Ioc Patrol Report

🏢 IOC智能运维报告生成器 - 为智能建筑/园区自动生成专业巡检报告。分析设备状态、报警记录、能耗数据、工单进度，生成日报/周报。支持PostgreSQL数据库连接，输出Markdown/HTML格式。适用于物业管理、商业综合体、工业园区、医院学校等智能建筑运维场景。

Security Scan

The skill's code and docs match its stated purpose, but the packaged config contains hard-coded PostgreSQL credentials pointing to an external host and some doc/metadata inconsistencies — this is unexpected and risky, so proceed with caution.

Purpose & Capability

The skill claims to generate reports from a PostgreSQL data source and the code (scripts/generate_report.py) implements DB queries as expected. However, the repository includes a config.yaml with a hard-coded remote database (host: hightop.xin, port: 55432, db: sunway, user: postgres, password: "Q9iT6>^bZsPIX}A&"). Packaging an unrelated remote DB credential is unnecessary for the stated purpose and strongly inconsistent with expected practice (the SKILL.md suggests using environment variables or a local config).

Instruction Scope

SKILL.md instructs users to edit ~/.openclaw/skills/ioc-patrol-report/config.yaml or use environment variables, but load_config() will prefer the included config.yaml if present. That means running the skill without editing will attempt to connect to the packaged external DB rather than your own. The runtime instructions do not warn about the shipped config with credentials. The code queries multiple tables (including personnel/access tables) which may contain sensitive PII — callers should ensure it connects only to their intended DB.

Install Mechanism

No install script or remote downloads are included; this is an instruction-plus-source package. There are no URL downloads or extract steps in the manifest. Dependencies are normal Python libs (psycopg2, pyyaml).

Credentials

skill.yaml declares DB_HOST/DB_PORT/DB_NAME/DB_USER/DB_PASSWORD as required configuration (reasonable for a DB-backed report tool). But the repository simultaneously ships a full config.yaml with plaintext credentials for an external host. That embedded secret is disproportionate and unnecessary. Also the package metadata listed at the top said 'Required env vars: none' which contradicts skill.yaml — another inconsistency.

Persistence & Privilege

The skill does not request elevated platform privileges and has always:false. It does not modify other skills or system-wide configs. Running it will create report files under the skill's reports/ directory (expected behavior).

What to consider before installing

Key things to consider before installing or running this skill: - Do NOT run the packaged code as-is. The repo contains a config.yaml with plaintext credentials for host hightop.xin (port 55432) and database 'sunway'. If you run the skill without editing the config, it will attempt to connect to that external database instead of your own. - Treat the embedded credentials as a red flag: remove or overwrite config.yaml, or replace it with a template that contains no real credentials. Prefer using environment variables (DB_HOST, DB_NAME, DB_USER, DB_PASSWORD) as suggested in SKILL.md. - Inspect scripts/generate_report.py yourself (or run in an isolated/sandboxed environment) before giving it network access. Note the script issues many SELECT queries (including personnel/access tables) which may expose sensitive data if pointed at your DB. - If you need to use this skill, create and point it at a dedicated, access-controlled database or a read-only user with minimal privileges. Verify queries and limit table access to only the columns required for reporting. - The code has small bugs/rough edges (duplicate except blocks; an except path calls a non-existent get_mock_data()), which suggests the package was not carefully audited — treat it as untrusted sample code until cleaned up. If you want, I can: (1) show the exact lines in config.yaml and generate_report.py that are most concerning, (2) produce a safe config template you can drop in place, or (3) advise how to run it in an isolated container with no network access.

🏢 IOC 智能巡检报告

为智能建筑/园区自动生成专业巡检报告

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✨ 核心功能

功能模块	描述
📊 设备状态巡检	分析设备在线率、故障分布、运行状态
🚨 报警处理分析	统计报警数量、响应时长、处理率、重复报警
⚡ 能耗数据分析	对比昨日/上周能耗，识别异常用能点
📝 工单进度追踪	统计工单完成率、SLA达成情况、积压情况
💡 智能建议生成	基于数据自动生成运维优化建议

---

🚀 快速开始

安装

clawhub install ioc-patrol-report

配置数据源

编辑 ~/.openclaw/skills/ioc-patrol-report/config.yaml：

database:
  host: localhost
  port: 5432
  name: ioc_db
  user: admin
  password: ${DB_PASSWORD}  # 建议使用环境变量

tables:
  devices: devices          # 设备表
  alarms: alarms            # 报警表
  work_orders: work_orders  # 工单表
  energy: energy_records    # 能耗表

report:
  company_name: "XX物业管理有限公司"
  project_name: "XX商业综合体"
  output_dir: "./reports"

