feat(aihr): export human calibration evidence
This commit is contained in:
+17
-3
@@ -187,8 +187,16 @@ public class AihrPracticeSeedService {
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(SELECT GROUP_CONCAT(DISTINCT o.project_code ORDER BY o.project_code SEPARATOR ';')
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FROM safe_org o WHERE o.ext_party_id = s.formal_ext_party_id) AS project_codes,
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s.session_id, s.trainee_name, s.scenario_name, s.total_score, s.status,
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s.finished_time, s.summary, s.review_advice, s.incentive_point
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s.finished_time, s.summary, s.review_advice, s.incentive_point,
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s.total_score AS ai_score,
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c.original_score AS calibration_original_score,
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c.corrected_score AS human_score,
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c.reviewer AS calibration_reviewer,
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c.create_time AS calibration_time,
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COALESCE(c.corrected_score, s.total_score) AS final_adopted_score
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FROM formal_sessions s
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LEFT JOIN latest_calibration l ON l.session_id = s.session_id
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LEFT JOIN aihr_practice_calibration c ON c.id = l.id
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ORDER BY s.finished_time DESC, s.id DESC
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""";
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@@ -1056,7 +1064,7 @@ public class AihrPracticeSeedService {
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appendCsvRow(csv, "sop_answer_usable_ready", sopReady);
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appendCsvRow(csv, "pilot_metric_ready", metrics.scenarioCount() >= 12 && completionReady && calibrationReady && sopReady);
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csv.append('\n');
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appendCsvRow(csv, "formal_ext_party_id", "project_codes", "session_id", "trainee", "scene", "score", "status", "finished_time", "summary", "review_advice", "incentive_point");
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appendCsvRow(csv, "formal_ext_party_id", "project_codes", "session_id", "trainee", "scene", "score", "status", "finished_time", "summary", "review_advice", "incentive_point", "ai_score", "calibration_original_score", "human_score", "calibration_reviewer", "calibration_time", "final_adopted_score");
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sessionRows.forEach(row -> appendCsvRow(
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csv,
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row.get("formal_ext_party_id"),
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@@ -1069,7 +1077,13 @@ public class AihrPracticeSeedService {
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row.get("finished_time"),
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row.get("summary"),
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row.get("review_advice"),
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row.get("incentive_point")
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row.get("incentive_point"),
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row.get("ai_score"),
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row.get("calibration_original_score"),
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row.get("human_score"),
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row.get("calibration_reviewer"),
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row.get("calibration_time"),
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row.get("final_adopted_score")
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));
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return csv.toString();
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}
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+11
-1
@@ -82,8 +82,12 @@ public class AihrPracticeSeedServiceTest {
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assertTrue(jdbcTemplate.metricsSql.contains("HAVING COUNT(*) >= 10"));
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assertTrue(jdbcTemplate.metricsSql.contains("JOIN formal_sessions s ON s.session_id = c.session_id"));
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assertTrue(jdbcTemplate.sessionRowsSql.contains("JOIN org_identity o ON o.identity_key = s.ext_party_id"));
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assertTrue(jdbcTemplate.sessionRowsSql.contains("LEFT JOIN latest_calibration l ON l.session_id = s.session_id"));
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assertTrue(csv.contains("\"training_count\",\"1\""));
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assertTrue(csv.contains("\"FORMAL-1\",\"P1\",\"session-1\""));
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assertTrue(csv.contains("\"ai_score\",\"calibration_original_score\",\"human_score\""));
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assertTrue(csv.contains("\"3\",\"86\",\"86\",\"78\",\"主管甲\""));
