLUMN_NAME, DATA_TYPE, DATA_LENGTH, DATA_PRECISION, DATA_SCALE, + NULLABLE, DATA_DEFAULT, COLUMN_ID + FROM DBA_TAB_COLUMNS + WHERE OWNER = ? AND TABLE_NAME = ? + ORDER BY COLUMN_ID + """, + (CONFIG["schema"], mvi_table), + ) + mvi_cols = cursor.fetchall() + # 表头 + fmt = " {:<18} {:<16} {:<10} {:<10} {:<8} {:<10} {:<20}" + print(fmt.format("列名", "数据类型", "长度", "精度", "标度", "可空", "默认值")) + print(" " + "-" * 90) + for row in mvi_cols: + col_name, data_type, data_length, data_precision, data_scale, nullable, data_default, col_id = row + # 类型显示：数值型用 类型(精度,标度)，字符型用 类型(长度) + if data_precision is not None: + type_str = f"{data_type}({data_precision},{data_scale or 0})" + elif data_length and data_type.upper() in ("VARCHAR", "CHAR", "VARCHAR2"): + type_str = f"{data_type}({data_length})" + elif data_length: + type_str = f"{data_type}({data_length})" + else: + type_str = data_type or "" + len_val = str(data_length) if data_length is not None else "-" + prec_val = str(data_precision) if data_precision is not None else "-" + scale_val = str(data_scale) if data_scale is not None else "-" + null_str = "NULL" if nullable == "Y" else "NOT NULL" + default_str = (str(data_default).strip() if data_default else "-")[:18] + print(fmt.format(col_name, type_str, len_val, prec_val, scale_val, null_str, default_str)) + + # 查询样本数据（若有） + cursor.execute(f'SELECT * FROM "{CONFIG["schema"]}"."{mvi_table}" WHERE ROWNUM <= 3') + sample_rows = cursor.fetchall() + if sample_rows: + col_names = [d[0] for d in cursor.description] + print(f"\n 样本数据 (最多 3 行):") + for i, row in enumerate(sample_rows, 1): + print(f" 行{i}: {dict(zip(col_names, row))}") + else: + print("\n (表无数据)") + except Exception as e: + print(f" 查询失败: {e}") + + # 按 1125暂时属性需求(1).xlsx 查询 METRIC_VALUE_INSTANCE（一条=一个属性） + print("\n" + "=" * 80) + print("【METRIC_VALUE_INSTANCE】按需求属性查询（xlsx 中一条 = 一个 METRIC_ID）") + print("=" * 80) + try: + # 1. 从 xlsx 解析需求属性列表 + req_attrs = _load_required_attrs(XLSX_PATH) + print(f"\n xlsx 需求属性 ({len(req_attrs)} 个): {req_attrs}") + + # 2. 查询库中实际存在的 METRIC_ID（distinct） + cursor.execute( + f'SELECT DISTINCT METRIC_ID FROM "{CONFIG["schema"]}"."METRIC_VALUE_INSTANCE" ORDER BY METRIC_ID' + ) + db_metric_ids = [r[0] for r in cursor.fetchall()] + print(f"\n 库中已有 METRIC_ID ({len(db_metric_ids)} 个): {db_metric_ids or '(无数据)'}") + + # 3. METRIC_PROFILE 对照（METRIC_ID -> 中文名） + cursor.execute( + f'SELECT METRIC_ID, METRIC_NAME FROM "{CONFIG["schema"]}"."METRIC_PROFILE"' + ) + profile_map = dict(cursor.fetchall()) + if profile_map: + print("\n METRIC_PROFILE 对照:") + for mid, mname in sorted(profile_map.items()): + print(f" {mid} -> {mname}") + + # 4. 交集：需求属性中在库里有的 + in_both = [a for a in req_attrs if a in db_metric_ids] + # 需求有但库无 + missing = [a for a in req_attrs if a not in db_metric_ids] + # 库有但需求未列 + extra = [m for m in db_metric_ids if m not in req_attrs] + + print(f"\n 需求与库交集 (可查): {in_both or '无'}") + print(f" 需求有但库无: {missing or '无'}") + print(f" 库有但需求未列: {extra or '无'}") + + # 5. 按属性查询示例 SQL + print("\n --- 按属性查询示例 SQL ---") + for attr in (in_both or req_attrs)[:5]: # 最多展示 5 个 + sql = f'SELECT * FROM "{CONFIG["schema"]}"."METRIC_VALUE_INSTANCE" WHERE METRIC_ID = \'{attr}\'' + print(f" -- {attr}: {sql}") + if len(in_both or req_attrs) > 5: + print(f" ... 其余属性同理，将 METRIC_ID = 'xxx' 替换即可") + except Exception as e: + print(f" 查询失败: {e}") + import traceback + traceback.print_exc() + + cursor.execute( + """ + SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE + FROM DBA_TAB_COLUMNS + WHERE OWNER = ? + ORDER BY TABLE_NAME, COLUMN_ID + """, + (CONFIG["schema"],), + ) + rows = cursor.fetchall() + + print("\n" + "=" * 60) + print("所有列详情 (TABLE_NAME | COLUMN_NAME | DATA_TYPE)") + print("=" * 60) + cur_table = None + for table, col, dtype in rows: + if table != cur_table: + cur_table = table + print(f"\n--- {table} ---") + print(f" {col}: {dtype}") + + cursor.close() + conn.close() + print("\n完成。请根据上述结构在 config 中编写 db.query。") + + +if __name__ == "__main__": + main() diff --git a/final/scripts/run_all.py b/final/scripts/run_all.py new file mode 100644 index 0000000..104fa07 --- /dev/null +++ b/final/scripts/run_all.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +""" +一键运行：实体属性表 + 三元组 的读取、清洗、导出 +用法: python scripts/run_all.py +""" +import json +import os +import sys + +_script_dir = os.path.dirname(os.path.abspath(__file__)) +_project_root = os.path.dirname(_script_dir) +sys.path.insert(0, _project_root) + + +def _json_serializer(obj): + if hasattr(obj, 'isoformat'): + return obj.isoformat() + if hasattr(obj, 'item'): + return obj.item() + if isinstance(obj, float) and (obj != obj or obj == float('inf') or obj == float('-inf')): + return None + raise TypeError(f'Object of type {type(obj).__name__} is not JSON serializable') + + +def load_config(project_root: str) -> dict: + config_dir = os.path.join(project_root, 'config') + json_path = os.path.join(config_dir, 'config.json') + yaml_path = os.path.join(config_dir, 'config.yaml') + if os.path.exists(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + return json.load(f) + if os.path.exists(yaml_path): + try: + import yaml + with open(yaml_path, 'r', encoding='utf-8') as f: + return yaml.safe_load(f) + except ImportError: + pass + return {'source': 'json', 'json': {}, 'triplet': {}, 'db': {}} + + +def _resolve_path(path: str, project_root: str) -> str: + return path if os.path.isabs(path) else os.path.join(project_root, path) + + +def main(): + from src.cleaner import AdvancedDataCleaner + from src.data_loader import load_data, load_triplets_from_dm, load_from_json, run_formalization_pipeline + from src.triplet_cleaner import clean_triplets + + config = load_config(_project_root) + source = config.get('source', 'json') + tc = config.get('triplet', {}) + + # 三元组：db 模式默认处理；json 模式需 triplet.input_file；triplet.enabled=false 可关闭 + triplet_input = tc.get('input_file') + run_triplets = tc.get('enabled', source == 'db' or bool(triplet_input)) + + print("=" * 50) + print("作战体系数据清洗 - 一键运行") + print("=" * 50) + print(f"数据源: {source}") + + # ---------- 1. 实体属性表 ---------- + print("\n【1/2】实体属性表 读取与清洗") + try: + data, paths = load_data(config, _project_root) + except (FileNotFoundError, ValueError) as e: + print(f"错误：{e}") + sys.exit(1) + + os.makedirs(os.path.dirname(paths['report']), exist_ok=True) + cleaner = AdvancedDataCleaner(output_file=paths['output'], report_file=paths['report'], data=data) + cleaner.run() + valid_ids = cleaner.get_valid_target_ids() + print(f" -> 输出: {paths['output']}") + print(f" -> 报告: {paths['report']}") + + # ---------- 2. 三元组 ---------- + if not run_triplets: + print("\n【2/2】三元组 已跳过（triplet.enabled=false 或 json 模式未配置 input_file）") + print("\n完成。") + return + + print("\n【2/2】三元组 读取与清洗") + + if source == 'db': + triplets = load_triplets_from_dm(config, _project_root) + print(f" 从达梦读取 {len(triplets)} 条三元组") + elif triplet_input: + input_path = _resolve_path(triplet_input, _project_root) + if os.path.exists(input_path): + raw = load_from_json(input_path, _project_root) + triplets = raw.get("triples", raw) if isinstance(raw, dict) else raw + triplets = triplets if isinstance(triplets, list) else [] + print(f" 从 {triplet_input} 读取 {len(triplets)} 条三元组") + else: + print(f" 跳过：三元组文件不存在 {input_path}") + triplets = [] + else: + print(" 跳过：json 模式未配置 triplet.input_file") + triplets = [] + + if not triplets: + print(" 无三元组数据，跳过导出") + print("\n完成。") + return + + # 三元组清洗配置 + clean_cfg = tc.get('clean', True) + do_clean = {'deduplicate': True, 'filter_orphans': False, 'remove_null_core': True, 'normalize_relation_type': True} if clean_cfg is True else (clean_cfg if isinstance(clean_cfg, dict) else {}) + deduplicate = do_clean.get('deduplicate', True) if do_clean else False + filter_orphans = do_clean.get('filter_orphans', False) if do_clean else False + remove_null_core = do_clean.get('remove_null_core', True) if do_clean else False + normalize_relation_type = do_clean.get('normalize_relation_type', True) if do_clean else False + + if do_clean and (deduplicate or filter_orphans or remove_null_core or normalize_relation_type): + triplets, report = clean_triplets( + triplets, + valid_ids=valid_ids if filter_orphans else None, + deduplicate=deduplicate, + filter_orphans=filter_orphans, + remove_null_core=remove_null_core, + normalize_relation_type=normalize_relation_type, + ) + report_path = _resolve_path(tc.get('report_file', 'report/triplet_cleaning_report.json'), _project_root) + os.makedirs(os.path.dirname(report_path), exist_ok=True) + with open(report_path, 'w', encoding='utf-8') as f: + json.dump(report, f, ensure_ascii=False, indent=2, default=_json_serializer) + print(f" 清洗: {report['original_count']} -> {report['final_count']}") + print(f" 报告: {report_path}") + + # 转为输出格式：head, head_type, relation, tail, tail_type + from src.triplet_cleaner import transform_triplet_output_format + id_to_role = cleaner.get_id_to_role() + triplets = transform_triplet_output_format(triplets, id_to_role) + + output_path = _resolve_path(tc.get('output_file', 'data/triples.json'), _project_root) + os.makedirs(os.path.dirname(output_path), exist_ok=True) + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(triplets, f, ensure_ascii=False, indent=2, default=_json_serializer) + print(f" 输出: {output_path}") + + # ---------- 3. 模块1.3：形式化入库 ---------- + formal_cfg = config.get("formal", {}) + if source == "db" and formal_cfg.get("enabled", False): + print("\n【3/3】形式化模块 读取-补齐-入库") + try: + result = run_formalization_pipeline(config) + if result.get("ok"): + stats = result.get("stats", {}) + print(f" 批次: {result.get('batch_id')}") + print(f" 推理三元组: {stats.get('total_triples', 0)}") + print(f" 形式化入库: {stats.get('inserted_formal_count', 0)}") + print(f" 属性补齐写回: {stats.get('generated_attrs_count', 0)}") + else: + print(f" 形式化流程跳过: {result.get('reason', 'unknown')}") + except Exception as e: + print(f" 形式化流程失败: {e}") + raise + elif formal_cfg.get("enabled", False): + print("\n【3/3】形式化模块 已跳过（仅 source=db 时启用）") + + print("\n完成。") + +if __name__ == "__main__": + main() diff --git a/final/scripts/utils.py b/final/scripts/utils.py new file mode 100644 index 0000000..6252d16 --- /dev/null +++ b/final/scripts/utils.py @@ -0,0 +1,110 @@ +import numpy as np +from typing import Dict, List, Any +from scipy.spatial.distance import euclidean + +class NetworkUtils: + """无需 aux_data，全部现场从节点属性读取""" + """ 角色匹配矩阵暂时没加 """ + # ----------- 通用兜底 ----------- + _DEFAULT = { + "protocol": 0.80, + "format": 0.85, + "security": 0.90, + "role": 0.70, + "area": 0.50, + "org": 0.70, + "area_1": 1, + "area_2": 0.7, + "history": 0.8 + } + + # ----------- 空间 ----------- + @staticmethod + def calculate_distance(pos1, pos2): + return euclidean(pos1, pos2) + + @staticmethod + def calculate_range_match(R_i: float, R_j: float, d_ij: float) -> float: + r_min = min(R_i, R_j) + if d_ij <= r_min: + return 1.0 + return r_min / d_ij if d_ij > 0 else 0.0 + + @staticmethod + def calculate_time_window_overlap(node_i, node_j): + w_i = node_i["temporal"].get("time_window", 