新增功能：统计与分析

lab-service-api/lab-lims-api/src/main/java/org/springblade/lims/entry/StatsSampleTag.java

@@ -0,0 +1,18 @@
+package org.springblade.lims.entry;
+
+import com.baomidou.mybatisplus.annotation.TableName;
+import lombok.Data;
+import org.springblade.core.mp.base.BaseEntity;
+
+import java.io.Serializable;
+
+/**
+ */
+@Data
+@SuppressWarnings("all")
+@TableName("t_stats_sample_tag")
+public class StatsSampleTag extends BaseEntity implements Serializable {
+
+    private Long sampleSetId;
+    private String tagName;
+}

lab-service/lab-lims/src/main/java/org/springblade/lims/LimsApplication.java

@@ -6,6 +6,7 @@ import org.springblade.core.cloud.feign.EnableBladeFeign;
 import org.springblade.core.launch.BladeApplication;
 
 import org.springframework.cloud.client.SpringCloudApplication;
+import org.springframework.context.annotation.ComponentScan;
 import org.springframework.scheduling.annotation.EnableScheduling;
 
 /**
@@ -15,6 +16,7 @@ import org.springframework.scheduling.annotation.EnableScheduling;
  */
 @EnableBladeFeign
 @SpringCloudApplication
+@ComponentScan({"org.springblade.lims", "org.springblade.stats"})
 @EnableScheduling
 public class LimsApplication {
 

lab-service/lab-lims/src/main/java/org/springblade/lims/excel/StatsAnalysisExportExcel.java

@@ -0,0 +1,59 @@
+package org.springblade.lims.excel;
+
+import com.alibaba.excel.annotation.ExcelProperty;
+import lombok.Data;
+
+import java.io.Serializable;
+
+/**
+ * 统计分析导出 Excel DTO
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+@Data
+public class StatsAnalysisExportExcel implements Serializable {
+	private static final long serialVersionUID = 1L;
+
+	@ExcelProperty("地区/场")
+	private String regionName;
+
+	@ExcelProperty("采样数")
+	private Integer totalSamples;
+
+	@ExcelProperty("阳性数")
+	private Integer positiveCount;
+
+	@ExcelProperty("阳性率")
+	private String positiveRate;
+
+	@ExcelProperty("均值(Mean)")
+	private String meanValue;
+
+	@ExcelProperty("标准差(SD)")
+	private String stdDev;
+
+	@ExcelProperty("变异系数(CV)")
+	private String cv;
+
+	@ExcelProperty("标准误(SE)")
+	private String se;
+
+	@ExcelProperty("受理编号")
+	private String acceptanceNum;
+
+	@ExcelProperty("动物种类")
+	private String animalType;
+
+	@ExcelProperty("免疫情况")
+	private String immuneSituation;
+
+	@ExcelProperty("检测项目")
+	private String examineItem;
+
+	@ExcelProperty("检测方法")
+	private String examineMethod;
+
+	@ExcelProperty("样品来源")
+	private String simpleSource;
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/controller/StatsAnalysisController.java

