绝尘
cf017f81e2
重构：删 5 零引用 crate（df-evolve/plugin/stages/task/traceability）+ 清死模块、ai.rs 拆 11 子 module、ai.ts 拆 6 composable、i18n 拆目录 功能：知识库全栈（df-project/scan + CRUD + 时间线 + 前端）、Settings 拆分、appSettings KV 迁移、模型池、LLM 并发 Semaphore 修复：审批持久化根治、ConditionEngine 默认拒绝、NodeRegistry unimplemented 清除、promote 补偿删除、工具结果截断 50KB、路径校验防 symlink 逃逸 文档：B-03 人工审批设计、决策记录三分档、规格契约自检、经验记录、todo 看板、PROGRESS 更新 详见 PROGRESS.md。src-tauri/儿童每日打卡应用/ 与本项目无关，已排除。
381 lines
16 KiB
Rust
381 lines
16 KiB
Rust
//! 对抗式评估系统 — 正反方辩论 + AI 分析师
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//!
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//! 当前为基于评分与内容信号的启发式实现（稳定、有区分度）。
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//! TODO: 接入 df-ai LlmProvider 让正反方论点由 LLM 生成，启发式降级为 fallback。
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use anyhow::Result;
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use serde::{Deserialize, Serialize};
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use df_core::types::{IdeaId, Priority};
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use crate::capture::Idea;
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use crate::scoring::IdeaScores;
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/// 对抗评估结果
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct AdversarialEval {
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pub idea_id: IdeaId,
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pub positive: Argument,
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pub negative: Argument,
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pub analyst: AnalystAnalysis,
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pub final_score: f64,
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pub recommendation: Recommendation,
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}
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/// 论点（正方/反方共用同一结构）
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Argument {
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pub thesis: String, // 核心观点
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pub evidence: Vec<String>, // 证据支持
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pub reasoning: Vec<String>, // 推理过程
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pub confidence: f64, // 置信度 0-1
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}
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/// AI 分析师综合分析
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct AnalystAnalysis {
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pub summary: String, // 综合总结
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pub strengths: Vec<String>, // 主要优势
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pub weaknesses: Vec<String>, // 主要劣势
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pub risks: Vec<String>, // 潜在风险
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pub opportunities: Vec<String>, // 机会点
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pub final_assessment: AssessmentLevel, // 最终评估
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}
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/// 评估等级
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
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pub enum AssessmentLevel {
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StrongGo, // 强烈推荐执行
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Recommended, // 推荐执行
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Conditional, // 有条件执行
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Revised, // 需要修改后执行
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Defer, // 推迟执行
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}
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/// 最终建议
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
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pub enum Recommendation {
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ImmediateAction, // 立即行动
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Soon, // 尽快行动
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WithResources, // 配置资源后行动
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ResearchMore, // 需要更多研究
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Monitor, // 持续监控
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}
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/// 对抗评估引擎
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pub struct AdversarialEngine;
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impl AdversarialEngine {
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/// 执行完整的对抗评估
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#[allow(clippy::unused_async)] // 签名保留 async,待接 LLM 注入异步调用
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pub async fn evaluate(idea: &Idea) -> Result<AdversarialEval> {
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// 先做多维评分，作为正反方论点与置信度的依据
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let scores = crate::scoring::ScoringEngine::compute_default(idea);
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let positive = Self::generate_positive_argument(idea, &scores)?;
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let negative = Self::generate_negative_argument(idea, &scores)?;
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let analyst = Self::analyst_analysis(idea, &scores)?;
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let recommendation = Self::recommendation_for(&analyst.final_assessment);
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Ok(AdversarialEval {
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idea_id: idea.id.clone(),
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positive,
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negative,
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analyst,
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final_score: scores.overall,
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recommendation,
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})
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}
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/// 生成正方观点（支持执行）— confidence 由可行性 + 影响力驱动
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/// 注:返回 Result 为后续 LLM 注入失败预留,启发式阶段恒 Ok
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fn generate_positive_argument(idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
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let desc = idea.description.trim();
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let mut evidence = Vec::new();
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evidence.push(format!("优先级：{}", priority_label(&idea.priority)));
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if desc.is_empty() {
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evidence.push("需求待补充（建议补全描述）".to_string());
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} else {