24) + w_j = node_j["temporal"].get("time_window", 24) + overlap = min(w_i, w_j) + return overlap / max(w_i, w_j, 1) + + # ----------- 协议兼容性：Jaccard ----------- + @staticmethod + def get_protocol_compatibility(node_i, node_j, _dummy=None): + """节点属性里放 'protocol_list'""" + p_i = set(node_i.get("protocol_list", [])) + p_j = set(node_j.get("protocol_list", [])) + if not p_i or not p_j: + return NetworkUtils._DEFAULT["protocol"] + intersection = len(p_i & p_j) + union = len(p_i | p_j) + return intersection / union if union else NetworkUtils._DEFAULT["protocol"] + + # ----------- 安全等级：1-5 映射 0-1 ----------- + @staticmethod + def get_security_compatibility(node_i, node_j, _dummy=None): + lv_i = node_i.get("security_level", 3) + lv_j = node_j.get("security_level", 3) + gap = abs(lv_i - lv_j) + return max(0, 1 - gap / 5) + + # ----------- 组织隶属：同单位给高分 ----------- + @staticmethod + def get_organization_relation(node_i, node_j, _dummy=None): + unit_i = node_i.get("org_unit", "") + unit_j = node_j.get("org_unit", "") + return 1.0 if unit_i == unit_j and unit_i else NetworkUtils._DEFAULT["org"] + + # ----------- 历史交互：直接读属性 ----------- + @staticmethod + def get_interaction_history(node_i, node_j, _dummy=None): + his_i = node_i.get("history_success", NetworkUtils._DEFAULT["history"]) + # print("111111111111111111111",node_i.get("history_success")) + his_j = node_j.get("history_success", NetworkUtils._DEFAULT["history"]) + return (his_i + his_j) / 2 + + # ----------- 区域关联：同 code 高分 ----------- + @staticmethod + def get_area_relation(node_i, node_j, _dummy=None): + code_i = node_i.get("nation") + code_j = node_j.get("nation") + if code_i == code_j: + return NetworkUtils._DEFAULT["area_1"] + else: + return NetworkUtils._DEFAULT["area_2"] + + # ----------- 接口/角色/格式：若节点属性扩展了再读，否则给默认 ----------- + @staticmethod + def get_role_match(node_i, node_j, _dummy=None): + return node_i.get("role_match", NetworkUtils._DEFAULT["role"]) + + @staticmethod + def get_interface_standard(node_i, node_j, _dummy=None) -> float: + """接口标准兼容性 = Jaccard(i_list, j_list)""" + i_set = set(node_i.get("interface_list", [])) + j_set = set(node_j.get("interface_list", [])) + if not i_set or not j_set: + return NetworkUtils._DEFAULT["protocol"] # 默认0.8 + inter = len(i_set & j_set) + union = len(i_set | j_set) + return inter / union if union else NetworkUtils._DEFAULT["protocol"] + + @staticmethod + def get_format_compatibility(node_i, node_j, _dummy=None) -> float: + """数据格式兼容性 = Jaccard(i_format, j_format)""" + i_set = set(node_i.get("format_list", [])) + j_set = set(node_j.get("format_list", [])) + if not i_set or not j_set: + return NetworkUtils._DEFAULT["format"] # 默认0.85 + inter = len(i_set & j_set) + union = len(i_set | j_set) + return inter / union if union else NetworkUtils._DEFAULT["format"] \ No newline at end of file diff --git a/final/src/__init__.py b/final/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/final/src/__pycache__/__init__.cpython-313.pyc b/final/src/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..dc2ca63 Binary files /dev/null and b/final/src/__pycache__/__init__.cpython-313.pyc differ diff --git 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differ diff --git a/final/src/attribute_schema.py b/final/src/attribute_schema.py new file mode 100644 index 0000000..d32bca1 --- /dev/null +++ b/final/src/attribute_schema.py @@ -0,0 +1,701 @@ +# -*- coding: utf-8 -*- +""" +属性规范定义模块 - 基于 1125暂时属性需求 +实现内容与格式的双重对齐：格式校验（结构类型）+ 内容校验（取值范围、类型） +""" + +import math +from typing import Any, Dict, List, Optional, Tuple + +# ============ 目标 Schema 定义（来自 1125暂时属性需求.xlsx） ============ + +# function_vector: 字典，键为功能维度，值为 [0,1] 浮点数 +FUNCTION_VECTOR_KEYS = [ + "f_IC", # 情报收集能力 + "f_IA", # 信息分析能力 + "f_CS", # 协同作战能力 + "f_IT", # 信息传输能力 + "f_DP", # 数据处理能力 + "f_CPS", # 指挥控制能力 + "f_CC", # 综合保障能力 +] + +# spatial: 字典 +SPATIAL_KEYS = { + "position": lambda v: isinstance(v, (list, tuple)) and len(v) >= 2 + and all(isinstance(x, (int, float)) for x in v[:2]), + "effective_radius": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), +} + +# temporal: 字典 +TEMPORAL_KEYS = { + "response_time": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), + "cycle_time": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), + "data_age": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), + "time_window": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), +} + +# performance: 字典 +PERFORMANCE_KEYS = { + "core_performance": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool) + and 0 <= float(v) <= 1, + "survivability": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool) + and 0 <= float(v) <= 1, + "mtbf": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), +} + +# nodes.json 输出字段（完全对齐 xlsx，仅含规范字段 + 节点标识） +# TARGET_ID 为节点唯一标识（三元组 head/tail 引用所需） +OUTPUT_SCHEMA_FIELDS = [ + "TARGET_ID", + "function_vector", + "spatial", + "temporal", + "performance", + "protocol_list", + "format_list", + "interface_list", + "security_level", + "org_unit", + "nation", + "history_success", +] + +# 原始字段 -> 目标 schema 映射（用于 flat 数据转换为规范格式） +RAW_TO_TARGET_MAPPING = { + # function_vector 从多个能力字段推导 + "function_vector": { + "f_IC": ["DETECTION_ACCURACY", "TARGET_RECOGNITION_CAPABILITY"], # 情报收集 + "f_IA": ["INFORMATION_FUSION_CAPABILITY", "DETECTION_ACCURACY"], # 信息分析 + "f_CS": ["MOBILITY", "ENVIRONMENT_ADAPTABILITY"], # 协同作战 + "f_IT": ["ANTI_JAMMING_CAPABILITY"], # 信息传输（抗干扰） + "f_DP": ["PROCESSING_CAPACITY", "INFORMATION_FUSION_CAPABILITY"], # 数据处理 + "f_CPS": ["DECISION_RESPONSE_TIME"], # 指挥控制（响应时间反推） + "f_CC": ["SUPPORT_CAPACITY", "LOAD_CAPACITY"], # 综合保障 + }, + "spatial": { + "position": ["X", "Y"], + "effective_radius": ["COMMUNICATION_RANGE"], + }, + "temporal": { + "response_time": ["DECISION_RESPONSE_TIME"], + "cycle_time": ["REFRESH_RATE"], + "data_age": ["TRANSMISSION_DELAY"], # 传输延迟可近似数据新鲜度 + "time_window": ["REFRESH_RATE"], # 周期倒数近似时间窗口 + }, + "performance": { + "core_performance": ["STRIKE_ACCURACY", "DETECTION_ACCURACY", "MOBILITY"], + "survivability": ["ENVIRONMENT_ADAPTABILITY", "ANTI_JAMMING_CAPABILITY"], + "mtbf": ["RELIABILITY", "SYSTEM_RELIABILITY"], + }, + "nation": ["COUNTRY_REGION"], + "org_unit": ["LEVEL", "ROLE_ID"], + "history_success": [], # 无直接映射，需默认或缺失 +} + + +def _clamp_0_1(val: Any) -> Optional[float]: + """将值限制在 [0,1] 范围内，无效返回 None""" + if val is None or (isinstance(val, float) and (val != val)): + return None + try: + f = float(val) + if f < 0: + return 0.0 + if f > 1: + return 1.0 + return f + except (TypeError, ValueError): + return None + + +def _safe_float(val: Any) -> Optional[float]: + if val is None or (isinstance(val, float) and (val != val)): + return None + try: + return float(val) + except (TypeError, ValueError): + return None + + +def _safe_int(val: Any, lo: int = None, hi: int = None) -> Optional[int]: + if val is None: + return None + try: + i = int(float(val)) + if lo is not None and i < lo: + return lo + if hi is not None and i > hi: + return hi + return i + except (TypeError, ValueError): + return None + + +def _ensure_list_of_strings(val: Any) -> List[str]: + """确保为字符串列表""" + if val is None: + return [] + if isinstance(val, list): + return [str(x).strip() for x in val if x is not None and str(x).strip()] + if isinstance(val, str) and val.strip(): + return [val.strip()] + return [] + + +# ---------- 固定参考值转换（用于 function_vector 中需特殊归一化的维度）---------- +# 参考时间（秒）：响应时间 t 越短，f_CPS 越高 +_CPS_REF_TIME = 30.0 +# 处理能力参考上限 +_FDP_REF_CAP = 10000.0 +# 保障/载荷能力参考 +_FCC_REF_SUPPORT = 10000.0 +_FCC_REF_LOAD = 100.0 + + +def _response_time_to_cps(val: Any) -> Optional[float]: + """响应时间(秒) -> f_CPS [0,1]，响应越快能力越高""" + f = _safe_float(val) + if f is None or f < 0: + return None + v = 1.0 / (1.0 + f / _CPS_REF_TIME) + return min(1.0, max(0.0, v)) + + +def _processing_to_fdp(raw: dict) -> Optional[float]: + """PROCESSING_CAPACITY 或 INFORMATION_FUSION_CAPABILITY -> f_DP [0,1]""" + pc = raw.get("PROCESSING_CAPACITY") + if pc is not None and (not isinstance(pc, float) or pc == pc): + try: + v = float(pc) + if v < 0: + return 0.0 + norm = math.log10(1 + v) / math.log10(1 + _FDP_REF_CAP) + return min(1.0, norm) + except (TypeError, ValueError): + pass + ifc = raw.get("INFORMATION_FUSION_CAPABILITY") + if ifc is not None and (not isinstance(ifc, float) or ifc == ifc): + return _clamp_0_1(ifc) + return None + + +def _support_load_to_fcc(raw: dict) -> Optional[float]: + """SUPPORT_CAPACITY 与 LOAD_CAPACITY -> f_CC [0,1]，支持组合""" + support = raw.get("SUPPORT_CAPACITY") + load = raw.get("LOAD_CAPACITY") + s_norm = None + l_norm = None + if support is not None and (not isinstance(support, float) or support == support): + try: + v = float(support) + if v >= 0: + s_norm = min(1.0, math.log10(1 + v) / math.log10(1 + _FCC_REF_SUPPORT)) + except (TypeError, ValueError): + pass + if load is not None and (not isinstance(load, float) or load == load): + try: + v = float(load) + if v >= 0: + l_norm = min(1.0, v / _FCC_REF_LOAD) + except (TypeError, ValueError): + pass + if s_norm is not None and l_norm is not None: + return round(0.6 * s_norm + 0.4 * l_norm, 4) + if s_norm is not None: + return s_norm + return l_norm + + +# ============ 格式校验 ============ + +def check_format_function_vector(val: Any) -> Tuple[bool, str]: + """校验 function_vector 格式：必须为字典""" + if val is None: + return True, "optional" + if not isinstance(val, dict): + return False, f"期望 dict，实际 {type(val).__name__}" + for k in val: + if not isinstance(k, str): + return False, f"键必须为 str，发现 {type(k).__name__}" + v = val[k] + if not isinstance(v, (int, float)) or isinstance(v, bool): + return False, f"键 {k} 的值应为 [0,1] 浮点数，实际 {type(v).__name__}" + f = float(v) + if f < 0 or f > 1: + return False, f"键 {k} 的值 {f} 超出 [0,1]" + return True, "ok" + + +def check_format_spatial(val: Any) -> Tuple[bool, str]: + """校验 spatial 格式：必须为字典，含 position 与 effective_radius""" + if val is None: + return True, "optional" + if not isinstance(val, dict): + return False, f"期望 dict，实际 {type(val).__name__}" + pos = val.get("position") + if pos is not None: + if not isinstance(pos, (list, tuple)) or len(pos) < 2: + return False, "position 应为 [x, y] 列表" + if not all(isinstance(x, (int, float)) for x in pos[:2]): + return False, "position 元素应为数字" + rad = val.get("effective_radius") + if rad is not None and not isinstance(rad, (int, float)): + return False, "effective_radius 应为数字" + return True, "ok" + + +def check_format_temporal(val: Any) -> Tuple[bool, str]: + """校验 temporal 格式""" + if val is None: + return True, "optional" + if not isinstance(val, dict): + return False, f"期望 dict，实际 {type(val).__name__}" + for k in ["response_time", "cycle_time", "data_age", "time_window"]: + v = val.get(k) + if v is not None and not isinstance(v, (int, float)): + return False, f"{k} 应为数字" + return True, "ok" + + +def check_format_performance(val: Any) -> Tuple[bool, str]: + """校验 performance 格式""" + if val is None: + return True, "optional" + if not isinstance(val, dict): + return False, f"期望 dict，实际 {type(val).