feat(ssa): Complete Phase I-IV intelligent dialogue and tool system development

Phase I - Session Blackboard + READ Layer:
- SessionBlackboardService with Postgres-Only cache
- DataProfileService for data overview generation
- PicoInferenceService for LLM-driven PICO extraction
- Frontend DataContextCard and VariableDictionaryPanel
- E2E tests: 31/31 passed

Phase II - Conversation Layer LLM + Intent Router:
- ConversationService with SSE streaming
- IntentRouterService (rule-first + LLM fallback, 6 intents)
- SystemPromptService with 6-segment dynamic assembly
- TokenTruncationService for context management
- ChatHandlerService as unified chat entry
- Frontend SSAChatPane and useSSAChat hook
- E2E tests: 38/38 passed

Phase III - Method Consultation + AskUser Standardization:
- ToolRegistryService with Repository Pattern
- MethodConsultService with DecisionTable + LLM enhancement
- AskUserService with global interrupt handling
- Frontend AskUserCard component
- E2E tests: 13/13 passed

Phase IV - Dialogue-Driven Analysis + QPER Integration:
- ToolOrchestratorService (plan/execute/report)
- analysis_plan SSE event for WorkflowPlan transmission
- Dual-channel confirmation (ask_user card + workspace button)
- PICO as optional hint for LLM parsing
- E2E tests: 25/25 passed

R Statistics Service:
- 5 new R tools: anova_one, baseline_table, fisher, linear_reg, wilcoxon
- Enhanced guardrails and block helpers
- Comprehensive test suite (run_all_tools_test.js)

Documentation:
- Updated system status document (v5.9)
- Updated SSA module status and development plan (v1.8)

Total E2E: 107/107 passed (Phase I: 31, Phase II: 38, Phase III: 13, Phase IV: 25)

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/src/modules/ssa/services/TokenTruncationService.ts