@@ -0,0 +1,93 @@
+package org.springblade.stats.controller;
+
+import com.baomidou.mybatisplus.core.metadata.IPage;
+import io.swagger.annotations.Api;
+import io.swagger.annotations.ApiOperation;
+import io.swagger.annotations.ApiParam;
+import lombok.AllArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.springblade.core.boot.ctrl.BladeController;
+import org.springblade.core.excel.util.ExcelUtil;
+import org.springblade.core.mp.support.Query;
+import org.springblade.core.tool.api.R;
+import org.springblade.lims.excel.StatsAnalysisExportExcel;
+import org.springblade.stats.dto.StatsAnalysisQuery;
+import org.springblade.stats.service.IStatsAnalysisService;
+import org.springblade.stats.util.StatsExcelUtil;
+import org.springblade.stats.vo.StatsAnalysisVO;
+import org.springframework.web.bind.annotation.*;
+
+import javax.servlet.http.HttpServletResponse;
+import java.util.List;
+
+/**
+ * 统计分析 控制器
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+@Slf4j
+@RestController
+@AllArgsConstructor
+@RequestMapping("/stats/analysis")
+@Api(value = "统计分析", tags = "统计分析")
+public class StatsAnalysisController extends BladeController {
+
+	private final IStatsAnalysisService service;
+
+	/**
+	 * 城市级统计列表
+	 */
+	@GetMapping("/list")
+	@ApiOperation(value = "城市级统计列表", notes = "传入筛选条件和分页参数，返回城市级聚合数据")
+	public R<IPage<StatsAnalysisVO>> list(StatsAnalysisQuery query, Query pageQuery) {
+		return R.data(service.queryCityLevel(query, pageQuery));
+	}
+
+	/**
+	 * 区县级统计
+	 */
+	@GetMapping("/district")
+	@ApiOperation(value = "区县级统计", notes = "传入城市编码，返回该城市下各区县的数据")
+	public R<IPage<StatsAnalysisVO>> district(@ApiParam(value = "城市编码") @RequestParam String cityCode,
+											  StatsAnalysisQuery query, Query pageQuery) {
+		return R.data(service.queryDistrictLevel(cityCode, query, pageQuery));
+	}
+
+	/**
+	 * 被检单位级统计
+	 */
+	@GetMapping("/customer")
+	@ApiOperation(value = "被检单位级统计", notes = "传入区县编码，返回该区县下各被检单位的数据")
+	public R<IPage<StatsAnalysisVO>> customer(@ApiParam(value = "区县编码") @RequestParam String districtCode,
+											  StatsAnalysisQuery query, Query pageQuery) {
+		return R.data(service.queryCustomerLevel(districtCode, query, pageQuery));
+	}
+
+	/**
+	 * 整体统计数据（用于 summary cards）
+	 */
+	@GetMapping("/overall")
+	@ApiOperation(value = "整体统计数据", notes = "返回汇总统计数据，用于首页概览卡片展示")
+	public R<StatsAnalysisVO> overall(StatsAnalysisQuery query) {
+		return R.data(service.queryOverallStats(query));
+	}
+
+	/**
+	 * 导出 Excel
+	 */
+	@GetMapping("/export")
+	@ApiOperation(value = "导出Excel", notes = "按指定级别导出统计结果（city/district/customer）")
+	public void export(StatsAnalysisQuery query,
+					   @ApiParam(value = "聚合级别: city/district/customer", required = true) @RequestParam String level,
+					   HttpServletResponse response) {
+		try {
+			List<StatsAnalysisVO> data = service.exportData(query, level);
+			List<StatsAnalysisExportExcel> exportList = StatsExcelUtil.toExportList(data);
+			ExcelUtil.export(response, "统计分析结果", "统计分析", exportList, StatsAnalysisExportExcel.class);
+		} catch (Exception e) {
+			log.error("导出Excel失败", e);
+		}
+	}
+
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/controller/StatsSampleTagController.java

