工具评估师

# 带量化分析的高级工具评估框架
import pandas as pd
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import requests
import time

@dataclass
class EvaluationCriteria:
    name: str
    weight: float  # 0-1 权重
    max_score: int = 10
    description: str = ""

@dataclass
class ToolScoring:
    tool_name: str
    scores: Dict[str, float]
    total_score: float
    weighted_score: float
    notes: Dict[str, str]

class ToolEvaluator:
    def __init__(self):
        self.criteria = self._define_evaluation_criteria()
        self.test_results = {}
        self.cost_analysis = {}
        self.risk_assessment = {}

    def _define_evaluation_criteria(self) -> List[EvaluationCriteria]:
        """定义加权评估维度"""
        return [
            EvaluationCriteria("functionality", 0.25, description="核心功能完整度"),
            EvaluationCriteria("usability", 0.20, description="用户体验和易用性"),
            EvaluationCriteria("performance", 0.15, description="速度、稳定性、可扩展性"),
            EvaluationCriteria("security", 0.15, description="数据保护和合规性"),
            EvaluationCriteria("integration", 0.10, description="API 质量和系统兼容性"),
            EvaluationCriteria("support", 0.08, description="供应商支持质量和文档"),
            EvaluationCriteria("cost", 0.07, description="总拥有成本和性价比")
        ]

    def evaluate_tool(self, tool_name: str, tool_config: Dict) -> ToolScoring:
        """带量化评分的全面工具评估"""
        scores = {}
        notes = {}

        # 功能测试
        functionality_score, func_notes = self._test_functionality(tool_config)
        scores["functionality"] = functionality_score
        notes["functionality"] = func_notes

        # 易用性测试
        usability_score, usability_notes = self._test_usability(tool_config)
        scores["usability"] = usability_score
        notes["usability"] = usability_notes

        # 性能测试
        performance_score, perf_notes = self._test_performance(tool_config)
        scores["performance"] = performance_score
        notes["performance"] = perf_notes

        # 安全评估
        security_score, sec_notes = self._assess_security(tool_config)
        scores["security"] = security_score
        notes["security"] = sec_notes

        # 集成测试
        integration_score, int_notes = self._test_integration(tool_config)
        scores["integration"] = integration_score
        notes["integration"] = int_notes

        # 支持评估
        support_score, support_notes = self._evaluate_support(tool_config)
        scores["support"] = support_score
        notes["support"] = support_notes

        # 成本分析
        cost_score, cost_notes = self._analyze_cost(tool_config)
        scores["cost"] = cost_score
        notes["cost"] = cost_notes

        # 计算加权分数
        total_score = sum(scores.values())
        weighted_score = sum(
            scores[criterion.name] * criterion.weight
            for criterion in self.criteria
        )

        return ToolScoring(
            tool_name=tool_name,
            scores=scores,
            total_score=total_score,
            weighted_score=weighted_score,
            notes=notes
        )

    def _test_functionality(self, tool_config: Dict) -> tuple[float, str]:
        """按需求清单测试核心功能"""
        required_features = tool_config.get("required_features", [])
        optional_features = tool_config.get("optional_features", [])

        # 测试每个必需功能
        feature_scores = []
        test_notes = []

        for feature in required_features:
            score = self._test_feature(feature, tool_config)
            feature_scores.append(score)
            test_notes.append(f"{feature}: {score}/10")

        # 必需功能占 80% 权重
        required_avg = np.mean(feature_scores) if feature_scores else 0

        # 测试可选功能
        optional_scores = []
        for feature in optional_features:
            score = self._test_feature(feature, tool_config)
            optional_scores.append(score)
            test_notes.append(f"{feature}（可选）: {score}/10")

        optional_avg = np.mean(optional_scores) if optional_scores else 0

        final_score = (required_avg * 0.8) + (optional_avg * 0.2)
        notes = "; ".join(test_notes)

        return final_score, notes

    def _test_performance(self, tool_config: Dict) -> tuple[float, str]:
        """带量化指标的性能测试"""
        api_endpoint = tool_config.get("api_endpoint")
        if not api_endpoint:
            return 5.0, "没有可测试的 API 端点"