__name__}" + for k in ["core_performance", "survivability"]: + v = val.get(k) + if v is not None: + try: + f = float(v) + if f < 0 or f > 1: + return False, f"{k} 应在 [0,1]，实际 {f}" + except (TypeError, ValueError): + return False, f"{k} 应为数字" + return True, "ok" + + +def check_format_protocol_list(val: Any) -> Tuple[bool, str]: + if val is None: + return True, "optional" + if not isinstance(val, list): + return False, f"期望 list，实际 {type(val).__name__}" + for i, x in enumerate(val): + if not isinstance(x, str): + return False, f"元素[{i}] 应为 str，实际 {type(x).__name__}" + return True, "ok" + + +def check_format_format_list(val: Any) -> Tuple[bool, str]: + return check_format_protocol_list(val) + + +def check_format_interface_list(val: Any) -> Tuple[bool, str]: + return check_format_protocol_list(val) + + +def check_format_security_level(val: Any) -> Tuple[bool, str]: + if val is None: + return True, "optional" + if not isinstance(val, int) or isinstance(val, bool): + try: + int(val) + except (TypeError, ValueError): + return False, f"期望 int [1,5]，实际 {type(val).__name__}" + v = int(val) + if v < 1 or v > 5: + return False, f"security_level 应在 [1,5]，实际 {v}" + return True, "ok" + + +def check_format_org_unit(val: Any) -> Tuple[bool, str]: + if val is None: + return True, "optional" + if not isinstance(val, str): + return False, f"期望 str，实际 {type(val).__name__}" + return True, "ok" + + +def check_format_nation(val: Any) -> Tuple[bool, str]: + return check_format_org_unit(val) + + +def check_format_history_success(val: Any) -> Tuple[bool, str]: + if val is None: + return True, "optional" + if not isinstance(val, (int, float)) or isinstance(val, bool): + return False, f"期望 float [0,1]，实际 {type(val).__name__}" + f = float(val) + if f < 0 or f > 1: + return False, f"history_success 应在 [0,1]，实际 {f}" + return True, "ok" + + +# ============ 内容校验（取值范围、语义） ============ + +def check_content_function_vector(val: dict) -> List[str]: + """内容校验：键是否在规范内，值是否合法""" + issues = [] + if not isinstance(val, dict): + return ["非字典类型"] + for k, v in val.items(): + if k not in FUNCTION_VECTOR_KEYS: + issues.append(f"未知键: {k}") + else: + try: + f = float(v) + if f < 0 or f > 1: + issues.append(f"{k}={f} 超出 [0,1]") + except (TypeError, ValueError): + issues.append(f"{k} 值非数字: {v}") + return issues + + +def check_content_spatial(val: dict) -> List[str]: + issues = [] + if not isinstance(val, dict): + return ["非字典类型"] + pos = val.get("position") + if pos is not None and len(pos) >= 2: + if not all(isinstance(x, (int, float)) for x in pos[:2]): + issues.append("position 含非数字") + rad = val.get("effective_radius") + if rad is not None: + try: + f = float(rad) + if f < 0: + issues.append("effective_radius 不应为负") + except (TypeError, ValueError): + issues.append("effective_radius 非数字") + return issues + + +def check_content_temporal(val: dict) -> List[str]: + issues = [] + if not isinstance(val, dict): + return ["非字典类型"] + for k in ["response_time", "cycle_time", "data_age", "time_window"]: + v = val.get(k) + if v is not None: + try: + f = float(v) + if f < 0: + issues.append(f"{k} 不应为负: {f}") + except (TypeError, ValueError): + issues.append(f"{k} 非数字: {v}") + return issues + + +def check_content_performance(val: dict) -> List[str]: + issues = [] + if not isinstance(val, dict): + return ["非字典类型"] + for k in ["core_performance", "survivability"]: + v = val.get(k) + if v is not None: + try: + f = float(v) + if f < 0 or f > 1: + issues.append(f"{k} 超出 [0,1]: {f}") + except (TypeError, ValueError): + issues.append(f"{k} 非数字: {v}") + mtbf = val.get("mtbf") + if mtbf is not None: + try: + f = float(mtbf) + if f < 0: + issues.append("mtbf 不应为负") + except (TypeError, ValueError): + issues.append("mtbf 非数字") + return issues + + +# ============ 统一校验入口 ============ + +FORMAT_CHECKERS = { + "function_vector": check_format_function_vector, + "spatial": check_format_spatial, + "temporal": check_format_temporal, + "performance": check_format_performance, + "protocol_list": check_format_protocol_list, + "format_list": check_format_format_list, + "interface_list": check_format_interface_list, + "security_level": check_format_security_level, + "org_unit": check_format_org_unit, + "nation": check_format_nation, + "history_success": check_format_history_success, +} + +CONTENT_CHECKERS = { + "function_vector": check_content_function_vector, + "spatial": check_content_spatial, + "temporal": check_content_temporal, + "performance": check_content_performance, +} + + +def validate_record( + record: dict, + target_schema_fields: Optional[List[str]] = None, +) -> Dict[str, Any]: + """ + 对单条记录进行格式与内容双重校验。 + 若 record 已包含目标 schema 字段，则直接校验； + 否则会先通过 raw_to_target_schema 转换后再校验。 + """ + target_fields = target_schema_fields or list(FORMAT_CHECKERS.keys()) + result = { + "format_errors": {}, + "content_errors": {}, + "aligned": False, + "transformed": False, + } + + # 若缺少目标字段，尝试从原始字段转换 + has_target = any(f in record for f in target_fields) + data = record + if not has_target: + data = raw_to_target_schema(record) + result["transformed"] = True + + # 格式校验 + for field in target_fields: + val = data.get(field) + if val is None and field in ["protocol_list", "format_list", "interface_list"]: + val = [] + checker = FORMAT_CHECKERS.get(field) + if checker: + ok, msg = checker(val) + else: + ok, msg = True, "no_checker" + if not ok: + result["format_errors"][field] = msg + + # 内容校验（仅对复杂类型） + for field in CONTENT_CHECKERS: + val = data.get(field) + if val is None: + continue + issues = CONTENT_CHECKERS[field](val) + if issues: + result["content_errors"][field] = issues + + result["aligned"] = ( + len(result["format_errors"]) == 0 and len(result["content_errors"]) == 0 + ) + return result + + +# ============ 原始数据 -> 目标 Schema 转换 ============ + +def _get_first_valid(row: dict, keys: List[str], cast=_clamp_0_1) -> Optional[Any]: + """从 row 中按 keys 顺序取第一个非空值并转换""" + for k in keys: + v = row.get(k) + if v is not None and (not isinstance(v, float) or v == v): + return cast(v) if cast else v + return None + + +def raw_to_target_schema(raw: dict) -> dict: + """ + 将扁平原始记录转换为符合 1125 属性规范的目标结构。 + 仅输出 xlsx 定义字段，不保留任何原始其他字段。 + """ + # TARGET_ID：节点唯一标识 + tid = raw.get("TARGET_ID") + tid = str(tid).strip() if tid is not None and str(tid).strip() else None + + out: Dict[str, Any] = {f: None for f in OUTPUT_SCHEMA_FIELDS} + out["TARGET_ID"] = tid + + # function_vector（f_CPS/f_DP/f_CC 使用固定参考值转换，其余用 _clamp_0_1） + fv = {} + _fv_mapping = RAW_TO_TARGET_MAPPING["function_vector"] + for fk, raw_keys in _fv_mapping.items(): + if fk == "f_CPS": + t = raw.get("DECISION_RESPONSE_TIME") + val = _response_time_to_cps(t) + elif fk == "f_DP": + val = _processing_to_fdp(raw) + elif fk == "f_CC": + val = _support_load_to_fcc(raw) + else: + val = _get_first_valid(raw, raw_keys, _clamp_0_1) + if val is not None: + fv[fk] = round(val, 4) + # 下游 f_relation1125/f_net1125 要求必为 dict，无推导值时用默认（避免 NoneType） + if not fv: + fv = {fk: 0.5 for fk in FUNCTION_VECTOR_KEYS} + out["function_vector"] = fv + + # spatial（支持 X/x、Y/y、position、POSITION_X/Y、LONGITUDE/LATITUDE；确保元素恒为 float） + def _safe_coord(v: Any) -> float: + if v is None or (isinstance(v, float) and v != v): + return 0.0 + try: + return round(float(v), 2) + except (TypeError, ValueError): + return 0.0 + + pos_raw = raw.get("position") or raw.get("POSITION") + if isinstance(pos_raw, (list, tuple)) and len(pos_raw) >= 2: + x, y = _safe_coord(pos_raw[0]), _safe_coord(pos_raw[1]) + else: + x = raw.get("X") or raw.get("x") or raw.get("POSITION_X") or raw.get("LONGITUDE") + y = raw.get("Y") or raw.get("y") or raw.get("POSITION_Y") or raw.get("LATITUDE") + x, y = _safe_coord(x), _safe_coord(y) + pos = [x, y] + radius = _get_first_valid(raw, RAW_TO_TARGET_MAPPING["spatial"]["effective_radius"], _safe_float) + sp = { + "position": pos, + "effective_radius": round(float(radius), 4) if radius is not None else 1.0, + } + out["spatial"] = sp + + # temporal + _temporal_defaults = {"response_time": 1.0, "cycle_time": 1.0, "data_age": 1.0, "time_window": 24.0} + temporal = {} + for tk, rks in RAW_TO_TARGET_MAPPING["temporal"].items(): + v = _get_first_valid(raw, rks, _safe_float) + if v is not None: + temporal[tk] = round(float(v), 4) + for k, default in _temporal_defaults.items(): + temporal.setdefault(k, default) + out["temporal"] = temporal + + # performance + _perf_defaults = {"core_performance": 0.5, "survivability": 0.5, "mtbf": 1.0} + perf = {} + for pk, rks in RAW_TO_TARGET_MAPPING["performance"].items(): + cast = _clamp_0_1 if pk in ["core_performance", "survivability"] else _safe_float + v = _get_first_valid(raw, rks, cast) + if v is not None: + perf[pk] = round(float(v), 4) + for k, default in _perf_defaults.items(): + perf.setdefault(k, default) + out["performance"] = perf + + # protocol_list, format_list, interface_list + for field in ["protocol_list", "format_list", "interface_list"]: + raw_key = field.upper().replace("_LIST", "") + out[field] = _ensure_list_of_strings(raw.get(raw_key, raw.get(field))) + + # security_level：下游 utils.get_security_compatibility 会做 lv_i - lv_j，不可为 None + sl = raw.get("security_level") or raw.get("SECURITY_LEVEL") + out["security_level"] = _safe_int(sl, 1, 5) if sl is not None else 3 + + # org_unit, nation + nation = _get_first_valid(raw, RAW_TO_TARGET_MAPPING["nation"], lambda v: str(v).strip() if v is not None else None) + out["nation"] = nation + org = _get_first_valid(raw, RAW_TO_TARGET_MAPPING["org_unit"], lambda v: str(v).strip() if v is not None else None) + out["org_unit"] = org + + # history_success：下游 utils.get_interaction_history 会做 (his_i + his_j)/2，不可为 None + hs = raw.get("history_success") or raw.get("HISTORY_SUCCESS") + if hs is not None: + v = _clamp_0_1(hs) + out["history_success"] = round(v, 4) if v is not None else 0.8 + else: + out["history_success"] = 0.8 + + return out + + +def align_and_validate_records( + records: List[dict], + id_key: str = "TARGET_ID", +) -> Tuple[List[dict], dict]: + """ + 对记录列表进行转换、格式与内容校验。 + 返回：(对齐后的记录列表, 统计报告) + """ + aligned = [] + report = { + "total": len(records), + "format_errors_by_field": {}, + "content_errors_by_field": {}, + "records_with_errors": 0, + "aligned_count": 0, + "transformed_count": 0, + "samples": [], + } + + for rec in records: + rid = rec.get(id_key, "?") + val_result = validate_record(rec) + aligned_rec = raw_to_target_schema(rec) + aligned.append(aligned_rec) + + if val_result["transformed"]: + report["transformed_count"] += 1 + if val_result["aligned"]: + report["aligned_count"] += 1 + else: + report["records_with_errors"] += 1 + for f, msg in val_result["format_errors"].items(): + report["format_errors_by_field"].setdefault(f, 0) + report["format_errors_by_field"][f] += 1 + for f, issues in val_result["content_errors"].items(): + report["content_errors_by_field"].setdefault(f, 0) + report["content_errors_by_field"][f] += 1 + if len(report["samples"]) < 5: + report["samples"].append({ + "id": rid, + "format_errors": val_result["format_errors"], + "content_errors": val_result["content_errors"], + }) + + return aligned, report + + +def sanitize_node_for_relation_calc(node: dict) -> dict: + """ + 写入 nodes.json 前对节点做最终净化，确保 f_relation1125/utils 中参与运算的字段无 None。 + 避免 euclidean、get_security_compatibility、get_interaction_history 等出现 NoneType 运算错误。 + """ + import copy + n = copy.deepcopy(node) + + def _safe_float(v: Any, default: float) -> float: + if v is None or (isinstance(v, float) and v != v): + return default + try: + return float(v) + except (TypeError, ValueError): + return default + + # spatial.position 必须为 [float, float] + sp = n.get("spatial") + if not isinstance(sp, dict): + sp = {} + pos = sp.get("position") + if not isinstance(pos, (list, tuple)) or len(pos) < 2: + pos = [0.0, 0.0] + else: + pos = [_safe_float(pos[0], 0.0), _safe_float(pos[1], 0.0)] + sp["position"] = pos + sp["effective_radius"] = _safe_float(sp.get("effective_radius"), 1.0) + n["spatial"] = sp + + # temporal 参与 time_window_overlap + tmp = n.get("temporal") + if not isinstance(tmp, dict): + tmp = {} + for k in ["response_time", "cycle_time", "data_age", "time_window"]: + if tmp.get(k) is None: + tmp[k] = 24.0 if k == "time_window" else 1.0 + n["temporal"] = tmp + + # security_level、history_success 参与 utils 运算 + sl = n.get("security_level") + n["security_level"] = int(_safe_float(sl, 3)) if sl is not None else 3 + if n["security_level"] < 1 or n["security_level"] > 5: + n["security_level"] = 3 + + hs = n.get("history_success") + n["history_success"] = _safe_float(hs, 0.8) + n["history_success"] = max(0.0, min(1.0, n["history_success"])) + + return n diff --git a/src/cleaner.py b/final/src/cleaner.py similarity index 70% rename from src/cleaner.py rename to final/src/cleaner.py index ad0d553..bd7867d 100644 --- a/src/cleaner.py +++ b/final/src/cleaner.py @@ -2,6 +2,10 @@ import json import numpy as np import pandas as pd from datetime import datetime +try: + from .attribute_schema import align_and_validate_records, sanitize_node_for_relation_calc +except ImportError: + from attribute_schema import align_and_validate_records, sanitize_node_for_relation_calc def _json_serializer(obj): @@ -45,7 +49,16 @@ class AdvancedDataCleaner: "fields_processed": ["COMMUNICATION_RANGE"], "total_smoothed": 0 }, - "standardization": [] + "standardization": [], + "attribute_alignment": { # 内容与格式双重对齐（基于 1125 属性需求） + "enabled": True, + "format_errors_by_field": {}, + "content_errors_by_field": {}, + "records_with_errors": 0, + "aligned_count": 0, + "transformed_count": 0, + "samples": [] + } } } @@ -141,6 +154,22 @@ class AdvancedDataCleaner: self.df["COMMUNICATION_RANGE"] = np.round(smoothed, 2) self.report["details"]["noise_reduction"]["total_smoothed"] = len(vals) + def align_attribute_schema(self): + """内容与格式双重对齐：按 1125 属性需求转换并校验""" + if self.df is None or len(self.df) == 0: + return + records = self.df.to_dict('records') + aligned, align_report = align_and_validate_records(records, id_key="TARGET_ID") + self.df = pd.DataFrame(aligned) + self.report["details"]["attribute_alignment"].update({ + "format_errors_by_field": align_report.get("format_errors_by_field", {}), + "content_errors_by_field": align_report.get("content_errors_by_field", {}), + "records_with_errors": align_report.get("records_with_errors", 0), + "aligned_count": align_report.get("aligned_count", 0), + "transformed_count": align_report.get("transformed_count", 0), + "samples": align_report.get("samples", []), + }) + def run(self): print("正在执行高级清洗...") self.load_data() @@ -148,18 +177,39 @@ class AdvancedDataCleaner: self.handle_missing_values() self.correct_outliers() self.apply_kalman_filter() - + self.align_attribute_schema() + # 最终统计 self.report["summary"]["final_records"] = len(self.df) self.report["details"]["standardization"].append("Coordinates normalized to 2 decimal places") self.report["details"]["standardization"].append("Timestamps formatted to ISO-8601") + self.report["details"]["standardization"].append("Attributes aligned to 1125 schema (format + content)") # 保存数据（确保输出目录存在） import os output_dir = os.path.dirname(self.output_file) if output_dir: os.makedirs(output_dir, exist_ok=True) - result_data = self.df.to_dict('records') + records = self.df.to_dict('records') + # 输出格式兼容 main_227 / f_relation1125：{nodes: {TARGET_ID: obj}, global_params: {mtbf_max}} + nodes_dict = {} + mtbf_values = [] + id_col = 'TARGET_ID' + for r in records: + nid = r.get(id_col) + if nid is not None: + nid = str(nid).strip() + if nid: + nodes_dict[nid] = sanitize_node_for_relation_calc(r) + perf = r.get('performance') if isinstance(r.get('performance'), dict) else {} + mtbf = perf.get('mtbf') + if mtbf is not None and (isinstance(mtbf, (int, float)) and not (isinstance(mtbf, float) and mtbf != mtbf)): + mtbf_values.append(float(mtbf)) + mtbf_max = max(mtbf_values, default=1.0) if mtbf_values else 1.0 + result_data = { + "nodes": nodes_dict, + "global_params": {"mtbf_max": round(mtbf_max, 6)}, + } with open(self.output_file, 'w', encoding='utf-8') as f: json.dump(result_data, f, ensure_ascii=False, indent=2, default=_json_serializer) @@ -172,6 +222,25 @@ class AdvancedDataCleaner: print(f"完成！报告已生成至 {self.report_file}") + def get_valid_target_ids(self) -> set: + """返回清洗后的有效 TARGET_ID 集合（用于三元组 filter_orphans）""" + if self.df is None or 'TARGET_ID' not in self.df.columns: + return set() + return set(self.df['TARGET_ID'].dropna().astype(str).str.strip().unique()) + + def get_id_to_role(self) -> dict: + """返回 TARGET_ID -> ROLE_ID 映射（用于三元组 head_type/tail_type）""" + if hasattr(self, '_id_to_role'): + return self._id_to_role + if self.df is None or 'TARGET_ID' not in self.df.columns or 'ROLE_ID' not in self.df.columns: + return {} + return dict( + zip( + self.df['TARGET_ID'].dropna().astype(str).str.strip(), + self.df['ROLE_ID'].fillna('').astype(str).str.strip(), + ) + ) + def load_config(project_root: str) -> dict: """加载 config/config.json 或 config.yaml（json 优先，便于部署时只改 json）""" import os @@ -192,7 +261,7 @@ def load_config(project_root: str) -> dict: 'source': 'json', 'json': { 'input_file': 'data/raw_data_sample.json', - 'output_file': 'data/cleaned_data_final.json', + 'output_file': 'data/nodes.json', 'report_file': 'report/detailed_cleaning_report.json', }, } diff --git a/final/src/data_loader.py b/final/src/data_loader.py new file mode 100644 index 0000000..13cff42 --- /dev/null +++ b/final/src/data_loader.py @@ -0,0 +1,614 @@ +""" +数据加载模块：支持从 JSON 文件或达梦数据库读取数据 +开发阶段使用 JSON，生产环境切换到达梦时只需修改 config 中的 source +""" +import json +import os +import uuid +from typing import Any, Dict, List, Optional, Tuple + +try: + from attribute_schema import FUNCTION_VECTOR_KEYS, raw_to_target_schema +except ImportError: + from src.attribute_schema import FUNCTION_VECTOR_KEYS, raw_to_target_schema + + +def load_from_json(file_path: str, project_root: str = None) -> list: + """从 JSON 文件加载数据""" + path = file_path + if project_root and not os.path.isabs(file_path): + path = os.path.join(project_root, file_path) + with open(path, 'r', encoding='utf-8') as f: + return json.load(f) + + +def _dm_connect(config: dict): + """创建达梦连接，返回 (conn, db_cfg)。需环境变量 DM_PASS。""" + try: + import dmPython + except ImportError as e: + raise RuntimeError( + "达梦数据库模式需要安装 dmPython。\n" + "请执行: pip install dmPython\n" + "并在有达梦客户端的环境运行（需配置 DM_HOME/LD_LIBRARY_PATH）" + ) from e + + db_cfg = config.get('db', {}) + host = os.getenv('DM_HOST', db_cfg.get('host', '127.0.0.1')) + port = int(os.getenv('DM_PORT', str(db_cfg.get('port', 9080)))) + user = os.getenv('DM_USER', db_cfg.get('user', 'SYSDBA')) + password = os.getenv('DM_PASS') + if not password: + raise ValueError( + "达梦模式需要设置环境变量 DM_PASS。\n" + "示例: export DM_PASS=你的密码" + ) + + conn = dmPython.connect( + user=user, + password=password, + server=host, + port=port, + schema=db_cfg.get('schema', 'SYSDBA'), + ) + return conn, db_cfg + + +def load_from_dm(config: dict, project_root: str = None) -> list: + """ + 从达梦数据库加载数据 + 需要安装 dmPython 及达梦客户端库，仅在 source=db 时调用 + """ + conn, db_cfg = _dm_connect(config) + + try: + cursor = conn.cursor() + query = (db_cfg.get('query') or '').strip() + table = db_cfg.get('table') + if query: + cursor.execute(query) + elif table: + schema = db_cfg.get('schema', 'SYSDBA') + cursor.execute(f'SELECT * FROM "{schema}"."{table}"') + else: + raise ValueError( + "达梦模式需配置 db.query（自定义 SQL）或 db.table。\n" + "当前达梦为规范化表，需通过 query 编写 JOIN 得到扁平化数据。" + ) + columns = [desc[0] for desc in cursor.description] + rows = cursor.fetchall() + cursor.close() + return [dict(zip(columns, row)) for row in rows] + finally: + conn.close() + + +def load_data(config: dict, project_root: str) -> Tuple[list, dict]: + """ + 根据配置加载数据，返回 (数据列表, 路径配置) + """ + source = config.get('source', 'json') + project_root = project_root or os.getcwd() + + if source == 'json': + json_cfg = config.get('json', {}) + data = load_from_json(json_cfg.get('input_file', 'data/raw_data_sample.json'), project_root) + paths = { + 'output': _resolve_path(json_cfg.get('output_file', 'data/nodes.json'), project_root), + 'report': _resolve_path(json_cfg.get('report_file', 'report/detailed_cleaning_report.json'), project_root), + } + return data, paths + elif source == 'db': + data = load_from_dm(config, project_root) + json_cfg = config.get('json', {}) + paths = { + 'output': _resolve_path(json_cfg.get('output_file', 'data/nodes.json'), project_root), + 'report': _resolve_path(json_cfg.get('report_file', 'report/detailed_cleaning_report.json'), project_root), + } + return data, paths + else: + raise ValueError(f"不支持的 source 类型: {source}，应为 json 或 db") + + +def load_triplets_from_dm(config: dict, project_root: str = None) -> list: + """ + 从达梦数据库读取三元组数据（RELATION_INSTANCE 表） + 返回格式: [{"HEAD_ID": x, "RELATION_TYPE": y, "TAIL_ID": z, ...}, ...] + """ + conn, db_cfg = _dm_connect(config) + + try: + cursor = conn.cursor() + schema = db_cfg.get('schema', 'SYSDBA') + triplet_table = db_cfg.get('triplet_table', 'RELATION_INSTANCE') + query = db_cfg.get('triplet_query') + if query and query.strip(): + cursor.execute(query.strip()) + else: + cursor.execute(f'SELECT * FROM "{schema}"."{triplet_table}"') + columns = [desc[0] for desc in cursor.description] + rows = cursor.fetchall() + cursor.close() + return [dict(zip(columns, row)) for row in rows] + finally: + conn.close() + + +def _ci_key_map(d: dict) -> Dict[str, str]: + return {str(k).upper(): k for k in d.keys()} + + +def _get_ci(d: dict, key: str) -> Any: + k = _ci_key_map(d).get(str(key).upper()) + return d.get(k) if k else None + + +def _set_ci(d: dict, key: str, value: Any) -> None: + um = _ci_key_map(d) + k = um.get(str(key).upper()) + if k is None: + d[key] = value + else: + d[k] = value + + +def _table_columns(cursor, schema: str, table: str) -> List[str]: + cursor.execute(f'SELECT * FROM "{schema}"."