Version: 0.9.45.dev.260427

后端： 1. execute 主链路重构为“上下文工具域 + 主动优化候选闭环”——移除 order_guard，粗排后默认进入主动微调，先诊断再从后端候选中选择 move/swap，避免 LLM 自由全局乱搜 2. 工具体系升级为动态注入协议——新增 context_tools_add / remove、工具域与二级包映射、主动优化白名单；schedule / taskclass / web 工具按域按包暴露，msg0 规则包与 execute 上下文同步重写 3. analyze_health 升级为主动优化唯一裁判入口——补齐 rhythm / tightness / profile / feasibility 指标、候选扫描与复诊打分、停滞信号、forced imperfection 判定，并把连续优化状态写回运行态 4. 任务类能力并入新 Agent 执行链——新增 upsert_task_class 写工具与启动注入事务写入；任务类模型补充学科画像与整天屏蔽配置，粗排支持 excluded_days_of_week，steady 策略改为基于目标位置/单日负载/分散度/缓冲的候选打分 5. 运行态与路由补齐优化模式语义——新增 active tool domain/packs、pending context hook、active optimize only、taskclass 写入回盘快照；区分 first_full / global_reopt / local_adjust，并完善首次粗排后默认 refine 的判定前端： 6. 助手时间线渲染细化——推理内容改为独立 reasoning block，支持与工具/状态/正文按时序交错展示，自动收口折叠，修正 confirm reject 恢复动作仓库： 7. newAgent 文档整体迁入 docs/backend，补充主动优化执行规划与顺序约束拆解文档，删除旧调试日志文件 PS:这次科研了2天，总算是有些进展了——LLM永远只适合做选择题、判断题，不适合做开放创新题。
2026-04-27 01:09:37 +08:00
parent 04b5836b39
commit 66c06eed0a
60 changed files with 9163 additions and 1819 deletions
--- a/backend/newAgent/tools/schedule/analyze_health_decision_v2.go
+++ b/backend/newAgent/tools/schedule/analyze_health_decision_v2.go
@@ -0,0 +1,124 @@
+package schedule
+
+import "strings"
+
+// buildAnalyzeHealthDecisionV2 生成 analyze_health 在主动优化场景下的最终裁决。
+//
+// 职责边界：
+// 1. 先尊重 base 层的判断：只有 base 明确允许继续优化时，才进入候选枚举。
+// 2. 候选只来自后端已经验证合法、并且复诊后确实变好的 move/swap 方案。
+// 3. 若没有真正改善的候选，则明确返回 close，避免把 LLM 推回开放式全窗搜索。
+func buildAnalyzeHealthDecisionV2(
+	state *ScheduleState,
+	snapshot analyzeHealthSnapshot,
+) analyzeHealthDecision {
+	base := buildAnalyzeHealthDecisionBase(state, snapshot)
+	decision := analyzeHealthDecision{
+		ShouldContinueOptimize: base.ShouldContinueOptimize,
+		PrimaryProblem:         base.PrimaryProblem,
+		ProblemScope:           base.ProblemScope,
+		IsForcedImperfection:   base.IsForcedImperfection,
+		RecommendedOperation:   base.RecommendedOperation,
+		ImprovementSignal: buildHealthImprovementSignal(
+			snapshot.Rhythm,
+			snapshot.Tightness,
+			base.ProblemScope,
+			base.RecommendedOperation,
+			snapshot.Profile,
+			snapshot.Feasibility,
+		),
+	}
+
+	if !shouldEnterHealthCandidateLoop(base) {
+		decision.Candidates = []analyzeHealthCandidate{
+			buildHealthCloseCandidate("保持当前安排并收口：当前不需要再进入主动优化候选。", snapshot, base),
+		}
+		decision.ShouldContinueOptimize = false
+		return decision
+	}
+
+	bestScan, ok := findBestHealthProblemScanResult(state, snapshot)
+	if !ok || bestScan.Problem.Kind != healthProblemHeavyAdjacent || bestScan.Problem.Pair == nil {
+		decision.Candidates = []analyzeHealthCandidate{