@@ -0,0 +1,80 @@
+package org.springblade.stats.controller;
+
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
+import com.baomidou.mybatisplus.core.metadata.IPage;
+import io.swagger.annotations.Api;
+import io.swagger.annotations.ApiOperation;
+import io.swagger.annotations.ApiParam;
+import lombok.AllArgsConstructor;
+import org.springblade.core.boot.ctrl.BladeController;
+import org.springblade.core.mp.support.Condition;
+import org.springblade.core.mp.support.Query;
+import org.springblade.core.tool.api.R;
+import org.springblade.core.tool.utils.Func;
+import org.springblade.lims.entry.StatsSampleTag;
+import org.springblade.stats.service.IStatsSampleTagService;
+import org.springframework.web.bind.annotation.*;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * 样品标签 控制器
+ *
+ * @author swj
+ * @since 2026-05-29
+ */
+@RestController
+@AllArgsConstructor
+@RequestMapping("/stats/sampleTag")
+@Api(value = "样品标签", tags = "样品标签")
+public class StatsSampleTagController extends BladeController {
+
+	private final IStatsSampleTagService service;
+
+	/**
+	 * 分页列表
+	 */
+	@GetMapping("/list")
+	@ApiOperation(value = "分页", notes = "传入sampleSetId")
+	public R<IPage<StatsSampleTag>> list(@ApiParam(value = "样品集ID") Long sampleSetId, Query query) {
+		LambdaQueryWrapper<StatsSampleTag> wrapper = new LambdaQueryWrapper<StatsSampleTag>()
+			.eq(StatsSampleTag::getSampleSetId, sampleSetId);
+		IPage<StatsSampleTag> page = service.page(Condition.getPage(query), wrapper);
+		return R.data(page);
+	}
+
+	/**
+	 * 新增
+	 */
+	@PostMapping("/save")
+	@ApiOperation(value = "新增", notes = "传入StatsSampleTag")
+	public R save(@RequestBody StatsSampleTag entry) {
+		// 检查重复标签
+		List<StatsSampleTag> existing = service.listBySampleSetId(entry.getSampleSetId());
+		boolean isDuplicate = existing.stream().anyMatch(t -> t.getTagName().equals(entry.getTagName()));
+		if (isDuplicate) {
+			return R.fail("标签已存在");
+		}
+		return R.status(service.save(entry));
+	}
+
+	/**
+	 * 删除
+	 */
+	@PostMapping("/deleteByIds")
+	@ApiOperation(value = "逻辑删除", notes = "传入ids")
+	public R deleteByIds(@ApiParam(value = "主键集合", required = true) @RequestParam String ids) {
+		return R.status(service.deleteLogic(Func.toLongList(ids)));
+	}
+
+	/**
+	 * 标签字典
+	 */
+	@GetMapping("/tagDict")
+	@ApiOperation(value = "标签字典", notes = "返回预定义的标签列表")
+	public R<List<String>> tagDict() {
+		List<String> tagList = Arrays.asList("紧急", "常规", "复检", "重点监控", "可疑");
+		return R.data(tagList);
+	}
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/dto/StatsAnalysisQuery.java

@@ -0,0 +1,26 @@
+package org.springblade.stats.dto;
+
+import lombok.Data;
+import java.io.Serializable;
+
+/**
+ * 统计分析查询 DTO
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+@Data
+public class StatsAnalysisQuery implements Serializable {
+	private static final long serialVersionUID = 1L;
+
+	/** 样品来源 */
+	private String simpleSource;
+	/** 动物种类 */
+	private String animalType;
+	/** 免疫情况 */
+	private String immuneSituation;
+	/** 开始时间 (yyyy-MM-dd) */
+	private String startTime;
+	/** 结束时间 (yyyy-MM-dd) */
+	private String endTime;
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/mapper/StatsSampleTagMapper.java

@@ -0,0 +1,7 @@
+package org.springblade.stats.mapper;
+
+import com.baomidou.mybatisplus.core.mapper.BaseMapper;
+import org.springblade.lims.entry.StatsSampleTag;
+
+public interface StatsSampleTagMapper extends BaseMapper<StatsSampleTag> {
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/service/IStatsAnalysisService.java

@@ -0,0 +1,63 @@
+package org.springblade.stats.service;
+
+import com.baomidou.mybatisplus.core.metadata.IPage;
+import org.springblade.core.mp.support.Query;
+import org.springblade.stats.dto.StatsAnalysisQuery;
+import org.springblade.stats.vo.StatsAnalysisVO;
+
+import java.util.List;
+
+/**
+ * 统计分析服务接口
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+public interface IStatsAnalysisService {
+
+	/**
+	 * 城市级聚合查询
+	 *
+	 * @param query     筛选条件
+	 * @param pageQuery 分页参数
+	 * @return 分页的城市级统计结果
+	 */
+	IPage<StatsAnalysisVO> queryCityLevel(StatsAnalysisQuery query, Query pageQuery);
+
+	/**
+	 * 区县级聚合查询
+	 *
+	 * @param cityCode   城市编码
+	 * @param query      筛选条件
+	 * @param pageQuery  分页参数
+	 * @return 分页的区县级统计结果
+	 */
+	IPage<StatsAnalysisVO> queryDistrictLevel(String cityCode, StatsAnalysisQuery query, Query pageQuery);
+
+	/**
+	 * 被检单位级聚合查询
+	 *
+	 * @param districtCode 区县编码
+	 * @param query        筛选条件
+	 * @param pageQuery    分页参数
+	 * @return 分页的被检单位级统计结果
+	 */
+	IPage<StatsAnalysisVO> queryCustomerLevel(String districtCode, StatsAnalysisQuery query, Query pageQuery);
+
+	/**
+	 * 导出数据（不分页）
+	 *
+	 * @param query 筛选条件
+	 * @param level 聚合级别: city/district/customer
+	 * @return 全部统计结果列表
+	 */
+	List<StatsAnalysisVO> exportData(StatsAnalysisQuery query, String level);
+
+	/**
+	 * 整体统计数据（用于 summary cards）
+	 *
+	 * @param query 筛选条件
+	 * @return 总体统计结果
+	 */
+	StatsAnalysisVO queryOverallStats(StatsAnalysisQuery query);
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/service/IStatsSampleTagService.java