@@ -0,0 +1,204 @@
+/**
+ * Phase I — Token 截断服务
+ *
+ * 在将 SessionBlackboard 数据注入 LLM Prompt 之前，
+ * 按优先级策略裁剪 payload 以适配模型上下文窗口。
+ *
+ * 裁剪策略（按优先级从低到高保留）：
+ *   1. 完整变量字典 → 仅保留非 isIdLike 的变量
+ *   2. topValues 列表 → 截断到 top 5
+ *   3. 数值列详细统计 → 保留 mean/std/median + 去掉 skewness/kurtosis
+ *   4. normalityTests → 仅保留非正态的变量
+ *   5. picoInference → 始终保留（最高优先级）
+ *   6. fiveSectionReport.content → 若超限则截断到前 500 字符
+ *
+ * 预估 token 使用简易方式: 1 中文字 ≈ 2 tokens, 1 英文词 ≈ 1.3 tokens
+ * 通过 JSON.stringify 长度 / 2 作为粗略上界。
+ */
+
+import { logger } from '../../../common/logging/index.js';
+import type {
+  SessionBlackboard,
+  DataOverview,
+  VariableDictEntry,
+  FiveSectionReport,
+} from '../types/session-blackboard.types.js';
+
+export interface TruncationOptions {
+  maxTokens?: number;
+  strategy?: 'aggressive' | 'balanced' | 'minimal';
+}
+
+interface TruncatedContext {
+  overview: string;
+  variables: string;
+  pico: string;
+  report: string;
+  estimatedTokens: number;
+}
+
+const DEFAULT_MAX_TOKENS = 3000;
+
+export class TokenTruncationService {
+
+  /**
+   * 将 SessionBlackboard 截断为可注入 Prompt 的紧凑文本。
+   */
+  truncate(
+    blackboard: SessionBlackboard,
+    options: TruncationOptions = {},
+  ): TruncatedContext {
+    const maxTokens = options.maxTokens ?? DEFAULT_MAX_TOKENS;
+    const strategy = options.strategy ?? 'balanced';
+
+    logger.debug('[SSA:TokenTrunc] Truncating context', {
+      sessionId: blackboard.sessionId,
+      maxTokens,
+      strategy,
+    });
+
+    const pico = this.formatPico(blackboard);
+    const overview = this.formatOverview(blackboard.dataOverview, strategy);
+    const variables = this.formatVariables(blackboard.variableDictionary, strategy);
+    const report = this.formatReport(blackboard, strategy);
+
+    let ctx: TruncatedContext = {
+      pico,
+      overview,
+      variables,
+      report,
+      estimatedTokens: 0,
+    };
+
+    ctx.estimatedTokens = this.estimateTokens(ctx);
+
+    if (ctx.estimatedTokens > maxTokens) {
+      ctx = this.applyAggressiveTruncation(ctx, blackboard, maxTokens);
+    }
+
+    logger.debug('[SSA:TokenTrunc] Truncation complete', {
+      estimatedTokens: ctx.estimatedTokens,
+      maxTokens,
+    });
+
+    return ctx;
+  }
+
+  /**
+   * 一次性生成可直接拼入 system prompt 的字符串。
+   */
+  toPromptString(ctx: TruncatedContext): string {
+    const parts: string[] = [];
+
+    if (ctx.pico) parts.push(`## PICO 结构\n${ctx.pico}`);
+    if (ctx.overview) parts.push(`## 数据概览\n${ctx.overview}`);
+    if (ctx.variables) parts.push(`## 变量列表\n${ctx.variables}`);
+    if (ctx.report) parts.push(`## 数据诊断摘要\n${ctx.report}`);
+
+    return parts.join('\n\n');
+  }
+
+  private formatPico(bb: SessionBlackboard): string {
+    const p = bb.picoInference;
+    if (!p) return '';
+    const lines = [];
+    if (p.population) lines.push(`P (人群): ${p.population}`);
+    if (p.intervention) lines.push(`I (干预): ${p.intervention}`);
+    if (p.comparison) lines.push(`C (对照): ${p.comparison}`);
+    if (p.outcome) lines.push(`O (结局): ${p.outcome}`);
+    return lines.join('\n');
+  }
+
+  private formatOverview(ov: DataOverview | null, strategy: string): string {
+    if (!ov) return '';
+    const s = ov.profile.summary;
+    let text = `${s.totalRows} 行 × ${s.totalColumns} 列, 缺失率 ${s.overallMissingRate}%, 完整病例 ${ov.completeCaseCount}`;
+
+    if (strategy !== 'aggressive' && ov.normalityTests?.length) {
+      const nonNormal = ov.normalityTests.filter(t => !t.isNormal).map(t => t.variable);
+      if (nonNormal.length > 0) {
+        text += `\n非正态: ${nonNormal.join(', ')}`;
+      }
+    }
+
+    return text;
+  }
+
+  private formatVariables(dict: VariableDictEntry[], strategy: string): string {
+    let vars = dict.filter(v => !v.isIdLike);
+
+    if (strategy === 'aggressive') {
+      vars = vars.slice(0, 15);
+    }
+
+    return vars.map(v => {
+      const type = v.confirmedType ?? v.inferredType;
+      const label = v.label ? ` "${v.label}"` : '';
+      const role = v.picoRole ? ` [${v.picoRole}]` : '';
+      return `- ${v.name}: ${type}${label}${role}`;
+    }).join('\n');
+  }
+
+  private formatReport(bb: SessionBlackboard, strategy: string): string {
+    const report = bb.dataOverview
+      ? this.buildReportSummary(bb.dataOverview)
+      : '';
+
+    if (strategy === 'aggressive' && report.length > 500) {
+      return report.slice(0, 500) + '...';
+    }
+    return report;
+  }
+
+  private buildReportSummary(ov: DataOverview): string {
+    const s = ov.profile.summary;
+    const lines: string[] = [];
+
+    const missingCols = ov.profile.columns.filter(c => c.missingCount > 0);
+    if (missingCols.length > 0) {
+      lines.push(`缺失变量(${missingCols.length}): ${missingCols.map(c => c.name).join(', ')}`);
+    }
+
+    const outlierCols = ov.profile.columns.filter(c => (c as any).outlierCount > 0);
+    if (outlierCols.length > 0) {
+      lines.push(`异常值变量(${outlierCols.length}): ${outlierCols.map(c => c.name).join(', ')}`);
+    }
+
+    const catCount = s.categoricalColumns;
+    const numCount = s.numericColumns;
+    lines.push(`类型: 数值${numCount} + 分类${catCount}`);
+
+    return lines.join('\n');
+  }
+
+  private estimateTokens(ctx: TruncatedContext): number {
+    const total = ctx.pico.length + ctx.overview.length + ctx.variables.length + ctx.report.length;
+    return Math.ceil(total / 2);
+  }
+
+  private applyAggressiveTruncation(
+    ctx: TruncatedContext,
+    bb: SessionBlackboard,
+    maxTokens: number,
+  ): TruncatedContext {
+    const result = { ...ctx };
+
+    result.report = result.report.length > 300 ? result.report.slice(0, 300) + '...' : result.report;
+
+    let vars = bb.variableDictionary.filter(v => !v.isIdLike);
+    if (vars.length > 10) {
+      const picoVars = vars.filter(v => v.picoRole);
+      const others = vars.filter(v => !v.picoRole).slice(0, 10 - picoVars.length);
+      vars = [...picoVars, ...others];
+    }
+    result.variables = vars.map(v => {
+      const type = v.confirmedType ?? v.inferredType;
+      return `- ${v.name}: ${type}`;
+    }).join('\n');
+
+    result.estimatedTokens = this.estimateTokens(result);
+    return result;
+  }
+}
+
+export const tokenTruncationService = new TokenTruncationService();