        # 响应时间测试
        response_times = []
        for _ in range(10):
            start_time = time.time()
            try:
                response = requests.get(api_endpoint, timeout=10)
                end_time = time.time()
                response_times.append(end_time - start_time)
            except requests.RequestException:
                response_times.append(10.0)  # 超时惩罚

        avg_response_time = np.mean(response_times)
        p95_response_time = np.percentile(response_times, 95)

        # 根据响应时间评分（越低越好）
        if avg_response_time < 0.1:
            speed_score = 10
        elif avg_response_time < 0.5:
            speed_score = 8
        elif avg_response_time < 1.0:
            speed_score = 6
        elif avg_response_time < 2.0:
            speed_score = 4
        else:
            speed_score = 2

        notes = f"平均: {avg_response_time:.2f}s, P95: {p95_response_time:.2f}s"
        return speed_score, notes

    def calculate_total_cost_ownership(self, tool_config: Dict, years: int = 3) -> Dict:
        """全面的总拥有成本分析"""
        costs = {
            "licensing": tool_config.get("annual_license_cost", 0) * years,
            "implementation": tool_config.get("implementation_cost", 0),
            "training": tool_config.get("training_cost", 0),
            "maintenance": tool_config.get("annual_maintenance_cost", 0) * years,
            "integration": tool_config.get("integration_cost", 0),
            "migration": tool_config.get("migration_cost", 0),
            "support": tool_config.get("annual_support_cost", 0) * years,
        }

        total_cost = sum(costs.values())

        # 算每用户每年成本
        users = tool_config.get("expected_users", 1)
        cost_per_user_year = total_cost / (users * years)

        return {
            "cost_breakdown": costs,
            "total_cost": total_cost,
            "cost_per_user_year": cost_per_user_year,
            "years_analyzed": years
        }

    def generate_comparison_report(self, tool_evaluations: List[ToolScoring]) -> Dict:
        """生成全面的对比报告"""
        # 创建对比矩阵
        comparison_df = pd.DataFrame([
            {
                "Tool": eval.tool_name,
                **eval.scores,
                "Weighted Score": eval.weighted_score
            }
            for eval in tool_evaluations
        ])

        # 排名
        comparison_df["Rank"] = comparison_df["Weighted Score"].rank(ascending=False)

        # 找出各维度的优胜者
        analysis = {
            "top_performer": comparison_df.loc[comparison_df["Rank"] == 1, "Tool"].iloc[0],
            "score_comparison": comparison_df.to_dict("records"),
            "category_leaders": {
                criterion.name: comparison_df.loc[comparison_df[criterion.name].idxmax(), "Tool"]
                for criterion in self.criteria
            },
            "recommendations": self._generate_recommendations(comparison_df, tool_evaluations)
        }

        return analysis

# [工具类别] 评估与选型报告

## 管理层摘要
**推荐方案**：[排名第一的工具及核心优势]
**所需投入**：[总成本，附 ROI 时间线和盈亏平衡分析]
**实施时间**：[各阶段及关键里程碑和资源需求]
**业务影响**：[量化的生产力提升和效率改进]

## 评估结果
**工具对比矩阵**：[各评估维度的加权评分]
**各维度最佳**：[特定能力上的最优工具]
**性能基准**：[量化性能测试结果]
**用户体验评分**：[不同角色的可用性测试结果]

## 财务分析
**总拥有成本**：[3 年 TCO 明细及敏感性分析]
**ROI 测算**：[不同推广场景下的预期回报]
**成本对比**：[人均成本和扩容影响]
**预算影响**：[年度预算需求和付款方式]

## 风险评估
**实施风险**：[技术、组织和供应商风险]
**安全评估**：[合规、数据保护和漏洞评估]
**供应商评估**：[稳定性、路线图匹配和合作潜力]
**应对策略**：[风险降低和应急方案]

## 实施策略
**推广计划**：[分阶段实施，先试点后全面部署]
**变更管理**：[培训策略、沟通计划和推广支持]
**集成需求**：[技术集成和数据迁移规划]
**成功指标**：[衡量实施成功和 ROI 的 KPI]

---
**评估员**：[姓名]
**评估日期**：[日期]
**置信度**：[高/中/低，附方法论说明]
**下次评审**：[计划的复评时间和触发条件]