@@ -0,0 +1,23 @@
+package org.springblade.stats.service;
+
+import org.springblade.core.mp.base.BaseService;
+import org.springblade.lims.entry.StatsSampleTag;
+
+import java.util.List;
+
+/**
+ * 样品标签 服务类
+ *
+ * @author swj
+ * @since 2026-05-29
+ */
+public interface IStatsSampleTagService extends BaseService<StatsSampleTag> {
+
+	/**
+	 * 根据样品集ID查询标签列表
+	 *
+	 * @param sampleSetId 样品集ID
+	 * @return 标签列表
+	 */
+	List<StatsSampleTag> listBySampleSetId(Long sampleSetId);
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/service/impl/StatsAnalysisServiceImpl.java

@@ -0,0 +1,442 @@
+package org.springblade.stats.service.impl;
+
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
+import com.baomidou.mybatisplus.core.metadata.IPage;
+import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
+import lombok.AllArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.springblade.core.mp.support.Query;
+import org.springblade.lims.entry.Entrust;
+import org.springblade.lims.entry.Examine;
+import org.springblade.lims.entry.ExamineResult;
+import org.springblade.lims.mapper.EntrustMapper;
+import org.springblade.lims.mapper.ExamineMapper;
+import org.springblade.lims.mapper.ExamineResultMapper;
+import org.springblade.stats.dto.StatsAnalysisQuery;
+import org.springblade.stats.service.IStatsAnalysisService;
+import org.springblade.stats.util.StatsJsonParser;
+import org.springblade.stats.vo.StatsAnalysisVO;
+import org.springframework.stereotype.Service;
+
+import java.text.ParseException;
+import java.text.SimpleDateFormat;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+/**
+ * 统计分析服务实现 — 核心聚合引擎
+ * <p>
+ * 跨 f_entrust_main / f_examine / f_examine_result 三表查询，
+ * 使用 StatsJsonParser 解析 originRecordResult JSON，
+ * 在 Java 端按城市/区县/被检单位分组聚合，计算 Mean / SD / CV / SE。
+ * </p>
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+@Slf4j
+@Service
+@AllArgsConstructor
+public class StatsAnalysisServiceImpl implements IStatsAnalysisService {
+
+	private final EntrustMapper entrustMapper;
+	private final ExamineMapper examineMapper;
+	private final ExamineResultMapper examineResultMapper;
+
+	// ==================== Public API ====================
+
+	@Override
+	public IPage<StatsAnalysisVO> queryCityLevel(StatsAnalysisQuery query, Query pageQuery) {
+		List<RawRecord> records = loadRawRecords(query);
+		Map<String, StatsAccumulator> cityAccum = new LinkedHashMap<>();
+
+		for (RawRecord rec : records) {
+			if (rec.getCity() == null) continue;
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+
+			cityAccum.computeIfAbsent(rec.getCity(), k ->
+				new StatsAccumulator(rec.getCity(), rec.getRegionCode(), "city"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+
+		return paginate(computeStats(cityAccum), pageQuery);
+	}
+
+	@Override
+	public IPage<StatsAnalysisVO> queryDistrictLevel(String cityCode, StatsAnalysisQuery query, Query pageQuery) {
+		List<RawRecord> records = loadRawRecords(query);
+
+		// 筛选出指定城市的记录
+		List<RawRecord> cityRecords = records.stream()
+			.filter(r -> r.getCity() != null && r.getCity().equals(cityCode))
+			.collect(Collectors.toList());
+
+		Map<String, StatsAccumulator> districtAccum = new LinkedHashMap<>();