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let head: String = desc.chars().take(60).collect();
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evidence.push(format!("明确需求：{}", head));
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}
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if idea.tags.is_empty() {
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evidence.push("关联领域待界定".to_string());
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} else {
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evidence.push(format!("关联领域：{}", idea.tags.join("、")));
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}
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if scores.impact >= 7.0 {
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evidence.push("业务价值显著，影响面较广".to_string());
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}
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// 正方置信度:可行性+影响力等权折算到 [0.1, 0.95],满分≈0.95 留质疑余地
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let confidence =
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((scores.feasibility * 0.5 + scores.impact * 0.5) / 10.0).clamp(0.1, 0.95);
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let reasoning = vec![
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format!("可行性评分 {:.1}/10，路径相对清晰", scores.feasibility),
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format!("影响力评分 {:.1}/10，预期回报可观", scores.impact),
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"整体风险可控，适合推进".to_string(),
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];
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Ok(Argument {
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thesis: format!("「{}」具备明确价值与可行性，建议优先推进", idea.title),
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evidence,
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reasoning,
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confidence,
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})
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}
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/// 生成反方观点（反对或谨慎）— 论点基于想法实际缺陷，confidence 随风险上升
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fn generate_negative_argument(idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
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let desc = idea.description.trim();
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let mut evidence = Vec::new();
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if desc.is_empty() {
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evidence.push("描述过于简略，需求边界不清".to_string());
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} else if desc.chars().count() < 50 {
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evidence.push("描述偏短，实现细节尚未论证".to_string());
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}
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if idea.tags.is_empty() {
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evidence.push("缺少标签，影响范围未界定".to_string());
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}
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if scores.feasibility < 6.0 {
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evidence.push(format!("可行性 {:.1}/10 偏低，实现路径存疑", scores.feasibility));
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}
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if matches!(idea.priority, Priority::Low) {
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evidence.push("优先级偏低，可能非当前关键路径".to_string());
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}
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if evidence.is_empty() {
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evidence.push("机会成本需权衡，可能存在更优替代方案".to_string());
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}
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// 反方强度:feasibility 每降 1 分 +0.04,impact 每降 1 分 +0.03,基线 0.25(满分也保留最低质疑),clamp [0.1, 0.9]
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let confidence = ((10.0 - scores.feasibility) * 0.04 + (10.0 - scores.impact) * 0.03 + 0.25)
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.clamp(0.1, 0.9);
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let reasoning = vec![
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format!("资源投入与当前综合评分 {:.1} 需匹配", scores.overall),
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"ROI 需进一步验证".to_string(),
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"需评估是否存在更优解".to_string(),
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];
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Ok(Argument {
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thesis: format!("「{}」需谨慎评估，存在风险与机会成本", idea.title),
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evidence,
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reasoning,
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confidence,
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})
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}
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/// AI 分析师综合分析 — 评估等级由综合评分决定，优势/劣势按维度动态生成
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fn analyst_analysis(idea: &Idea, scores: &IdeaScores) -> Result<AnalystAnalysis> {
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let final_assessment = match scores.overall {
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x if x >= 7.5 => AssessmentLevel::StrongGo,
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x if x >= 6.0 => AssessmentLevel::Recommended,
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x if x >= 4.5 => AssessmentLevel::Conditional,
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x if x >= 3.0 => AssessmentLevel::Revised,
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_ => AssessmentLevel::Defer,
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};
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let mut strengths = Vec::new();
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if scores.impact >= 6.0 {
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strengths.push("业务价值明确".to_string());
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}
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if scores.feasibility >= 6.0 {
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strengths.push("技术路径清晰".to_string());
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}
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if scores.urgency >= 7.0 {
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strengths.push("时间窗口合适".to_string());
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}
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if strengths.is_empty() {
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strengths.push("方向值得探索".to_string());
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}
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let mut weaknesses = Vec::new();