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assertTrue(csv.contains("\"78\"\n"));
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}
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@Test
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@@ -618,7 +622,13 @@ public class AihrPracticeSeedServiceTest {
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Map.entry("finished_time", Timestamp.valueOf("2026-07-08 10:00:00")),
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Map.entry("summary", "正式试点记录"),
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Map.entry("review_advice", "保持回访"),
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Map.entry("incentive_point", 3)
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Map.entry("incentive_point", 3),
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Map.entry("ai_score", 86),
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Map.entry("calibration_original_score", 86),
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Map.entry("human_score", 78),
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Map.entry("calibration_reviewer", "主管甲"),
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Map.entry("calibration_time", Timestamp.valueOf("2026-07-08 11:00:00")),
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Map.entry("final_adopted_score", 78)
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));
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}
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}
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@@ -8,7 +8,7 @@
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| AI面试 `/recruit/interview` | `POST /api/recruit/interview/start`、`/answer`、`/finish` | 已接入真实模型优先链路：配置 chat 模型后 `/start` 动态生成面试题，`/finish` 按前端提交的真实回答做结构化评分；未配置模型时使用本地 Rubric 估分，不返回固定候选人分数 |
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| 三角色对练 `/train/practice` | `POST /api/train/practice/start`、`/turn`、`/finish` | 已接入编排 API；数据库启用 chat 模型后，`/turn` 客户回复按人设走真 LLM 生成（seed 剧本作剧情锚点），`/finish` 走单次 temperature=0 结构化评分（4 维分+导师改写+点评）；模型未配置或调用失败自动回退 seed，契约不变 |
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| 正式试点数据导出 | `GET /api/train/practice/export?startDate=YYYY-MM-DD&endDate=YYYY-MM-DD` | 起止日期必填且包含结束日；只统计窗口内能通过唯一手机号或外部 ID 映射到在职组织快照的正式会话，排除重复手机号和身份碰撞。完训定义为每人至少 10 次，校准必须关联同一窗口内正式会话；CSV 同时给出校准命中数和 SOP 可用数，避免用四舍五入后的比率反推门禁状态；汇总和明细均携带正式人员及项目口径，不混入历史 seed/开发身份 |
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| 正式试点数据导出 | `GET /api/train/practice/export?startDate=YYYY-MM-DD&endDate=YYYY-MM-DD` | 起止日期必填且包含结束日；只统计窗口内能通过唯一手机号或外部 ID 映射到在职组织快照的正式会话，排除重复手机号和身份碰撞。完训定义为每人至少 10 次，校准必须关联同一窗口内正式会话；CSV 同时给出校准命中数、SOP 可用数，以及明细级 AI 分、人工校准分、校准人、校准时间和最终采用分，避免用四舍五入后的比率反推门禁状态；汇总和明细均携带正式人员及项目口径，不混入历史 seed/开发身份 |
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| 对练语音 | `POST /api/ai/asr`（multipart 字段 `file`，≤5MB）、`POST /api/ai/tts`（JSON `{text≤300字, voice}`，返回 `{audioUrl}` base64 dataURL） | 走 OpenAI-compatible audio 接口（如硅基流动 SenseVoice/CosyVoice2）；模型管理需启用 `category=asr/tts` 配置；移动端优先用浏览器录音，`getUserMedia/MediaRecorder` 不可用或麦克风权限失败时，用 `audio/*` file input 选择/录制音频后继续调同一 ASR 接口；ASR/TTS 未配置或失败返回 fail，前端降级文本 |
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| 案例沉淀 `/knowledge/cases` | `POST /api/knowledge/case/upload`、`/organize`、`/curate` | `/upload` 改为 multipart 真实语音上传并走 ASR；`/organize` 用真实转写调 chat 模型整理案例，未配置模型时按真实 transcript 本地结构化；不再用固定样例转写冒充成功 |
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| SOP知识库 `/knowledge/sop` | `POST /api/knowledge/search`、`GET /api/knowledge/position-sop`、`POST /api/knowledge/doc/upload` | 已接入 MySQL Fulltext + Qdrant 混合召回、岗位学习适配摘要、OSS-first 文档上传、txt/md/PDF/Word/Excel/PPT 解析和 embedding 写入，失败回退 seed；`position-sop` 目前只返回一期生活顾问学习导航，不代表正式上岗资格 |
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@@ -27,7 +27,7 @@
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| 正式主管身份、项目范围和数据权限 | 5.4、L1-B、G3 | 代码已有 `aihr_org_snapshot`、岗位/项目过滤和主管接口；移动端角色查询失败已改为安全降级员工端，但正式上游快照的手机号关联仍不足，线上视觉测试不能替代真实主管身份验证 | 用正式组织同步 dry-run 先核对项目、岗位、`ext_party_id`、手机号；只在上游数据合规后执行正式同步 |
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| M5 正式试点证据 | L1-B/C/D、G1/G3/G4 | 本地严格门禁和导出能力已存在；本地样本不能代表正式窗口，不能用 seed/烟测数据替代 | 固定 1–2 个住宅项目和窗口，按唯一在职身份采集训练、人工校准、SOP 评审、完训率和满意度 |
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| AI 分数与人工复核边界 | 4.1、4.5、G1/G2 | 面试和对练已有人工复核/校准入口；仍需在正式试点报告中保留“AI 建议、人工可否决”的证据 | 导出中保留 AI 分、人工分、复核人、复核时间和最终采用值 |
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| AI 分数与人工复核边界 | 4.1、4.5、G1/G2 | 面试和对练已有人工复核/校准入口；正式试点 CSV 已补明细级 AI 分、人工校准分、校准人、校准时间和最终采用分；正式试点仍需用真实窗口验证人工可否决证据 | 在正式试点报告中抽查校准记录与最终采用值，确认人工分可覆盖 AI 建议且不直接挂钩绩效/晋升 |
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### P1：一期 BRD 功能仍未形成完整业务闭环
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@@ -346,6 +346,8 @@ contains frontend/src/views/train/practice.vue "导出试点CSV"
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contains frontend/src/views/train/reviews.vue "导出试点 CSV"
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contains frontend/src/views/train/reviews.vue "人工校准总分"
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contains frontend/src/api/aihr/practice.ts "/api/train/practice/export"
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contains backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/service/AihrPracticeSeedService.java "final_adopted_score"
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contains backend/script/sql/aihr_practice_mysql8.sql "aihr_practice_calibration"