+		for (RawRecord rec : cityRecords) {
+			// 使用 regionCode 作为区县分组键（实际应用中可按 regionCode 前缀匹配字典表）
+			String districtKey = StringUtils.isNotBlank(rec.getRegionCode()) ? rec.getRegionCode() : "未知区县";
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+
+			districtAccum.computeIfAbsent(districtKey, k ->
+				new StatsAccumulator(districtKey, rec.getRegionCode(), "district"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+
+		return paginate(computeStats(districtAccum), pageQuery);
+	}
+
+	@Override
+	public IPage<StatsAnalysisVO> queryCustomerLevel(String districtCode, StatsAnalysisQuery query, Query pageQuery) {
+		List<RawRecord> records = loadRawRecords(query);
+
+		// 筛选出指定区县的记录
+		List<RawRecord> districtRecords = records.stream()
+			.filter(r -> r.getRegionCode() != null && r.getRegionCode().equals(districtCode))
+			.collect(Collectors.toList());
+
+		Map<String, StatsAccumulator> customerAccum = new LinkedHashMap<>();
+		for (RawRecord rec : districtRecords) {
+			String customerName = StringUtils.isNotBlank(rec.getCustomerName()) ? rec.getCustomerName() : "未知单位";
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+
+			customerAccum.computeIfAbsent(customerName, k ->
+				new StatsAccumulator(customerName, rec.getRegionCode(), "customer"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+
+		return paginate(computeStats(customerAccum), pageQuery);
+	}
+
+	@Override
+	public List<StatsAnalysisVO> exportData(StatsAnalysisQuery query, String level) {
+		List<RawRecord> records = loadRawRecords(query);
+
+		if ("city".equals(level)) {
+			return computeStats(groupByCity(records));
+		} else if ("district".equals(level)) {
+			return computeStats(groupByDistrict(records));
+		} else if ("customer".equals(level)) {
+			return computeStats(groupByCustomer(records));
+		}
+		return new ArrayList<>();
+	}
+
+	@Override
+	public StatsAnalysisVO queryOverallStats(StatsAnalysisQuery query) {
+		List<RawRecord> records = loadRawRecords(query);
+		if (records.isEmpty()) {
+			StatsAnalysisVO vo = new StatsAnalysisVO();
+			vo.setRegionName("总计");
+			vo.setRegionLevel("overall");
+			vo.setTotalSamples(0);
+			vo.setPositiveCount(0);
+			vo.setPositiveRate(0.0);
+			vo.setMeanValue(0.0);
+			vo.setStdDev(0.0);
+			vo.setCv(0.0);
+			vo.setSe(0.0);
+			vo.setHasChildren(true);
+			return vo;
+		}
+
+		int totalSamples = 0;
+		int positiveCount = 0;
+		List<Double> rates = new ArrayList<>();
+
+		for (RawRecord rec : records) {
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			totalSamples += sc.getTotal();
+			positiveCount += sc.getPositive();
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+			rates.add(rate);
+		}
+
+		StatsAnalysisVO vo = new StatsAnalysisVO();
+		vo.setRegionName("总计");
+		vo.setRegionLevel("overall");
+		vo.setTotalSamples(totalSamples);
+		vo.setPositiveCount(positiveCount);
+		vo.setPositiveRate(totalSamples > 0 ? (double) positiveCount / totalSamples * 100 : 0.0);
+
+		fillStatistics(vo, rates);
+		vo.setHasChildren(true);
+		return vo;
+	}
+
+	// ==================== Internal helpers ====================
+
+	/**
+	 * 从数据库加载符合条件的原始记录（三表关联查询，Java 端过滤）
+	 */
+	private List<RawRecord> loadRawRecords(StatsAnalysisQuery query) {