{table}" WHERE 1=0') + return [desc[0] for desc in cursor.description] + + +def _serialize_metric_value(val: Any) -> str: + if val is None: + return "" + if isinstance(val, (dict, list)): + return json.dumps(val, ensure_ascii=False) + return str(val) + + +def _value_from_aligned_node(node: dict, attr: str) -> Any: + """从 raw_to_target_schema 输出中取 required_attrs 对应值。""" + if attr in FUNCTION_VECTOR_KEYS: + return (node.get("function_vector") or {}).get(attr) + if attr in ("position", "effective_radius"): + return (node.get("spatial") or {}).get(attr) + if attr in ("response_time", "cycle_time", "data_age", "time_window"): + return (node.get("temporal") or {}).get(attr) + if attr in ("core_performance", "survivability", "mtbf"): + return (node.get("performance") or {}).get(attr) + if attr in ("protocol_list", "format_list", "interface_list"): + return node.get(attr) + if attr in ("security_level", "org_unit", "nation", "history_success"): + return node.get(attr) + return None + + +def _fetch_reasoned_triples(cursor, config: dict, formal: dict) -> List[dict]: + db_cfg = config.get("db", {}) + schema = db_cfg.get("schema", "SYSDBA") + user_id = formal.get("user_id", "ADMIN") + task_id = formal.get("task_id", "TASK_FULL_KB") + source_sys_type = formal.get("source_sys_type", "关系推理后的体系") + raw_query = (formal.get("source_query") or "").strip() + if raw_query: + cursor.execute(raw_query) + else: + table = formal.get("triplet_table") or db_cfg.get("triplet_table", "RELATION_INSTANCE") + uc = formal.get("user_column", "USER_ID") + tc = formal.get("task_column", "TASK_ID") + sc = formal.get("sys_type_column", "SYS_TYPE") + sql = ( + f'SELECT * FROM "{schema}"."{table}" ' + f'WHERE "{uc}" = ? AND "{tc}" = ? AND "{sc}" = ?' + ) + cursor.execute(sql, [user_id, task_id, source_sys_type]) + columns = [desc[0] for desc in cursor.description] + rows = cursor.fetchall() + return [dict(zip(columns, row)) for row in rows] + + +def _validate_source_sys_type(rows: List[dict], formal: dict) -> Optional[str]: + """若存在与 source_sys_type 不一致的 SYS_TYPE，返回错误信息（硬失败）。""" + if formal.get("skip_sys_type_validation"): + return None + sc = formal.get("sys_type_column", "SYS_TYPE") + expected = formal.get("source_sys_type", "关系推理后的体系") + if not rows: + return None + vals = [_get_ci(r, sc) for r in rows] + if all(v is None for v in vals): + # 结果集中无 SYS_TYPE 列（例如仅用 source_query 投影），由 SQL 保证类型 + return None + bad_indices = [] + for i, v in enumerate(vals): + if v is None or str(v).strip() != str(expected).strip(): + bad_indices.append(i) + if bad_indices: + return f"sys_type_mismatch: expected={expected!r}, bad_row_indices={bad_indices[:20]}" + return None + + +def _entity_ids_from_triples(triples: List[dict]) -> List[str]: + ids = set() + for t in triples: + h = _get_ci(t, "HEAD_ID") or t.get("head") + tail = _get_ci(t, "TAIL_ID") or t.get("tail") + if h is not None and str(h).strip(): + ids.add(str(h).strip()) + if tail is not None and str(tail).strip(): + ids.add(str(tail).strip()) + return sorted(ids) + + +def _load_objectives_by_target( + cursor, schema: str, table: str, entity_col: str, target_ids: List[str], +) -> Dict[str, dict]: + if not target_ids: + return {} + cols = _table_columns(cursor, schema, table) + ec = None + for c in cols: + if c.upper() == entity_col.upper(): + ec = c + break + if not ec: + return {} + placeholders = ",".join(["?"] * len(target_ids)) + sql = f'SELECT * FROM "{schema}"."{table}" WHERE "{ec}" IN ({placeholders})' + cursor.execute(sql, target_ids) + out = {} + row_cols = [desc[0] for desc in cursor.description] + for row in cursor.fetchall(): + d = dict(zip(row_cols, row)) + tid = _get_ci(d, entity_col) + if tid is not None: + out[str(tid).strip()] = d + return out + + +def _load_existing_metric_keys( + cursor, schema: str, attr_table: str, entity_col: str, + metric_col: str, target_ids: List[str], +) -> Dict[str, set]: + """target_id -> set(METRIC_ID) 已有记录。""" + if not target_ids: + return {} + tcols = _table_columns(cursor, schema, attr_table) + ec = next((c for c in tcols if c.upper() == entity_col.upper()), None) + mc = next((c for c in tcols if c.upper() == metric_col.upper()), None) + if not ec or not mc: + return {} + placeholders = ",".join(["?"] * len(target_ids)) + sql = ( + f'SELECT "{ec}", "{mc}" FROM "{schema}"."{attr_table}" ' + f'WHERE "{ec}" IN ({placeholders})' + ) + cursor.execute(sql, target_ids) + out: Dict[str, set] = {} + for row in cursor.fetchall(): + tid, mid = row[0], row[1] + if tid is None or mid is None: + continue + tid_s = str(tid).strip() + out.setdefault(tid_s, set()).add(str(mid).strip()) + return out + + +def _insert_generated_metrics( + cursor, + schema: str, + attr_table: str, + table_cols: List[str], + entity_col: str, + metric_col: str, + value_col: str, + target_id: str, + metric_id: str, + value_str: str, + static_extra: Optional[dict], +) -> bool: + """插入一条指标行；仅写入表中存在的列。未写入任何列时返回 False。""" + um = {c.upper(): c for c in table_cols} + row: Dict[str, Any] = {} + if entity_col.upper() in um: + row[um[entity_col.upper()]] = target_id + if metric_col.upper() in um: + row[um[metric_col.upper()]] = metric_id + if value_col.upper() in um: + row[um[value_col.upper()]] = value_str + if static_extra: + for k, v in static_extra.items(): + ku = str(k).upper() + if ku in um: + row[um[ku]] = v + cols = list(row.keys()) + if entity_col.upper() not in um or metric_col.upper() not in um: + return False + if not cols: + return False + quoted = ",".join(f'"{c}"' for c in cols) + ph = ",".join(["?"] * len(cols)) + sql = f'INSERT INTO "{schema}"."{attr_table}" ({quoted}) VALUES ({ph})' + cursor.execute(sql, [row[c] for c in cols]) + return True + + +def _build_formal_triple_rows( + source_rows: List[dict], + formal: dict, + batch_id: str, + result_table_cols: List[str], +) -> Tuple[List[dict], int]: + """复制源行，写入 target_sys_type 与 batch_id；丢弃无效核字段。""" + sys_col = formal.get("sys_type_column", "SYS_TYPE") + target_sys = formal.get("target_sys_type", "形式化后的体系") + pk = formal.get("triplet_primary_key") or formal.get("triplet_pk") + omit_pk = formal.get("triplet_omit_primary_key_on_insert", True) + batch_col = formal.get("triple_batch_column", "BATCH_ID") + colset_upper = {c.upper() for c in result_table_cols} + out_rows = [] + skipped = 0 + for r in source_rows: + h = _get_ci(r, "HEAD_ID") or r.get("head") + tail = _get_ci(r, "TAIL_ID") or r.get("tail") + rel = _get_ci(r, "RELATION_TYPE") or r.get("relation") + if not h or not tail or not rel: + skipped += 1 + continue + new_r = dict(r) + _set_ci(new_r, sys_col, target_sys) + if batch_col.upper() in colset_upper: + _set_ci(new_r, batch_col, batch_id) + if omit_pk and pk: + kmap = _ci_key_map(new_r) + pk_actual = kmap.get(str(pk).upper()) + if pk_actual and pk_actual in new_r: + del new_r[pk_actual] + filtered = {} + for k, v in new_r.items(): + if k in result_table_cols: + filtered[k] = v + out_rows.append(filtered) + return out_rows, skipped + + +def _insert_rows_executemany( + cursor, schema: str, table: str, rows: List[dict], +) -> int: + if not rows: + return 0 + all_keys = [] + for r in rows: + for k in r: + if k not in all_keys: + all_keys.append(k) + quoted = ",".join(f'"{k}"' for k in all_keys) + ph = ",".join(["?"] * len(all_keys)) + sql = f'INSERT INTO "{schema}"."{table}" ({quoted}) VALUES ({ph})' + params = [tuple(r.get(k) for k in all_keys) for r in rows] + cursor.executemany(sql, params) + return len(rows) + + +def _insert_res_formal_analysis( + cursor, + schema: str, + analysis_table: str, + batch_id: str, + formal: dict, + stats: dict, + detail_obj: dict, +) -> bool: + cols = _table_columns(cursor, schema, analysis_table) + cu = {c.upper(): c for c in cols} + row: Dict[str, Any] = {} + mapping = [ + ("BATCH_ID", batch_id), + ("USER_ID", formal.get("user_id", "ADMIN")), + ("TASK_ID", formal.get("task_id", "TASK_FULL_KB")), + ("SOURCE_SYS_TYPE", formal.get("source_sys_type", "")), + ("TARGET_SYS_TYPE", formal.get("target_sys_type", "")), + ] + for key, val in mapping: + if key in cu: + row[cu[key]] = val + detail_json = json.dumps(detail_obj, ensure_ascii=False) + detail_candidates = ( + "DETAIL_JSON", + "DETAIL", + "ANALYSIS_JSON", + "CONTENT", + "RULE_DETAIL", + "STATS_JSON", + "RES_FORMAL_ANALYSIS", + ) + detail_key_col = None + for cand in detail_candidates: + if cand in cu: + detail_key_col = cu[cand] + break + if not detail_key_col: + for c in cols: + u = c.upper() + if any( + token in u + for token in ( + "DETAIL", + "JSON", + "CONTENT", + "RULE", + "TEXT", + "MEMO", + "DESC", + "REMARK", + "INFO", + "ANALYSIS", + ) + ): + detail_key_col = c + break + if detail_key_col: + row[detail_key_col] = detail_json + static = formal.get("analysis_static_columns") or formal.get("analysis_static") or {} + for k, v in static.items(): + if str(k).upper() in cu: + row[cu[str(k).upper()]] = v + insert_cols = formal.get("analysis_insert_columns") + if insert_cols: + use_cols = [c for c in insert_cols if c in cols] + if not use_cols: + return False + vals = [row.get(c) for c in use_cols] + ph = ",".join(["?"] * len(use_cols)) + qc = ",".join(f'"{c}"' for c in use_cols) + sql = f'INSERT INTO "{schema}"."{analysis_table}" ({qc}) VALUES ({ph})' + cursor.execute(sql, vals) + return True + if not row: + return False + quoted = ",".join(f'"{c}"' for c in row.keys()) + ph = ",".join(["?"] * len(row)) + sql = f'INSERT INTO "{schema}"."{analysis_table}" ({quoted}) VALUES ({ph})' + cursor.execute(sql, list(row.values())) + return True + + +def run_formalization_pipeline(config: dict) -> dict: + """ + 模块 1.3：读取「关系推理后的体系」三元组，补齐实体属性并写回， + 将形式化结果作为新记录写入 formal_result_table，并写入 RES_FORMAL_ANALYSIS。 + 需在 config 中 formal.enabled=true 且 source=db；达梦密码 DM_PASS。 + """ + formal = config.get("formal") or {} + if not formal.get("enabled", False): + return {"ok": False, "reason": "formal.disabled", "batch_id": None, "stats": {}} + if config.get("source", "json") != "db": + return {"ok": False, "reason": "source_not_db", "batch_id": None, "stats": {}} + + batch_id = str(uuid.uuid4()) + stats: Dict[str, Any] = { + "total_triples": 0, + "inserted_formal_count": 0, + "generated_attrs_count": 0, + "skipped_triples": 0, + "analysis_written": False, + } + + conn = None + try: + conn, db_cfg = _dm_connect(config) + except (RuntimeError, ValueError) as e: + return {"ok": False, "reason": str(e), "batch_id": None, "stats": stats} + + schema = db_cfg.get("schema", "SYSDBA") + user_id = formal.get("user_id", "ADMIN") + task_id = formal.get("task_id", "TASK_FULL_KB") + source_sys_type = formal.get("source_sys_type", "关系推理后的体系") + target_sys_type = formal.get("target_sys_type", "形式化后的体系") + attr_table = formal.get("attribute_table", "METRIC_VALUE_INSTANCE") + result_table = formal.get("formal_result_table", "RELATION_INSTANCE") + analysis_table = formal.get("analysis_table", "RES_FORMAL_ANALYSIS") + entity_col = formal.get("entity_id_column", "TARGET_ID") + metric_col = formal.get("metric_id_column", "METRIC_ID") + value_col = formal.get("metric_value_column", "METRIC_VALUE") + objective_table = formal.get("objective_table") or db_cfg.get("table", "OBJECTIVE_INSTANCE") + required_attrs: List[str] = list(formal.get("required_attrs") or []) + + try: + if hasattr(conn, "autocommit"): + conn.autocommit = False + cursor = conn.cursor() + + triples = _fetch_reasoned_triples(cursor, config, formal) + stats["total_triples"] = len(triples) + err = _validate_source_sys_type(triples, formal) + if err: + conn.rollback() + return {"ok": False, "reason": err, "batch_id": batch_id, "stats": stats} + + eids = _entity_ids_from_triples(triples) + objectives = _load_objectives_by_target( + cursor, schema, objective_table, entity_col, eids, + ) + existing_metrics = _load_existing_metric_keys( + cursor, schema, attr_table, entity_col, metric_col, eids, + ) + mvi_cols = _table_columns(cursor, schema, attr_table) + static_metric = formal.get("metric_insert_static") or {} + + for tid in eids: + raw = dict(objectives.get(tid, {})) + raw["TARGET_ID"] = tid + aligned = raw_to_target_schema(raw) + have = existing_metrics.get(tid, set()) + for attr in required_attrs: + if attr in have: + continue + val = _value_from_aligned_node(aligned, attr) + vstr = _serialize_metric_value(val) + if _insert_generated_metrics( + cursor, + schema, + attr_table, + mvi_cols, + entity_col, + metric_col, + value_col, + tid, + attr, + vstr, + static_metric, + ): + stats["generated_attrs_count"] += 1 + have.add(attr) + + result_cols = _table_columns(cursor, schema, result_table) + formal_rows, skipped = _build_formal_triple_rows( + triples, formal, batch_id, result_cols, + ) + stats["skipped_triples"] = skipped + if formal_rows: + _insert_rows_executemany(cursor, schema, result_table, formal_rows) + stats["inserted_formal_count"] = len(formal_rows) + + detail = { + "batch_id": batch_id, + "user_id": user_id, + "task_id": task_id, + "source_sys_type": source_sys_type, + "target_sys_type": target_sys_type, + "stats": stats, + "rules": formal.get("rules_summary") + or "结构校验+属性对齐(raw_to_target_schema)+sys_type切换为形式化后的体系", + } + stats["analysis_written"] = _insert_res_formal_analysis( + cursor, schema, analysis_table, batch_id, formal, stats, detail, + ) + if not stats["analysis_written"]: + raise RuntimeError( + "RES_FORMAL_ANALYSIS 未写入：表无可匹配列。请配置 formal.analysis_insert_columns " + "或在表中提供 BATCH_ID/USER_ID/TASK_ID 及 DETAIL/JSON 类文本列。" + ) + + conn.commit() + cursor.close() + return {"ok": True, "reason": None, "batch_id": batch_id, "stats": stats} + except Exception as e: + if conn is not None: + try: + conn.rollback() + except Exception: + pass + stats.setdefault("error", str(e)) + return {"ok": False, "reason": str(e), "batch_id": batch_id, "stats": stats} + finally: + if conn is not None: + try: + conn.close() + except Exception: + pass + + +def _resolve_path(path: str, project_root: str) -> str: + if os.path.isabs(path): + return path + return os.path.join(project_root, path) diff --git a/final/src/main_227.py b/final/src/main_227.py new file mode 100644 index 0000000..13644fc --- /dev/null +++ b/final/src/main_227.py @@ -0,0 +1,666 @@ +# main.py +import numpy as np +import json, copy +import pandas as pd +from typing import Dict, List +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from scripts.f_relation1125 import RelationCalculator +from scripts.f_net1125 import TaskNetworkEvaluator + +# ========== 新增：导入 cleaner ========== +sys.path.append(str(Path(__file__).parent)) # 添加 src 目录到路径 +from cleaner import load_config, AdvancedDataCleaner +from data_loader import load_data as load_data_source, run_formalization_pipeline + +# -------------- 默认配置（与原来硬编码保持一致） -------------- +DEFAULT_CFG = { + "W_REL": { # 5 类关系权重 + "IS": {"w_f": 0.35, "w_s": 0.25, "w_t": 0.15, "w_p": 0.15, "w_i": 0.10}, + "CC": {"w_f": 0.30, "w_s": 0.20, "w_t": 0.20, "w_p": 0.20, "w_i": 0.10}, + "SF": {"w_f": 0.30, "w_s": 0.20, "w_t": 0.25, "w_p": 0.15, "w_i": 0.10}, + "PD": {"w_f": 0.25, "w_s": 0.35, "w_t": 0.15, "w_p": 0.15, "w_i": 0.10}, + "CO": {"w_f": 0.30, "w_s": 0.20, "w_t": 0.20, "w_p": 0.20, "w_i": 0.10}, + }, + "W_NET": { # 5 类任务网络权重 + "defense": {"w1": 0.30, "w2": 0.25, "w3": 0.20, "w4": 0.15, "w5": 0.10}, + "fire": {"w1": 0.35, "w2": 0.25, "w3": 0.20, "w4": 0.10, "w5": 0.10}, + "logistics":{"w1": 0.30, "w2": 0.25, "w3": 0.20, "w4": 0.15, "w5": 0.10}, + "medical": {"w1": 0.35, "w2": 0.25, "w3": 0.20, "w4": 0.10, "w5": 0.10}, + "evacuation":{"w1":0.30, "w2": 0.25, "w3": 0.20, "w4": 0.15, "w5": 0.10}, + }, + "LAMBDA": {"IS": 100, "CC": 50, "SF": 80, "PD": 30, "CO": 60}, + "TIME": {"alpha": 0.5, "beta": 0.6, "gamma": 0.4}, + "PD_SGM": 20, + "THRESHOLD": 0.15 # 关系过滤阈值 +} + + +def var_desc(): + return { + # 共用 + "dij": "节点 i 与 j 之间的欧氏距离", + "Ri": "节点 i 的有效作用半径", + "Rmatch(i,j)": "两节点作用范围重叠度", + "s_area": "作战区域关联度（同区 1.0 / 邻区 0.7 / 异区 0.3）", + "stdij": "接口标准兼容性（0-1）", + "rij": "任务角色匹配度（0-1）", + "t_resp": "节点响应时间得分 exp(-α·T_resp)", + "t_cycle": "节点处理周期得分 exp(-β·T_cycle)", + "fresh_i": "数据新鲜度 exp(-γ·数据年龄)", + "wij": "时间窗口重叠比例", + "p_core": "节点核心性能", + "p_rel": "节点可靠性 MTBF/MTBF_max", + "p_surv": "节点生存性", + "protij": "协议兼容性", + "fmtij": "数据格式兼容性", + "secij": "安全等级兼容性", + "orgij": "组织隶属关联度", + "histij": "历史交互成功率", + # 关系专用 + "M_F^IS": "情报保障功能匹配项", + "M_S^IS": "情报保障空间匹配项", + "M_T^IS": "情报保障时间匹配项", + "M_P^IS": "情报保障性能匹配项", + "M_I^IS": "情报保障交互匹配项", + "λ_IS": "情报特征距离（λ_IS=100 km）", + # 网络专用 + "N_IC": "信息对抗节点数量", + "∑f_IC/N_IC": "信息对抗节点平均能力", + "∑L_IS/N_IS": "情报保障关系平均强度", + "∑t_resp/N": "系统平均响应速度", + "∑L_CC/N_CC": "指挥控制关系平均强度", + "∑f_CS/N_CS": "协同打击节点平均能力", + "∑f_IA/N_IA": "信息获取节点平均能力", + "∑L_CO/N_CO": "协同作战关系平均强度", + "∑p_core·f_CS/N_CS": "打击节点性能加权能力", + "∑f_CPS/N_CPS": "综合保障节点平均能力", + "∑f_DP/N_DP": "部署平台节点平均能力", + "∑L_PD/N_PD": "平台部署关系平均强度", + "∑p_rel/N": "系统平均可靠性", + "∑f_IT/N_IT": "信息传输节点平均能力", + "∑L_SF/N_SF": "状态反馈关系平均强度", + "∑fresh_i/N": "系统平均数据新鲜度", + "α1...α5": "综合防御权重 [0.30,0.25,0.20,0.15,0.10]", + "β1...β5": "火力打击权重 [0.35,0.25,0.20,0.10,0.10]", + "γ1...γ5": "后勤保障权重 [0.30,0.25,0.20,0.15,0.10]", + "δ1...δ5": "医疗救援权重 [0.35,0.25,0.20,0.10,0.10]", + "ε1...ε5": "紧急疏散权重 [0.30,0.25,0.20,0.15,0.10]", + } + + +def load_data(project_root=None): + """加载节点和三联数据，路径从配置读取（兼容 run_all 输出）""" + if project_root is None: + project_root = Path(__file__).resolve().parent.parent + project_root = Path(project_root) + cfg = load_config(str(project_root)) + eval_cfg = cfg.get("eval", {}) + json_cfg = cfg.get("json", {}) + triplet_cfg = cfg.get("triplet", {}) + + # 节点文件：优先 eval.nodes_file，否则 json.output_file + nodes_file = eval_cfg.get("nodes_file") or json_cfg.get("output_file", "data/nodes.json") + nodes_path = project_root / nodes_file if not Path(nodes_file).is_absolute() else Path(nodes_file) + with open(nodes_path, 'r', encoding='utf-8') as f: + nodes_data = json.load(f) + + # 三元组文件：eval.triples_file，不存在则用 triplet.output_file + triples_file = eval_cfg.get("triples_file", "data/triples.json") + triples_path = project_root / triples_file if not Path(triples_file).is_absolute() else Path(triples_file) + if not triples_path.exists(): + triples_path = project_root / triplet_cfg.get("output_file", "data/triples.json") + with open(triples_path, 'r', encoding='utf-8') as f: + triples_raw = json.load(f) + # 兼容 list 格式（run_all 输出的 triples.json） + triples_data = triples_raw if isinstance(triples_raw, dict) and "triples" in triples_raw else {"triples": triples_raw if isinstance(triples_raw, list) else []} + + return nodes_data, triples_data + + +def calculate_and_filter_relations(nodes_data: Dict, triples_data: Dict, threshold, cfg): + """ + 计算所有三联关系的关系强度，并过滤强度大于阈值的边 + + 返回: + - filtered_relations: 过滤后的关系字典 + - filtered_triples: 过滤后的三元组列表（保持原始结构） + - relation_stats: 关系统计信息 + """ + print("开始计算关系强度并过滤...") + + # 初始化关系计算器 + + calculator = RelationCalculator(nodes_data, cfg) # 改造见下 + # 关系类型映射（中文 -> 英文缩写） + relation_mapping = { + "情报保障": "IS", + "指挥控制": "CC", + "状态反馈": "SF", + "平台部署": "PD", + "协同作战": "CO" + } + + all_relations = {} + filtered_relations = {} + filtered_triples = [] + + valid_triples = 0 + skipped_triples = 0 + passed_triples = 0 + + for triple in triples_data["triples"]: + head = triple["head"] + tail = triple["tail"] + relation_ch = triple["relation"] + + # 检查节点是否存在 + if head not in nodes_data["nodes"] or tail not in nodes_data["nodes"]: + print(f"警告: 节点不存在 - 头: {head}, 尾: {tail}，跳过此三元组") + skipped_triples += 1 + continue + + # 映射关系类型 + relation_en = relation_mapping.get(relation_ch) + if not relation_en: + print(f"警告: 未知关系类型 '{relation_ch}'，跳过此三元组") + skipped_triples += 1 + continue + + try: + # 根据关系类型调用对应的计算方法 + if relation_en == "IS": + strength = calculator.calculate_IS_relation(head, tail) + elif relation_en == "CC": + strength = calculator.calculate_CC_relation(head, tail) + elif relation_en == "SF": + strength = calculator.calculate_SF_relation(head, tail) + elif relation_en == "PD": + strength = calculator.calculate_PD_relation(head, tail) + elif relation_en == "CO": + strength = calculator.calculate_CO_relation(head, tail) + else: + print(f"警告: 未实现的关系类型 '{relation_en}'，跳过此三元组") + skipped_triples += 1 + continue + + # 存储所有关系强度（用于统计） + all_relations[(relation_en, head, tail)] = strength + valid_triples += 1 + + # 过滤：只保留强度大于阈值的关系 + if strength > threshold: + filtered_relations[(relation_en, head, tail)] = strength + # 创建过滤后的三元组（保持原始结构，添加强度字段） + filtered_triple = triple.copy() + filtered_triple["strength"] = float(strength) # 转换为Python float类型 + filtered_triples.append(filtered_triple) + passed_triples += 1 + + if valid_triples % 10 == 0: # 每10个关系打印一次进度 + print(f"已计算 {valid_triples} 个关系，通过 {passed_triples} 个...") + + except Exception as e: + print(f"错误: 计算关系失败 - 头: {head}, 尾: {tail}, 关系: {relation_en}, 错误: {e}") + skipped_triples += 1 + # 1. 先按类型细分 + type_detail = {} + for (rel, _, _), val in all_relations.items(): + type_detail.setdefault(rel, {"all": 0, "pass": 0}) + type_detail[rel]["all"] += 1 + # if val > threshold: + # type_detail[rel]["pass"] += 1 + # 新增：按类型收集被过滤的关系 + filtered_out = {} # rel -> [(head, tail, strength), ...] + for (rel, head, tail), val in all_relations.items(): + if val <= threshold: + filtered_out.setdefault(rel, []).append((head, tail, val)) + + # 2. 再打包大字典 + relation_stats = { + "total_triples": len(triples_data["triples"]), + "valid_triples": valid_triples, + "skipped_triples": skipped_triples, + "passed_triples": passed_triples, + "pass_rate": passed_triples / valid_triples if valid_triples else 0, + "threshold": threshold, + "filtered_out":filtered_out, + "type_detail": { + # rel: {"all": d["all"], "pass": d["pass"], "rate": d["pass"] / d["all"] if d["all"] else 0.} + rel: {"all": d["all"]} + for rel, d in type_detail.items() + } + } + + print(f"关系强度计算和过滤完成!") + print(f"总三元组: {relation_stats['total_triples']}, 有效计算: {valid_triples}, 跳过: {skipped_triples}") + print(f"通过过滤(> {threshold}): {passed_triples}, 通过率: {relation_stats['pass_rate']:.1%}") + + return filtered_relations, filtered_triples, relation_stats, all_relations + + +def evaluate_task_networks(nodes_data, relations, cfg): + """ + 评估所有任务网络效能 + """ + print("\n开始评估任务网络效能...") + + # 初始化网络评估器 + evaluator = TaskNetworkEvaluator(nodes_data["nodes"], relations, weights=cfg["W_NET"]) + + # 评估所有任务网络 + results = evaluator.eval_all() + + print("任务网络效能评估完成!") + return results + + +def generate_evaluation_report(relation_stats: Dict, network_results: Dict, + filtered_relations: Dict, all_relations: Dict, + threshold: float, cfg) -> str: + """ + 生成详细的评价报告 + """ + report = [] + report.append("=" * 60) + report.append("网络关系与效能评价报告") + report.append("=" * 60) + report.append("") + + # 1. 