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if scores.feasibility < 6.0 {
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weaknesses.push("可行性论证不足".to_string());
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}
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if idea.description.trim().is_empty() {
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weaknesses.push("需求描述缺失".to_string());
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}
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if scores.urgency < 4.0 {
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weaknesses.push("紧急度偏低，易被搁置".to_string());
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}
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if weaknesses.is_empty() {
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weaknesses.push("资源需求待评估".to_string());
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}
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// 启发式占位:固定风险模板,与具体想法无关,接 LLM 后改动态生成
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let risks = vec![
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"技术实现难度可能超出预期".to_string(),
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"优先级与资源争夺".to_string(),
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"需求范围蔓延".to_string(),
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];
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let opportunities = vec![
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"可能形成可复用能力".to_string(),
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"积累技术资产".to_string(),
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];
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let summary = format!(
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"「{}」综合评分 {:.1}/10，{}。建议{}。",
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idea.title,
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scores.overall,
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assessment_desc(&final_assessment),
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action_hint(&final_assessment)
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);
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Ok(AnalystAnalysis {
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summary,
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strengths,
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weaknesses,
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risks,
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opportunities,
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final_assessment,
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})
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}
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/// 评估等级 → 最终建议
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fn recommendation_for(level: &AssessmentLevel) -> Recommendation {
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match level {
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AssessmentLevel::StrongGo => Recommendation::ImmediateAction,
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AssessmentLevel::Recommended => Recommendation::Soon,
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AssessmentLevel::Conditional => Recommendation::WithResources,
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AssessmentLevel::Revised => Recommendation::ResearchMore,
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AssessmentLevel::Defer => Recommendation::Monitor,
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}
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}
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}
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fn priority_label(p: &Priority) -> &'static str {
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match p {
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Priority::Critical => "紧急",
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Priority::High => "高",
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Priority::Medium => "中",
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Priority::Low => "低",
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}
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}
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fn assessment_desc(level: &AssessmentLevel) -> &'static str {
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match level {
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AssessmentLevel::StrongGo => "价值高且可行性强",
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AssessmentLevel::Recommended => "整体值得推进",
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AssessmentLevel::Conditional => "有条件地推进",
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AssessmentLevel::Revised => "需调整后再评估",
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AssessmentLevel::Defer => "建议暂缓",
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}
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}
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fn action_hint(level: &AssessmentLevel) -> &'static str {
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match level {
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AssessmentLevel::StrongGo => "立即立项启动",
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AssessmentLevel::Recommended => "尽快排期",
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AssessmentLevel::Conditional => "配置资源后启动",
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AssessmentLevel::Revised => "补充信息后重新评估",
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AssessmentLevel::Defer => "持续观察时机",
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::capture::Idea;
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use crate::scoring::ScoringEngine;
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use df_core::types::{IdeaStatus, Priority};
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fn make_idea(title: &str, desc: &str, priority: Priority, tags: Vec<&str>) -> Idea {
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Idea {
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id: "test-id".to_string(),
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title: title.to_string(),
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description: desc.to_string(),
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status: IdeaStatus::Draft,
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priority,
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scores: None,
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tags: tags.into_iter().map(String::from).collect(),
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source: None,
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related_ids: Vec::new(),
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created_at: chrono::Utc::now(),
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updated_at: chrono::Utc::now(),