+		// Step 1: 查询符合筛选条件的委托单
+		LambdaQueryWrapper<Entrust> ew = new LambdaQueryWrapper<Entrust>()
+			.eq(Entrust::getIsDeleted, 0)
+			.ne(Entrust::getEntrustStatus, "-1");
+
+		SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd");
+		if (query.getStartTime() != null) {
+			try {
+				ew.ge(Entrust::getSamplingDate, sdf.parse(query.getStartTime()));
+			} catch (ParseException e) {
+				log.warn("无法解析 startTime: {}", query.getStartTime());
+			}
+		}
+		if (query.getEndTime() != null) {
+			try {
+				ew.le(Entrust::getSamplingDate, sdf.parse(query.getEndTime()));
+			} catch (ParseException e) {
+				log.warn("无法解析 endTime: {}", query.getEndTime());
+			}
+		}
+		if (StringUtils.isNotBlank(query.getSimpleSource())) {
+			ew.eq(Entrust::getSimpleSource, query.getSimpleSource());
+		}
+		if (StringUtils.isNotBlank(query.getImmuneSituation())) {
+			ew.eq(Entrust::getImmuneSituation, query.getImmuneSituation());
+		}
+
+		List<Entrust> entrusts = entrustMapper.selectList(ew);
+		if (entrusts.isEmpty()) {
+			return new ArrayList<>();
+		}
+
+		Set<Long> entrustIds = entrusts.stream().map(Entrust::getId).collect(Collectors.toSet());
+
+		// Step 2: 查询已完成的检验记录
+		LambdaQueryWrapper<Examine> examineWrapper = new LambdaQueryWrapper<Examine>()
+			.in(Examine::getEntrustId, entrustIds)
+			.eq(Examine::getIsFinished, "1")
+			.eq(Examine::getIsDeleted, 0);
+
+		// animalType 过滤: Examine.simpleName 格式为 animalType-XXXX (如 牛-口蹄疫)
+		if (StringUtils.isNotBlank(query.getAnimalType())) {
+			examineWrapper.likeRight(Examine::getSimpleName, query.getAnimalType() + "-");
+		}
+
+		List<Examine> examines = examineMapper.selectList(examineWrapper);
+		if (examines.isEmpty()) {
+			return new ArrayList<>();
+		}
+
+		Set<Long> examineIds = examines.stream().map(Examine::getId).collect(Collectors.toSet());
+
+		// Step 3: 查询检验结果
+		List<ExamineResult> results = examineResultMapper.selectList(
+			new LambdaQueryWrapper<ExamineResult>()
+				.in(ExamineResult::getExamineId, examineIds)
+		);
+		if (results.isEmpty()) {
+			return new ArrayList<>();
+		}
+
+		// Step 4: 构建映射关系并组装 RawRecord
+		Map<Long, Examine> examineMap = examines.stream()
+			.collect(Collectors.toMap(Examine::getId, e -> e));
+		Map<Long, Entrust> entrustMap = entrusts.stream()
+			.collect(Collectors.toMap(Entrust::getId, e -> e));
+
+		List<RawRecord> rawRecords = new ArrayList<>();
+		for (ExamineResult er : results) {
+			Examine ex = examineMap.get(er.getExamineId());
+			if (ex == null) continue;
+			Entrust en = entrustMap.get(ex.getEntrustId());
+			if (en == null) continue;
+
+			rawRecords.add(new RawRecord(
+				en.getCity(),
+				en.getRegionCode(),
+				en.getEntrustCustomerName(),
+				er.getOriginRecordResult()
+			));
+		}
+		return rawRecords;
+	}
+
+	/** 按城市分组 */
+	private Map<String, StatsAccumulator> groupByCity(List<RawRecord> records) {
+		Map<String, StatsAccumulator> map = new LinkedHashMap<>();
+		for (RawRecord rec : records) {
+			if (rec.getCity() == null) continue;
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+			map.computeIfAbsent(rec.getCity(), k -> new StatsAccumulator(rec.getCity(), rec.getRegionCode(), "city"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+		return map;