基本信息 + report.append("1. 基本信息") + report.append("-" * 40) + report.append(f"过滤阈值: {threshold} 【关系强度阈值】") + report.append(f"总三元组数量: {relation_stats['total_triples']}") + line = " ".join(f"{rel}={d['all']}" for rel, d in relation_stats["type_detail"].items()) + report.append(f"分别有：{line}") + report.append(f"有效计算三元组: {relation_stats['valid_triples']}") + report.append(f"跳过三元组: {relation_stats['skipped_triples']}") + report.append(f"通过过滤三元组: {relation_stats['passed_triples']}") + report.append(f"通过率: {relation_stats['pass_rate']:.1%}") + report.append("") + + # 添加边关系计算公式和说明 + report.append("2. 计算公式") + report.append("边关系计算公式") + report.append("-" * 50) + rel_cn = {"IS": "情报保障", "CC": "指挥控制", "SF": "状态反馈", "PD": "平台部署", "CO": "协同作战"} + desc = var_desc() + for rel in ["IS", "CC", "SF", "PD", "CO"]: + report.append( + f"{rel_cn[rel]}关系 L_{rel}(i,j) = w_f·M_F^{rel} + w_s·M_S^{rel} + w_t·M_T^{rel} + w_p·M_P^{rel} + w_i·M_I^{rel}") + report.append("【符号说明】") + for sym, exp in desc.items(): + if sym.startswith(("M_F^", "M_S^", "M_T^", "M_P^", "M_I^")): + report.append(f" {sym:<12} {exp}") + report.append("") + + + + report.append("任务网络效能计算公式") + report.append("-" * 50) + task_desc = { + "defense": "综合防御 P_defense = α1·(∑f_IC/N_IC) + α2·(∑f_IA/N_IA) + α3·(∑L_IS/N_IS) + α4·(∑t_resp/N) + α5·(∑L_CC/N_CC)", + "fire": "火力打击 P_fire = β1·(∑f_CS/N_CS) + β2·(∑f_IA/N_IA) + β3·(∑L_CO/N_CO) + β4·(∑p_core·f_CS/N_CS) + β5·(∑L_IS/N_IS)", + "logistics": "后勤保障 P_logistics = γ1·(∑f_CPS/N_CPS) + γ2·(∑f_DP/N_DP) + γ3·(∑L_PD/N_PD) + γ4·(∑p_rel/N) + γ5·(∑f_IT/N_IT)", + "medical": "医疗救援 P_medical = δ1·(∑f_CPS/N_CPS) + δ2·(∑t_resp/N) + δ3·(∑f_IT/N_IT) + δ4·(∑L_SF/N_SF) + δ5·(∑fresh_i/N)", + "evacuation": "紧急疏散 P_evacuation = ε1·(∑f_CC/N_CC) + ε2·(∑f_IT/N_IT) + ε3·(∑L_CC/N_CC) + ε4·(∑f_CPS/N_CPS) + ε5·(∑t_resp/N)" + } + for task, form in task_desc.items(): + report.append(form) + report.append("【符号说明】") + for sym, exp in desc.items(): + if any(sym.startswith(x) for x in ["N_", "∑", "α", "β", "γ", "δ", "ε"]): + report.append(f" {sym:<18} {exp}") + report.append("") + + # 2. 按关系类型统计 + report.append("3. 关系类型统计") + report.append("-" * 40) + + relation_type_stats = {} + for (rel_type, head, tail), strength in all_relations.items(): + if rel_type not in relation_type_stats: + relation_type_stats[rel_type] = {"all": [], "filtered": []} + relation_type_stats[rel_type]["all"].append(strength) + + if strength > threshold: + relation_type_stats[rel_type]["filtered"].append(strength) + + for rel_type in sorted(relation_type_stats.keys()): + stats = relation_type_stats[rel_type] + all_count = len(stats["all"]) + filtered_count = len(stats["filtered"]) + pass_rate = filtered_count / all_count if all_count > 0 else 0 + + report.append(f"{rel_type}关系:") + report.append(f" 总数: {all_count}, 通过: {filtered_count}, 通过率: {pass_rate:.1%}") + if stats["all"]: + report.append(f" 平均强度: {np.mean(stats['all']):.3f}") + if stats["filtered"]: + report.append(f" 过滤后平均强度: {np.mean(stats['filtered']):.3f}") + report.append("【被过滤掉的关系明细】") + report.append("-" * 40) + for rel, lst in relation_stats["filtered_out"].items(): + if not lst: + continue + report.append(f"{rel} 关系（强度 ≤ {threshold}）：") + for h, t, v in lst: + report.append(f" {h} → {t} 强度={v:.3f}") + report.append("") # 空行隔开 + + # 1. 取任务得分（排除 dimensions） + task_scores = {task: score for task, score in network_results.items() if isinstance(score, float)} + total = sum(task_scores.values()) or 1.0 # 防 0 + + # 2. 归一化 → 百分比 + task_pct = {task: (sc / total) * 100 for task, sc in task_scores.items()} + + # 3. 输出 + report.append("4. 所属任务网络评估") + report.append("-" * 40) + task_name = { + "defense": "综合防御", + "fire": "火力打击", + "logistics": "后勤保障", + "medical": "医疗救援", + "evacuation": "紧急疏散" + } + # 一行输出所有任务网络百分比 + line = " ".join(f"{task_name[task]} {pct:.1f}%" for task, pct in task_pct.items()) + report.append("任务网络概率：" + line) + # 4. 最可能任务 + max_task = max(task_pct, key=task_pct.get) + report.append(f"最有可能的任务网络：{task_name[max_task]} 可能性为：{task_pct[max_task]:.1f} %") + + # 添加最终网络的五个维度评价 + report.append(f"5. 最终{task_name[max_task]}任务网络的五个维度评价") + dimensions = network_results.get("dimensions", {}) + reference_value = 0.6 # 参考值 + + # 功能维度 + report.append("功能维度 (Function)：") + report.append(f"功能维度反映了网络中节点的功能匹配程度。得分: {dimensions.get('function', 0):.3f}") + if dimensions.get('function', 0) > reference_value: + report.append("高于参考值，表示节点在功能上高度互补，能够有效协同工作。") + else: + report.append("低于参考值，表示节点在功能上匹配程度较低，协同工作能力有待提升。") + + # 边关系维度 + report.append("空间系维度 (Relationship)：") + report.append(f"空间维度反映了节点之间的连接强度。得分: {dimensions.get('relationship', 0):.3f}") + if dimensions.get('relationship', 0) > reference_value: + report.append("高于参考值，表示节点之间的连接紧密，信息传递高效。") + else: + report.append("低于参考值，表示节点之间的连接较弱，信息传递效率较低。") + + # 性能维度 + report.append("性能维度 (Performance)：") + report.append(f"性能维度反映了节点的性能指标，如响应时间和可靠性。得分: {dimensions.get('performance', 0):.3f}") + if dimensions.get('performance', 0) > reference_value: + report.append("高于参考值，表示节点在性能上表现出色，能够稳定运行。") + else: + report.append("低于参考值，表示节点在性能上存在不足，稳定性有待提高。") + + # 时间维度 + report.append("时间维度 (Temporal)：") + report.append(f"时间维度反映了节点的时间同步性和响应能力。得分: {dimensions.get('temporal', 0):.3f}") + if dimensions.get('temporal', 0) > reference_value: + report.append("高于参考值，表示节点在时间上高度同步，能够快速响应。") + else: + report.append("低于参考值，表示节点在时间同步性上存在偏差，响应速度较慢。") + + # 交互维度 + report.append("交互维度 (Interaction)：") + report.append(f"交互维度反映了节点之间的协议、格式和安全兼容性。得分: {dimensions.get('interaction', 0):.3f}") + if dimensions.get('interaction', 0) > reference_value: + report.append("高于参考值，表示节点在交互上高度兼容，能够安全通信。") + else: + report.append("低于参考值，表示节点在交互上存在兼容性问题，通信安全性较低。") + + return "\n".join(report) + + +def save_filtered_triples(filtered_triples: List[Dict], filename: str = "filtered_triples.json"): + """ + 保存过滤后的三元组到JSON文件 + """ + project_root = Path(__file__).parent.parent # src -> 227 + output_path = project_root / "results" / filename + + output_data = {"triples": filtered_triples} + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=2, ensure_ascii=False) + + print(f"过滤后的三元组已保存到 {filename}") + + +def save_triples_and_task_probs(filtered_triples: list, + network_results: dict, + excel_name: str = "triples.xlsx", + csv_name: str = "triples.csv") -> None: + """ + 保存过滤后的三元组（csv + Excel 表1）+ + 归一化任务网络概率（Excel 表2） + """ + # 1. 三元组 DataFrame（含 head_type / tail_type） + triple_df = pd.DataFrame([ + { + "head": t["head"], + "head_type": t.get("head_type", ""), + "relation": t["relation"], + "tail": t["tail"], + "tail_type": t.get("tail_type", ""), + "strength": t["strength"] + } + for t in filtered_triples + ]) + + # 2. 任务网络概率 DataFrame + task_scores = {task: score for task, score in network_results.items() if isinstance(score, float)} + total = sum(task_scores.values()) or 1.0 + task_pct = {task: (sc / total) * 100 for task, sc in task_scores.items()} + + task_map = { + "defense": "1-综合防御任务网络", + "fire": "2-火力打击任务网络", + "logistics": "3-后勤保障任务网络", + "medical": "4-医疗救援任务网络", + "evacuation": "5-紧急疏散任务网络" + } + prob_df = pd.DataFrame([ + {"id": int(task_map[task].split('-')[0]), + "任务": task_map[task].split('-')[1], + "概率%": f"{pct:.1f}"} + for task, pct in task_pct.items() + ]) + + # 3. 写文件 + project_root = Path(__file__).parent.parent # src -> 227 + output_path_csv = project_root / "results" / csv_name + triple_df.to_csv(output_path_csv, index=False, encoding='utf-8-sig') + + output_path = project_root / "results" / excel_name + + with pd.ExcelWriter(output_path, engine='openpyxl') as writer: + triple_df.to_excel(writer, sheet_name='triples', index=False) + prob_df.to_excel(writer, sheet_name='TaskProbs', index=False) + + print(f"已生成：{csv_name} | {excel_name}（含 TaskProbs 页）") + + +def save_evaluation_report(report_content: str, filename: str = "evaluation_report.txt"): + """ + 保存评价报告到TXT文件 + """ + project_root = Path(__file__).parent.parent # src -> 227 + output_path = project_root / "results" / filename + + with open(output_path, 'w', encoding='utf-8') as f: + f.write(report_content) + + print(f"评价报告已保存到 {filename}") + + +def main(): + """ + 主函数：整合所有计算流程 + """ + """运行时交互版主函数""" + import os + from data_loader import load_data as load_data_source + + script_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.dirname(script_dir) + + config = load_config(project_root) + source = config.get('source', 'json') + print(f"数据源模式: {source}") + + try: + data, paths = load_data_source(config, project_root) + except (FileNotFoundError, ValueError) as e: + print(f"错误：{e}") + exit(1) + + os.makedirs(os.path.dirname(paths['report']), exist_ok=True) + + cleaner = AdvancedDataCleaner( + output_file=paths['output'], + report_file=paths['report'], + data=data, + ) + cleaner.run() + + formal_cfg = config.get("formal", {}) + if source == "db" and formal_cfg.get("enabled", False): + print("\n=== 模块1.3：形式化入库流程 ===") + try: + formal_result = run_formalization_pipeline(config) + if formal_result.get("ok"): + print(f"形式化批次: {formal_result.get('batch_id')}") + print(f"形式化统计: {formal_result.get('stats', {})}") + else: + print(f"形式化流程跳过: {formal_result.get('reason', 'unknown')}") + except Exception as e: + print(f"形式化流程执行失败: {e}") + raise + + + print("=== 网络关系与效能评估（交互填参版） ===") + + # 1. 加载默认配置 + cfg = copy.deepcopy(DEFAULT_CFG) + + # 交互式逐项覆盖 + def ask_one_weight(prompt: str, default: float): + """只问一个权重，回车=default""" + while True: + try: + v = input(f"{prompt} (默认={default})：").strip() + return default if v == "" else float(v) + except ValueError: + print("请输入 0-1 之间的数字！") + + def ask_one_int(prompt: str, default: int): + """问整数距离""" + while True: + try: + v = input(f"{prompt} (默认={default} km)：").strip() + return default if v == "" else int(v) + except ValueError: + print("请输入整数！") + + def ask_one_float(prompt: str, default: float): + """问时间衰减系数""" + while True: + try: + v = input(f"{prompt} (默认={default})：").strip() + return default if v == "" else float(v) + except ValueError: + print("请输入数字！") + + # print("\n========== ① 关系权重 (0-1) ==========") + # rels = ["IS", "CC", "SF", "PD", "CO"] + # rel_keys = ["w_f", "w_s", "w_t", "w_p", "w_i"] + # rel_names = ["功能(w_f)", "空间(w_s)", "时间(w_t)", "性能(w_p)", "交互(w_i)"] + # for rel in ["IS", "CC", "SF", "PD", "CO"]: + # print(f"\n-- {rel} 关系 --w_f/w_s/w_t/w_p/w_i") + # for k, name in zip(rel_keys, rel_names): + # old = cfg["W_REL"][rel][k] + # cfg["W_REL"][rel][k] = ask_one_weight(name, old) + # + # print("\n========== ② 网络权重 (0-1) ==========") + # tasks = ["defense", "fire", "logistics", "medical", "evacuation"] + # tfactor = ["w1", "w2", "w3", "w4", "w5"] + # for task in tasks: + # print(f"\n-- {task} -- w1/w2/w3/w4/w5") + # for i, tf in enumerate(tfactor): + # old = cfg["W_NET"][task][tf] + # cfg["W_NET"][task][tf] = ask_one_weight(tf, old) + # + # print("\n========== ③ 特征距离 LAMBDA (km) ==========") + # for rel in rels: + # old = cfg["LAMBDA"][rel] + # cfg["LAMBDA"][rel] = ask_one_float(f"{rel} 距离", old) + # + # print("\n========== ④ 时间衰减系数 ==========") + # cfg["TIME"]["alpha"] = ask_one_float("alpha", cfg["TIME"]["alpha"]) + # cfg["TIME"]["beta"] = ask_one_float("beta", cfg["TIME"]["beta"]) + # cfg["TIME"]["gamma"] = ask_one_float("gamma", cfg["TIME"]["gamma"]) + # + # print("\n========== ⑤ 其他 ==========") + # cfg["PD_SGM"] = ask_one_float("平台共位阈值 PD_SGM", cfg["PD_SGM"]) + # cfg["THRESHOLD"] = ask_one_weight("关系过滤阈值", cfg["THRESHOLD"]) + # + + print("开始网络关系计算") + print("=" * 50) + + + try: + # 1. 