feat(ssa): Complete QPER architecture - Query, Planner, Execute, Reflection layers

Implement the full QPER intelligent analysis pipeline:

- Phase E+: Block-based standardization for all 7 R tools, DynamicReport renderer, Word export enhancement

- Phase Q: LLM intent parsing with dynamic Zod validation against real column names, ClarificationCard component, DataProfile is_id_like tagging

- Phase P: ConfigLoader with Zod schema validation and hot-reload API, DecisionTableService (4-dimension matching), FlowTemplateService with EPV protection, PlannedTrace audit output

- Phase R: ReflectionService with statistical slot injection, sensitivity analysis conflict rules, ConclusionReport with section reveal animation, conclusion caching API, graceful R error classification

End-to-end test: 40/40 passed across two complete analysis scenarios.

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

r-statistics-service/tools/logistic_binary.R

@@ -254,7 +254,59 @@ cat("AIC:", AIC(model), "\\n")
   
   # ===== 返回结果 =====
   log_add("分析完成")
-  
+
+  # ===== 构建 report_blocks =====
+  blocks <- list()
+
+  # Block 1: 模型概况
+  blocks[[length(blocks) + 1]] <- make_kv_block(list(
+    "模型公式" = formula_str,
+    "观测数" = as.character(nrow(df)),
+    "预测变量数" = as.character(n_predictors),
+    "AIC" = as.character(round(aic, 2)),
+    "Nagelkerke R²" = as.character(round(r2_nagelkerke, 4)),
+    "EPV" = as.character(round(epv, 1))
+  ), title = "模型概况")
+
+  # Block 2: 回归系数表
+  coef_headers <- c("变量", "OR", "95% CI", "P 值", "显著性")
+  coef_rows <- lapply(coefficients_list, function(row) {
+    ci_str <- sprintf("[%.3f, %.3f]", row$ci_lower, row$ci_upper)
+    sig <- if (row$significant) "*" else ""
+    c(row$variable, as.character(row$OR), ci_str, row$p_value_fmt, sig)
+  })
+  blocks[[length(blocks) + 1]] <- make_table_block(coef_headers, coef_rows, title = "回归系数表", footnote = "* P < 0.05")
+
+  # Block 3: VIF 表（如存在）
+  if (!is.null(vif_results) && length(vif_results) > 0) {
+    vif_headers <- c("变量", "VIF")
+    vif_rows <- lapply(vif_results, function(row) c(row$variable, as.character(row$vif)))
+    blocks[[length(blocks) + 1]] <- make_table_block(vif_headers, vif_rows, title = "方差膨胀因子 (VIF)")
+  }
+
+  # Block 4: 森林图（如存在）
+  if (!is.null(plot_base64)) {
+    blocks[[length(blocks) + 1]] <- make_image_block(plot_base64, title = "森林图", alt = "Odds Ratios Forest Plot")
+  }
+
+  # Block 5: 结论摘要
+  sig_vars <- sapply(coefficients_list, function(r) if (r$variable != "(Intercept)" && r$significant) r$variable else NULL)
+  sig_vars <- unlist(sig_vars[!sapply(sig_vars, is.null)])
+  conclusion_lines <- c(
+    glue("模型拟合指标：AIC = {round(aic, 2)}，Nagelkerke R² = {round(r2_nagelkerke, 4)}。"),
+    ""
+  )
+  if (length(sig_vars) > 0) {
+    conclusion_lines <- c(conclusion_lines,
+      glue("在 α = 0.05 水平下，以下变量具有统计学意义：**{paste(sig_vars, collapse = '**, **')}**。"),
+      ""
+    )
+  } else {
+    conclusion_lines <- c(conclusion_lines, "在 α = 0.05 水平下，无预测变量达到统计学意义。", "")
+  }
+  conclusion_lines <- c(conclusion_lines, glue("EPV = {round(epv, 1)}（建议 ≥ 10）。"))
+  blocks[[length(blocks) + 1]] <- make_markdown_block(paste(conclusion_lines, collapse = "\n"), title = "结论摘要")
+
   return(list(
     status = "success",
     message = "分析完成",
@@ -274,6 +326,7 @@ cat("AIC:", AIC(model), "\\n")
       vif = vif_results,
       epv = jsonlite::unbox(round(epv, 1))
     ),
+    report_blocks = blocks,
     plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
     trace_log = logs,
     reproducible_code = as.character(reproducible_code)