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}
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}
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#[tokio::test]
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async fn a1_high_score_immediate_action() {
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let desc = "面向用户的核心功能，带来显著增长，大幅提升效率。集成成熟方案，复用已有组件。".repeat(3);
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let idea = make_idea("AI增长引擎", &desc, Priority::Critical, vec!["增长", "核心"]);
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let scores = ScoringEngine::compute_default(&idea);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a1] 高分想法 → 期望 ImmediateAction");
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println!(" scores: feas={:.2} impact={:.2} urg={:.2} overall={:.2}", scores.feasibility, scores.impact, scores.urgency, scores.overall);
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println!(" eval: final_score={:.2} recommendation={:?}", eval.final_score, eval.recommendation);
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println!(" 正方 confidence={:.2} 反方 confidence={:.2}", eval.positive.confidence, eval.negative.confidence);
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assert!(eval.final_score >= 7.5, "final_score 应≥7.5, 实际 {:.2}", eval.final_score);
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assert_eq!(eval.recommendation, Recommendation::ImmediateAction);
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}
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#[tokio::test]
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async fn a2_mid_score_soon() {
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let desc = "面向用户的功能，集成已有方案，提升体验".to_string();
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let idea = make_idea("体验优化", &desc, Priority::Medium, vec!["体验"]);
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let scores = ScoringEngine::compute_default(&idea);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a2] 中分想法 → 期望 Soon");
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println!(" scores overall={:.2} eval final_score={:.2} recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
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assert_eq!(eval.recommendation, Recommendation::Soon);
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}
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#[tokio::test]
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async fn a3_low_score_monitor() {
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let desc = "重构迁移大规模分布式重写从零全新架构高并发底层".to_string();
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let idea = make_idea("过度工程", &desc, Priority::Low, vec![]);
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let scores = ScoringEngine::compute_default(&idea);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a3] 低分想法 → 期望 Monitor");
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println!(" scores overall={:.2} eval final_score={:.2} recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
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assert!(eval.final_score < 3.0, "final_score 应<3.0, 实际 {:.2}", eval.final_score);
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assert_eq!(eval.recommendation, Recommendation::Monitor);
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}
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#[tokio::test]
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async fn a4_confidence_ranges() {
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let idea = make_idea("普通想法", "一般描述", Priority::Medium, vec!["标签"]);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a4] confidence 区间校验");
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println!(" 正方={:.2} (应∈[0.1, 0.95]) 反方={:.2} (应∈[0.1, 0.9])", eval.positive.confidence, eval.negative.confidence);
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assert!(eval.positive.confidence >= 0.1 && eval.positive.confidence <= 0.95);
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assert!(eval.negative.confidence >= 0.1 && eval.negative.confidence <= 0.9);
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}
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#[tokio::test]
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async fn a5_positive_thesis_contains_title() {
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let idea = make_idea("独家创意", "描述内容", Priority::High, vec![]);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a5] 正方论点含标题");
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println!(" thesis: {}", eval.positive.thesis);
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assert!(eval.positive.thesis.contains("独家创意"), "正方 thesis 应含标题");
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}
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#[tokio::test]
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async fn a6_negative_evidence_nonempty() {
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let idea = make_idea("待质疑想法", "短", Priority::Low, vec![]);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a6] 反方证据非空 ({} 条)", eval.negative.evidence.len());
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for (i, e) in eval.negative.evidence.iter().enumerate() {
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println!(" 证据{}: {}", i + 1, e);
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}
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assert!(!eval.negative.evidence.is_empty(), "反方 evidence 不应为空");
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}
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#[tokio::test]
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async fn a7_final_score_consistency() {
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let desc = "面向用户的核心功能".to_string();
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let idea = make_idea("一致性测试", &desc, Priority::High, vec!["核心"]);
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let scores = ScoringEngine::compute_default(&idea);
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let eval = AdversarialEngine::evaluate(&idea).await.unwrap();
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println!("\n[a7] final_score == scores.overall 一致性");
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println!(" scores.overall={:.2} eval.final_score={:.2}", scores.overall, eval.final_score);
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println!(" analyst.summary: {}", eval.analyst.summary);
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assert!((eval.final_score - scores.overall).abs() < 0.001, "final_score 应等于 overall");
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assert!(eval.analyst.summary.contains("一致性测试"), "summary 应含标题");
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}
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}
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