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contains frontend/src/api/aihr/practice.ts '/api/train/practice/scenarios/${id}/enabled'
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contains backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/controller/AihrPracticeController.java "/scenarios/{id}/enabled"
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contains backend/ruoyi-modules/ruoyi-aihr/src/main/java/org/dromara/aihr/domain/AihrSopDto.java "String position"
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@@ -83,7 +83,13 @@ const DETAIL_HEADERS = [
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'finished_time',
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'summary',
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'review_advice',
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'incentive_point'
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'incentive_point',
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'ai_score',
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'calibration_original_score',
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'human_score',
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'calibration_reviewer',
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'calibration_time',
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'final_adopted_score'
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];
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function fail(message) {
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@@ -280,6 +286,23 @@ export function verifyPilotExportContract({ csv, startDate, endDate, contentDisp
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}
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const score = Number(row[5]);
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if (!Number.isFinite(score) || score < 0 || score > 100) fail(`detail score ${row[5] || '<missing>'} is invalid`);
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if (Number(row[11]) !== score) fail(`detail ai_score ${row[11] || '<missing>'} does not match score ${row[5]}`);
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const calibrationOriginal = row[12] === '' ? null : Number(row[12]);
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const humanScore = row[13] === '' ? null : Number(row[13]);
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for (const [label, value] of [['calibration_original_score', calibrationOriginal], ['human_score', humanScore]]) {
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if (value !== null && (!Number.isFinite(value) || value < 0 || value > 100)) {
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fail(`detail ${label} ${value} is invalid`);
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}
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}
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const finalAdopted = Number(row[16]);
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if (!Number.isFinite(finalAdopted) || finalAdopted < 0 || finalAdopted > 100) {
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fail(`detail final_adopted_score ${row[16] || '<missing>'} is invalid`);
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}
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if (humanScore === null) {
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if (row[14] || row[15] || finalAdopted !== score) fail('detail without human score has inconsistent calibration evidence');
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} else if (!row[14] || !row[15] || finalAdopted !== humanScore) {
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fail('detail human score is missing reviewer/time or final adopted score');
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}
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scores.push(score);
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if (row[6] === '已复盘') reviewed += 1;
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if (score < 80 || row[6] === '待复盘') alerts += 1;
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@@ -44,8 +44,8 @@ function validCsv() {
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pilot_metric_ready: 'false'
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};
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const lines = ['metric,value', ...Object.entries(metrics).map(([key, value]) => `${key},${value}`)];
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lines.push('', 'formal_ext_party_id,project_codes,session_id,trainee,scene,score,status,finished_time,summary,review_advice,incentive_point');
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lines.push('FORMAL-1,P1,session-1,员工甲,投诉处理,86,已复盘,2026-07-08 10:00:00,正式记录,保持回访,3');
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lines.push('', 'formal_ext_party_id,project_codes,session_id,trainee,scene,score,status,finished_time,summary,review_advice,incentive_point,ai_score,calibration_original_score,human_score,calibration_reviewer,calibration_time,final_adopted_score');
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lines.push('FORMAL-1,P1,session-1,员工甲,投诉处理,86,已复盘,2026-07-08 10:00:00,正式记录,保持回访,3,86,86,78,主管甲,2026-07-08 11:00:00,78');
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return lines.join('\n');
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}
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