cma-management/backend/scripts/seed_finance_bot_kpis.py

"""
种子数据：财务Bot KPI（11个）
插入到 kpi_definitions 表，bot_source='finance-bot'
"""
import pymysql
import os
import sys
from datetime import datetime

DB_USER = os.getenv("CMA_DB_USER", "cma_user")
DB_PASS = os.getenv("CMA_DB_PASS", "cma_pass_2026")
DB_HOST = os.getenv("CMA_DB_HOST", "127.0.0.1")
DB_PORT = int(os.getenv("CMA_DB_PORT", "3306"))
DB_NAME = os.getenv("CMA_DB_NAME", "cma")

FINANCE_BOT_KPIS = [
    # ── 核心产出（5个 · 月度考核）──
    {
        "kpi_code": "FB_ANALYSIS_COUNT",
        "kpi_name": "分析报告产出数",
        "formula": "月度生成的结构化分析报告数量",
        "unit": "份",
        "target_value": 20,
        "frequency": "monthly",
        "category": "core_output",
        "weight": 15,
    },
    {
        "kpi_code": "FB_ACCURACY_RATE",
        "kpi_name": "数据提取准确率",
        "formula": "1−(数据错误次数/总分析次数)",
        "unit": "%",
        "target_value": 98,
        "frequency": "monthly",
        "category": "core_output",
        "weight": 25,
    },
    {
        "kpi_code": "FB_ISSUE_FOUND",
        "kpi_name": "问题发现数",
        "formula": "月度发现的影响经营的问题数量",
        "unit": "个",
        "target_value": 5,
        "frequency": "monthly",
        "category": "core_output",
        "weight": 20,
    },
    {
        "kpi_code": "FB_ACTION_RATE",
        "kpi_name": "行动采纳率",
        "formula": "被用户采纳的行动建议数/总建议数",
        "unit": "%",
        "target_value": 60,
        "frequency": "monthly",
        "category": "core_output",
        "weight": 25,
    },
    {
        "kpi_code": "FB_RESPONSE_TIME",
        "kpi_name": "响应时效",
        "formula": "用户发文件到出分析结果的平均时间",
        "unit": "分钟",
        "target_value": 10,
        "frequency": "monthly",
        "category": "core_output",
        "weight": 15,
    },
    # ── 质量监控（3个 · 月度考核）──
    {
        "kpi_code": "FB_DATA_GAP",
        "kpi_name": "数据间隙发现率",
        "formula": "发现的数据异常/缺失数 / 应发现数",
        "unit": "%",
        "target_value": 90,
        "frequency": "monthly",
        "category": "quality",
        "weight": 30,
    },
    {
        "kpi_code": "FB_CONSISTENCY",
        "kpi_name": "跨期一致性",
        "formula": "同期指标口径是否一致",
        "unit": "%",
        "target_value": 100,
        "frequency": "monthly",
        "category": "quality",
        "weight": 30,
    },
    {
        "kpi_code": "FB_CITATION",
        "kpi_name": "结论可追溯率",
        "formula": "每个结论有对应的数据来源",
        "unit": "%",
        "target_value": 100,
        "frequency": "monthly",
        "category": "quality",
        "weight": 40,
    },
    # ── 用户反馈（3个 · 季度考核）──
    {
        "kpi_code": "FB_SATISFACTION",
        "kpi_name": "用户满意度",
        "formula": "用户对分析报告的评分（1-5分）",
        "unit": "分",
        "target_value": 4.0,
        "frequency": "quarterly",
        "category": "user_feedback",
        "weight": 40,
    },
    {
        "kpi_code": "FB_REUSE_RATE",
        "kpi_name": "复用率",
        "formula": "用户连续使用天数/月总天数",
        "unit": "%",
        "target_value": 80,
        "frequency": "quarterly",
        "category": "user_feedback",
        "weight": 30,
    },
    {
        "kpi_code": "FB_REFERRAL",
        "kpi_name": "推荐率",
        "formula": "用户主动向他人推荐次数",
        "unit": "次",
        "target_value": 1,
        "frequency": "quarterly",
        "category": "user_feedback",
        "weight": 30,
    },
]


def run():
    conn = pymysql.connect(
        host=DB_HOST, user=DB_USER, password=DB_PASS,
        database=DB_NAME, charset="utf8mb4",
    )
    cursor = conn.cursor()

    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    inserted = 0
    skipped = 0

    for kpi in FINANCE_BOT_KPIS:
        code = kpi["kpi_code"]
        # 检查是否已存在
        cursor.execute("SELECT id FROM kpi_definitions WHERE kpi_code = %s", (code,))
        existing = cursor.fetchone()
        if existing:
            print(f"  ⏭  {code} 已存在 (id={existing[0]})")
            skipped += 1
            continue

        sql = """
            INSERT INTO kpi_definitions
                (entity_id, kpi_code, kpi_name, dimension, formula, unit,
                 target_value, frequency, category, status, bot_source, data_source,
                 data_owner, created_at, updated_at)
            VALUES
                (%s, %s, %s, %s, %s, %s,
                 %s, %s, %s, 'active', 'finance-bot', 'Bot自计数',
                 'FinanceBot', %s, %s)
        """
        cursor.execute(sql, (
            1, code, kpi["kpi_name"], "process", kpi["formula"], kpi["unit"],
            kpi["target_value"], kpi["frequency"], kpi["category"],
            now, now,
        ))
        new_id = cursor.lastrowid
        print(f"  ✅ {code} -> id={new_id}")
        inserted += 1

    conn.commit()
    cursor.close()
    conn.close()
    print(f"\n完成：新增 {inserted} 条，跳过 {skipped} 条（共 {len(FINANCE_BOT_KPIS)} 个KPI）")


if __name__ == "__main__":
    run()