# -*- coding: utf-8 -*-
"""
六维风险评分器
整合所有数据源和分析结果，生成结构化六维风险画像
"""
import logging
from typing import Optional

logger = logging.getLogger(__name__)


# 风险等级映射
RISK_LEVELS = {
    (0, 30): {"level": "低", "color": "#4CAF50", "emoji": "🟢"},
    (30, 50): {"level": "中低", "color": "#8BC34A", "emoji": "🟡"},
    (50, 70): {"level": "中高", "color": "#FF9800", "emoji": "🟠"},
    (70, 90): {"level": "高", "color": "#F44336", "emoji": "🔴"},
    (90, 101): {"level": "极高", "color": "#B71C1C", "emoji": "⛔"},
}


def get_risk_level(score: int) -> dict:
    """根据分数获取风险等级详情"""
    for (low, high), info in RISK_LEVELS.items():
        if low <= score < high:
            return info
    return {"level": "未知", "color": "#9E9E9E", "emoji": "❓"}


def calculate_six_dimension_scores(company_data: dict) -> dict:
    """
    基于企业数据直接计算六维风险评分（不依赖 LLM）
    用于快速预览和规则引擎降级场景
    """
    financials = company_data.get("financials", {})
    compliance = company_data.get("compliance", {})
    tech_route = company_data.get("tech_route", {})
    personnel = company_data.get("core_tech_personnel", [])
    supply_chain = company_data.get("supply_chain", {})

    scores = {}

    # 1. 技术路线颠覆风险
    competing = len(tech_route.get("competing_techs", []))
    moat = tech_route.get("tech_moat", "")
    tech_score = min(25 + competing * 15, 85)
    if "差距" in moat or "受制" in moat or "威胁" in moat:
        tech_score = min(tech_score + 15, 95)
    if "领先" in moat or "第一" in moat:
        tech_score = max(tech_score - 10, 10)
    scores["tech_disruption"] = tech_score

    # 2. 核心人员流失风险
    departed = [p for p in personnel if "离职" in p.get("status", "")]
    high_imp = [p for p in personnel if p.get("importance") == "极高"]
    if departed:
        scores["talent_loss"] = 80
    elif len(high_imp) <= 1 and len(personnel) <= 2:
        scores["talent_loss"] = 55
    else:
        scores["talent_loss"] = 25

    # 3. 算法/数据合规风险
    algo_status = compliance.get("algo_filing_status", "")
    data_risk = compliance.get("data_export_risk", "低")
    algo_score = 20
    if "未" in algo_status:
        algo_score = 75
    elif data_risk == "高":
        algo_score = 65
    elif data_risk == "中":
        algo_score = 40
    elif "已备案" in algo_status:
        algo_score = 15
    scores["algo_compliance"] = algo_score

    # 4. 地缘政治/出口管制风险
    entity_status = compliance.get("entity_list_status", "")
    if "被列入" in entity_status:
        scores["geopolitical"] = 92
    elif supply_chain.get("supplier_concentration_risk") == "极高":
        scores["geopolitical"] = 68
    elif supply_chain.get("supplier_concentration_risk") == "高":
        scores["geopolitical"] = 50
    else:
        scores["geopolitical"] = 18

    # 5. 研发资本化操纵风险
    cap_rate = financials.get("rd_capitalization_rate", 0)
    if cap_rate >= 0.4:
        scores["rd_capitalization"] = 90
    elif cap_rate >= 0.3:
        scores["rd_capitalization"] = 72
    elif cap_rate >= 0.15:
        scores["rd_capitalization"] = 48
    elif cap_rate > 0:
        scores["rd_capitalization"] = 25
    else:
        scores["rd_capitalization"] = 10

    # 6. 客户/供应商集中风险
    cust_ratio = financials.get("top5_customer_ratio", 0)
    supp_ratio = financials.get("top5_supplier_ratio", 0)
    max_conc = max(cust_ratio, supp_ratio)
    if max_conc >= 0.8:
        scores["concentration"] = 88
    elif max_conc >= 0.6:
        scores["concentration"] = 68
    elif max_conc >= 0.4:
        scores["concentration"] = 42
    else:
        scores["concentration"] = 18

    # 加权综合
    weights = {
        "tech_disruption": 0.20,
        "talent_loss": 0.15,
        "algo_compliance": 0.15,
        "geopolitical": 0.20,
        "rd_capitalization": 0.15,
        "concentration": 0.15,
    }
    comprehensive = int(sum(scores[k] * weights[k] for k in scores))

    # 维度中文名映射
    dim_names = {
        "tech_disruption": "技术路线颠覆",
        "talent_loss": "核心人员流失",
        "algo_compliance": "算法/数据合规",
        "geopolitical": "地缘政治/出口管制",
        "rd_capitalization": "研发资本化操纵",
        "concentration": "客户/供应商集中",
    }

    return {
        "scores": scores,
        "comprehensive_score": comprehensive,
        "risk_level": get_risk_level(comprehensive),
        "dimension_details": {
            k: {
                "name": dim_names[k],
                "score": v,
                "level": get_risk_level(v),
                "weight": weights[k],
            }
            for k, v in scores.items()
        },
    }