加载数据 + print("步骤1: 加载数据...") + nodes_data, triples_data = load_data(project_root) + print(f"加载节点数: {len(nodes_data['nodes'])}") + print(f"加载三元组数: {len(triples_data['triples'])}") + + # 2. 计算并过滤关系强度 + print(f"\n步骤2: 计算关系强度并过滤(阈值={cfg['THRESHOLD']})") + filtered_relations, filtered_triples, relation_stats, all_relations = calculate_and_filter_relations( + nodes_data, triples_data, cfg["THRESHOLD"], cfg + ) + + # 3. 评估任务网络效能（使用过滤后的关系） + print("\n步骤3: 使用过滤后的关系评估任务网络效能...") + network_results = evaluate_task_networks(nodes_data, filtered_relations, cfg) + + # 4. 生成评价报告 + print("\n步骤4: 生成评价报告...") + report_content = generate_evaluation_report( + relation_stats, network_results, filtered_relations, all_relations, cfg["THRESHOLD"],cfg + ) + + # 5. 保存结果 + print("\n步骤5: 保存结果文件...") + save_triples_and_task_probs(filtered_triples, network_results) + save_filtered_triples(filtered_triples) + save_evaluation_report(report_content) + + # 6. 在控制台显示关键信息 + print("\n" + "=" * 50) + print("关键结果摘要:") + print(f"- 过滤后保留的三元组: {len(filtered_triples)} 个") + print("所有计算完成!") + print("=" * 50) + + except Exception as e: + print(f"程序执行出错: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/final/src/triplet_cleaner.py b/final/src/triplet_cleaner.py new file mode 100644 index 0000000..73c3071 --- /dev/null +++ b/final/src/triplet_cleaner.py @@ -0,0 +1,113 @@ +""" +三元组清洗模块 +""" +from datetime import datetime +from typing import List, Dict, Any, Set, Tuple + + +def clean_triplets( + triplets: List[Dict[str, Any]], + valid_ids: Set[str] = None, + deduplicate: bool = True, + filter_orphans: bool = False, + remove_null_core: bool = True, + normalize_relation_type: bool = True, +) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + """ + 清洗三元组数据 + + :param triplets: 原始三元组列表 + :param valid_ids: 有效实体 ID 集合（用于 filter_orphans），None 表示不校验 + :param deduplicate: 是否按 (HEAD_ID, RELATION_TYPE, TAIL_ID) 去重 + :param filter_orphans: 是否过滤悬空引用（HEAD_ID/TAIL_ID 不在 valid_ids 中） + :param remove_null_core: 是否移除 HEAD_ID/TAIL_ID/RELATION_TYPE 为空的记录 + :param normalize_relation_type: 是否规范化 RELATION_TYPE（去空格） + :return: (清洗后列表, 清洗报告) + """ + report = { + "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "original_count": len(triplets), + "deduplicate": {"enabled": deduplicate, "removed": 0}, + "filter_orphans": {"enabled": filter_orphans, "removed": 0}, + "remove_null_core": {"enabled": remove_null_core, "removed": 0}, + "normalize_relation_type": {"enabled": normalize_relation_type}, + "final_count": 0, + } + + result = list(triplets) + + def _head(t): return _str(t.get("HEAD_ID") or t.get("head")) + def _tail(t): return _str(t.get("TAIL_ID") or t.get("tail")) + def _rel(t): return _str(t.get("RELATION_TYPE") or t.get("relation")) + + if remove_null_core: + before = len(result) + result = [t for t in result if _not_empty(_head(t)) and _not_empty(_tail(t)) and _not_empty(_rel(t))] + report["remove_null_core"]["removed"] = before - len(result) + + if normalize_relation_type: + for t in result: + k = "RELATION_TYPE" if "RELATION_TYPE" in t else "relation" + if isinstance(t.get(k), str): + t[k] = t[k].strip() + + if filter_orphans and valid_ids is not None: + before = len(result) + result = [t for t in result if _head(t) in valid_ids and _tail(t) in valid_ids] + report["filter_orphans"]["removed"] = before - len(result) + + if deduplicate: + before = len(result) + seen = set() + unique = [] + for t in result: + key = (_head(t), _rel(t), _tail(t)) + if key not in seen: + seen.add(key) + unique.append(t) + result = unique + report["deduplicate"]["removed"] = before - len(result) + + report["final_count"] = len(result) + return result, report + + +def _not_empty(v: Any) -> bool: + if v is None: + return False + if isinstance(v, str) and not v.strip(): + return False + return True + + +def _str(v: Any) -> str: + if v is None: + return "" + return str(v).strip() + + +def transform_triplet_output_format( + triplets: List[Dict[str, Any]], + id_to_role: dict = None, +) -> List[Dict[str, Any]]: + """ + 将三元组转为输出格式： + - HEAD_ID/head -> head, TAIL_ID/tail -> tail, RELATION_TYPE/relation -> relation + - 新增 head_type、tail_type（从 id_to_role 查 ROLE_ID，已有则保留） + """ + id_to_role = id_to_role or {} + result = [] + for t in triplets: + head = _str(t.get("HEAD_ID") or t.get("head")) + tail = _str(t.get("TAIL_ID") or t.get("tail")) + rel = _str(t.get("RELATION_TYPE") or t.get("relation")) + head_type = _str(t.get("head_type")) or id_to_role.get(head, "") + tail_type = _str(t.get("tail_type")) or id_to_role.get(tail, "") + result.append({ + "head": head, + "head_type": head_type, + "relation": rel, + "tail": tail, + "tail_type": tail_type, + }) + return result diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index b937909..0000000 --- a/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -numpy>=1.20.0 -pandas>=1.3.0 -PyYAML>=5.4 -# 达梦数据库（仅 source=db 时需要，开发阶段可不安） -# pip install dmPython diff --git a/scripts/inspect_dm_tables.py b/scripts/inspect_dm_tables.py deleted file mode 100644 index 2c9d381..0000000 --- a/scripts/inspect_dm_tables.py +++ /dev/null @@ -1,102 +0,0 @@ -#!/usr/bin/env python3 -""" -在达梦数据库机器上运行，查看各表结构，用于编写 config 中的 db.query -用法: python scripts/inspect_dm_tables.py - -密码获取顺序：环境变量 DM_PASSWORD > 下方 LOCAL_PASSWORD -若使用 LOCAL_PASSWORD，切勿提交到 git！可执行：git update-index --assume-unchanged scripts/inspect_dm_tables.py -""" -import os -import sys - -# 可选：在此直接填写密码（仅限本机调试，切勿提交到版本库！） -LOCAL_PASSWORD = None # 例如: "你的密码" - -# 支持从项目根或 scripts 目录运行 -_script_dir = os.path.dirname(os.path.abspath(__file__)) -_project_root = os.path.dirname(_script_dir) -if _project_root not in sys.path: - sys.path.insert(0, _project_root) - -try: - import dmPython -except ImportError: - print("请先安装: pip install dmPython") - sys.exit(1) - -CONFIG = { - "user": "SYSDBA", - "password": os.environ.get("DM_PASSWORD") or LOCAL_PASSWORD, - "server": "127.0.0.1", - "port": 5080, - "schema": "SYSDBA", -} - -TABLES = [ - "OBJECTIVE_INSTANCE", - "METRIC_VALUE_INSTANCE", - "METRIC_PROFILE", - "ROLE_PROFILE", - "TASK_PROFILE", - "RELATION_INSTANCE", -] - - -def main(): - if not CONFIG["password"]: - print("请设置环境变量 DM_PASSWORD 或在脚本中填写 LOCAL_PASSWORD") - sys.exit(1) - - try: - conn = dmPython.connect( - user=CONFIG["user"], - password=CONFIG["password"], - server=CONFIG["server"], - port=CONFIG["port"], - schema=CONFIG["schema"], - ) - except Exception as e: - print(f"连接失败: {e}") - sys.exit(1) - - cursor = conn.cursor() - print("=" * 60) - print("达梦表结构（用于编写 config/db.query）") - print("=" * 60) - - for table in TABLES: - try: - cursor.execute(f'SELECT * FROM "{CONFIG["schema"]}"."{table}" WHERE 1=0') - cols = [d[0] for d in cursor.description] - print(f"\n【{table}】 列: {cols}") - except Exception as e: - print(f"\n【{table}】 查询失败: {e}") - - cursor.execute( - """ - SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE - FROM DBA_TAB_COLUMNS - WHERE OWNER = ? - ORDER BY TABLE_NAME, COLUMN_ID - """, - (CONFIG["schema"],), - ) - rows = cursor.fetchall() - - print("\n" + "=" * 60) - print("所有列详情 (TABLE_NAME | COLUMN_NAME | DATA_TYPE)") - print("=" * 60) - cur_table = None - for table, col, dtype in rows: - if table != cur_table: - cur_table = table - print(f"\n--- {table} ---") - print(f" {col}: {dtype}") - - cursor.close() - conn.close() - print("\n完成。请根据上述结构在 config 中编写 db.query。") - - -if __name__ == "__main__": - main() diff --git a/src/data_loader.py b/src/data_loader.py deleted file mode 100644 index 6437a05..0000000 --- a/src/data_loader.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -数据加载模块：支持从 JSON 文件或达梦数据库读取数据 -开发阶段使用 JSON，生产环境切换到达梦时只需修改 config 中的 source -""" -import json -import os -from typing import Tuple - - -def load_from_json(file_path: str, project_root: str = None) -> list: - """从 JSON 文件加载数据""" - path = file_path - if project_root and not os.path.isabs(file_path): - path = os.path.join(project_root, file_path) - with open(path, 'r', encoding='utf-8') as f: - return json.load(f) - - -def load_from_dm(config: dict, project_root: str = None) -> list: - """ - 从达梦数据库加载数据 - 需要安装 dmPython 及达梦客户端库，仅在 source=db 时调用 - """ - try: - import dmPython - except ImportError as e: - raise RuntimeError( - "达梦数据库模式需要安装 dmPython。\n" - "请执行: pip install dmPython\n" - "并在有达梦客户端的环境运行（需配置 DM_HOME/LD_LIBRARY_PATH）" - ) from e - - db_cfg = config.get('db', {}) - password = os.environ.get('DM_PASSWORD') - if not password: - raise ValueError( - "达梦模式需要设置环境变量 DM_PASSWORD。\n" - "示例: export DM_PASSWORD=你的密码" - ) - - conn = dmPython.connect( - user=db_cfg.get('user', 'SYSDBA'), - password=password, - server=db_cfg.get('host', '127.0.0.1'), - port=int(db_cfg.get('port', 5080)), - schema=db_cfg.get('schema', 'SYSDBA'), - ) - - try: - cursor = conn.cursor() - query = (db_cfg.get('query') or '').strip() - table = db_cfg.get('table') - if query: - cursor.execute(query) - elif table: - schema = db_cfg.get('schema', 'SYSDBA') - cursor.execute(f'SELECT * FROM "{schema}"."{table}"') - else: - raise ValueError( - "达梦模式需配置 db.query（自定义 SQL）或 db.table。\n" - "当前达梦为规范化表，需通过 query 编写 JOIN 得到扁平化数据。" - ) - columns = [desc[0] for desc in cursor.description] - rows = cursor.fetchall() - cursor.close() - return [dict(zip(columns, row)) for row in rows] - finally: - conn.close() - - -def load_data(config: dict, project_root: str) -> Tuple[list, dict]: - """ - 根据配置加载数据，返回 (数据列表, 路径配置) - """ - source = config.get('source', 'json') - project_root = project_root or os.getcwd() - - if source == 'json': - json_cfg = config.get('json', {}) - data = load_from_json(json_cfg.get('input_file', 'data/raw_data_sample.json'), project_root) - paths = { - 'output': _resolve_path(json_cfg.get('output_file', 'data/cleaned_data_final.json'), project_root), - 'report': _resolve_path(json_cfg.get('report_file', 'report/detailed_cleaning_report.json'), project_root), - } - return data, paths - elif source == 'db': - data = load_from_dm(config, project_root) - json_cfg = config.get('json', {}) - paths = { - 'output': _resolve_path(json_cfg.get('output_file', 'data/cleaned_data_final.json'), project_root), - 'report': _resolve_path(json_cfg.get('report_file', 'report/detailed_cleaning_report.json'), project_root), - } - return data, paths - else: - raise ValueError(f"不支持的 source 类型: {source}，应为 json 或 db") - - -def _resolve_path(path: str, project_root: str) -> str: - if os.path.isabs(path): - return path - return os.path.join(project_root, path)