+			buildHealthCloseCandidate("保持当前安排并收口：当前没有值得继续处理的局部认知问题。", snapshot, base),
+		}
+		decision.ShouldContinueOptimize = false
+		decision.PrimaryProblem = "当前没有发现值得继续处理的局部认知问题"
+		decision.ProblemScope = nil
+		decision.RecommendedOperation = "close"
+		if snapshot.Tightness.TightnessLevel == "locked" || snapshot.Tightness.TightnessLevel == "tight" {
+			decision.IsForcedImperfection = true
+		}
+		decision.ImprovementSignal = buildHealthImprovementSignal(
+			snapshot.Rhythm,
+			snapshot.Tightness,
+			decision.ProblemScope,
+			decision.RecommendedOperation,
+			snapshot.Profile,
+			snapshot.Feasibility,
+		)
+		return decision
+	}
+
+	decision.PrimaryProblem = bestScan.Problem.Summary
+	decision.ProblemScope = bestScan.Problem.Scope
+	decision.Candidates = append(decision.Candidates, bestScan.Candidates...)
+	decision.Candidates = append(decision.Candidates,
+		buildHealthCloseCandidate("如果不想继续挪动，也可以保持当前安排并直接收口。", snapshot, base),
+	)
+	decision.ShouldContinueOptimize = true
+	decision.RecommendedOperation = strings.TrimSpace(bestScan.Candidates[0].Tool)
+	decision.ImprovementSignal = buildHealthImprovementSignal(
+		snapshot.Rhythm,
+		snapshot.Tightness,
+		decision.ProblemScope,
+		decision.RecommendedOperation,
+		snapshot.Profile,
+		snapshot.Feasibility,
+	)
+	return decision
+}
+
+// findBestHealthProblemScanResult 每轮重扫所有 heavy_adjacent 天，并选出当前收益最高的一天。
+//
+// 步骤化说明：
+// 1. 先收集所有仍需关注的 heavy_adjacent 天；这里只扫描问题天，不改候选类型。
+// 2. 再对每一天复用现有单天候选试算逻辑，保持“合法且复诊后确实变好”这一过滤语义不变。
+// 3. 最后只返回收益最高且达到最小阈值的一天；最终 decision.candidates 仍只来自这一天天然候选集。
+func findBestHealthProblemScanResult(
+	state *ScheduleState,
+	snapshot analyzeHealthSnapshot,
+) (analyzeHealthProblemScanResult, bool) {
+	problems := collectRepairableHeavyAdjacentProblems(state, snapshot)
+	if len(problems) == 0 {
+		return analyzeHealthProblemScanResult{}, false
+	}
+
+	results := make([]analyzeHealthProblemScanResult, 0, len(problems))
+	for _, problem := range problems {
+		scan, ok := buildHealthProblemScanResult(state, snapshot, problem)
+		if !ok {
+			continue
+		}
+		results = append(results, scan)
+	}
+	return selectBestHealthProblemScanResult(results)
+}
+
+// shouldEnterHealthCandidateLoop 判断本轮是否应进入“候选式主动优化”。
+//
+// 说明：
+// 1. 只有 base 已判定“值得继续优化”时才放行。
+// 2. 当前主动优化闭环只接受 move / swap 两类操作，其它动作不进入候选生成。
+// 3. 这样可以挡住 “ask_user / close / forced imperfection” 被后续枚举误覆盖的问题。
+func shouldEnterHealthCandidateLoop(base analyzeHealthDecisionBase) bool {
+	if !base.ShouldContinueOptimize {
+		return false
+	}
+	switch strings.TrimSpace(base.RecommendedOperation) {
+	case "move", "swap":
+		return true
+	default:
+		return false
+	}
+}