生成报告

# 生成今日巡检报告
cd skills/ioc-patrol-report
uv run scripts/generate_report.py --type daily

# 生成指定日期报告
uv run scripts/generate_report.py --type daily --date 2026-03-20

# 生成本周巡检报告
uv run scripts/generate_report.py --type weekly --week 2026-W12

---

📋 报告示例

日报输出格式

# 📊 IOC智能巡检日报 - 2026-03-20

## 一、设备状态总览

| 类型 | 总数 | 在线 | 离线 | 故障 | 在线率 |
|------|------|------|------|------|--------|
| 空调 | 128 | 125 | 2 | 1 | 97.7% |
| 照明 | 356 | 354 | 2 | 0 | 99.4% |
| 电梯 | 24 | 24 | 0 | 0 | 100% |
| 消防 | 89 | 89 | 0 | 0 | 100% |

## 二、报警处理情况

- 今日报警总数：23 条
- 已处理：21 条
- 处理中：2 条
- 平均响应时间：8.5 分钟
- 处理率：91.3%

### 重点关注报警
1. 🔴 3F-空调机组A异常（处理中）
2. 🟡 地下室照明回路故障（已处理）

## 三、能耗分析

| 能源类型 | 今日用量 | 昨日用量 | 环比 |
|----------|----------|----------|------|
| 电(kWh) | 12,345 | 12,100 | +2.0% |
| 水(m³) | 456 | 480 | -5.0% |
| 天然气(m³) | 123 | 130 | -5.4% |

## 四、工单进度

- 新增工单：5 个
- 已完成：4 个
- 进行中：3 个
- SLA达成率：92%

## 五、运维建议

1. ⚠️ 3F空调机组需重点关注，建议安排检修
2. ✅ 能耗整体平稳，用水量持续下降，节水措施见效
3. 📝 建议增加地下室照明巡检频次

---
*报告生成时间：2026-03-20 08:00*
*IOC智能运维系统*

---

📖 使用案例

案例1：商业综合体日常巡检

场景：某商业综合体物业每天需要生成巡检报告

配置：

report:
  company_name: "XX物业管理公司"
  project_name: "XX购物中心"

定时任务：

# crontab -e
0 8 * * * cd /root/clawd/skills/ioc-patrol-report && uv run scripts/generate_report.py --type daily

案例2：工业园区周报汇总

场景：工业园区每周汇总运维数据

配置：

report:
  company_name: "XX工业园区"
  project_name: "A区厂房"
  weekly_summary: true

执行：

# 每周一早8点生成
0 8 * * 1 uv run scripts/generate_report.py --type weekly

案例3：医院智能运维

场景：医院后勤部门需要重点关注设备运行

自定义配置：

priority_devices:
  - type: "医用气体"
    alert_threshold: 99.9%  # 可用性要求
  - type: "净化空调"
    alert_threshold: 99%
  - type: "电梯"
    alert_threshold: 99.5%

案例4：数据中心巡检

场景：数据中心需要严格监控温湿度和电力

自定义指标：

custom_metrics:
  temperature:
    range: [18, 27]  # 合规范围
    alert_deviation: 2  # 偏差告警阈值
  humidity:
    range: [40, 60]
  ups_load:
    max: 80%  # 负载上限

---

⚙️ 高级配置

自定义报告模板

编辑 assets/report-template.md：

# {{company_name}} 巡检报告

## {{project_name}} - {{date}}

{% for section in sections %}
{{ section.content }}
{% endfor %}

自定义分析规则

编辑 scripts/analyze.py：

def custom_kpi(df):
    """自定义KPI计算"""
    return {
        'availability': df['online'] / df['total'],
        'efficiency': df['output'] / df['input']
    }

def anomaly_detection(df):
    """能耗异常检测"""
    threshold = df['value'].mean() * 1.5
    return df[df['value'] > threshold]

---

📁 目录结构

ioc-patrol-report/
├── SKILL.md              # 本文档
├── package.json          # 技能配置
├── config.yaml           # 数据源配置
├── scripts/
│   ├── generate_report.py  # 主程序
│   ├── analyze.py          # 分析模块
│   └── db_connector.py     # 数据库连接
├── assets/
│   └── report-template.md  # 报告模板
├── references/
│   ├── ioc-knowledge.md    # IOC知识库
│   └── db-schema.md        # 数据库结构说明
└── tests/
    └── test_report.py      # 单元测试

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🔧 技术要求

• Python >= 3.10
• PostgreSQL >= 12
• 必需依赖：psycopg2-binary, pyyaml, pandas, jinja2

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📊 适用场景

场景	报告周期	重点关注
商业综合体	日报/周报	客流、能耗、设备
工业园区	周报/月报	设备运行、能耗成本
医院学校	日报	关键设备、环境参数
数据中心	实时/日报	温湿度、电力、网络
智慧社区	周报	安防、能耗、设备

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让运维报告自动化、智能化 📊