+	}
+
+	/** 按区县分组 */
+	private Map<String, StatsAccumulator> groupByDistrict(List<RawRecord> records) {
+		Map<String, StatsAccumulator> map = new LinkedHashMap<>();
+		for (RawRecord rec : records) {
+			String key = StringUtils.isNotBlank(rec.getRegionCode()) ? rec.getRegionCode() : "未知区县";
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+			map.computeIfAbsent(key, k -> new StatsAccumulator(key, rec.getRegionCode(), "district"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+		return map;
+	}
+
+	/** 按被检单位分组 */
+	private Map<String, StatsAccumulator> groupByCustomer(List<RawRecord> records) {
+		Map<String, StatsAccumulator> map = new LinkedHashMap<>();
+		for (RawRecord rec : records) {
+			String key = StringUtils.isNotBlank(rec.getCustomerName()) ? rec.getCustomerName() : "未知单位";
+			StatsJsonParser.SampleCount sc = StatsJsonParser.countSamples(rec.getOriginRecordResult());
+			double rate = sc.getTotal() > 0 ? (double) sc.getPositive() / sc.getTotal() * 100 : 0;
+			map.computeIfAbsent(key, k -> new StatsAccumulator(key, rec.getRegionCode(), "customer"))
+				.add(sc.getTotal(), sc.getPositive(), rate);
+		}
+		return map;
+	}
+
+	/** 将 Accumulator 列表转为 VO 列表，并计算各项统计量 */
+	private List<StatsAnalysisVO> computeStats(Map<String, StatsAccumulator> accumMap) {
+		return accumMap.values().stream()
+			.map(this::toStatsAnalysisVO)
+			.collect(Collectors.toList());
+	}
+
+	/** 单个 Accumulator → VO */
+	private StatsAnalysisVO toStatsAnalysisVO(StatsAccumulator acc) {
+		StatsAnalysisVO vo = new StatsAnalysisVO();
+		vo.setRegionName(acc.getRegionName());
+		vo.setRegionCode(acc.getRegionCode());
+		vo.setRegionLevel(acc.getRegionLevel());
+		vo.setTotalSamples(acc.getTotalSamples());
+		vo.setPositiveCount(acc.getPositiveCount());
+		vo.setPositiveRate(acc.getTotalSamples() > 0
+			? (double) acc.getPositiveCount() / acc.getTotalSamples() * 100 : 0.0);
+
+		fillStatistics(vo, acc.getSubPositiveRates());
+		vo.setHasChildren(true);
+		return vo;
+	}
+
+	/**
+	 * 计算统计量并填充到 VO 中
+	 * <ul>
+	 *   <li>Mean — 各子项目阳性率的算术平均值</li>
+	 *   <li>SD — 样本标准差 (n-1)</li>
+	 *   <li>CV — 变异系数 = SD / Mean * 100 (%)</li>
+	 *   <li>SE — 标准误 = SD / sqrt(n)</li>
+	 * </ul>
+	 */
+	private void fillStatistics(StatsAnalysisVO vo, List<Double> subRates) {
+		int n = subRates.size();
+		if (n == 0) {
+			vo.setMeanValue(0.0);
+			vo.setStdDev(0.0);
+			vo.setCv(0.0);
+			vo.setSe(0.0);
+			return;
+		}
+
+		double mean = subRates.stream().mapToDouble(Double::doubleValue).average().orElse(0.0);
+		vo.setMeanValue(round2(mean));
+
+		if (n > 1) {
+			double sumSquaredDiff = subRates.stream()
+				.mapToDouble(v -> Math.pow(v - mean, 2))
+				.sum();
+			double variance = sumSquaredDiff / (n - 1);
+			double sd = Math.sqrt(variance);
+			vo.setStdDev(round2(sd));
+			vo.setSe(round2(sd / Math.sqrt(n)));
+			vo.setCv(mean > 0 ? round2(sd / mean * 100) : 0.0);
+		} else {
+			vo.setStdDev(0.0);
+			vo.setSe(0.0);
+			vo.setCv(0.0);
+		}
+	}
+
+	/** 对 paginated VO 列表进行分页 */
+	private <T> IPage<T> paginate(List<T> list, Query pageQuery) {
+		long current = pageQuery.getCurrent();
+		long size = pageQuery.getSize();
+		long total = list.size();
+
+		Page<T> page = new Page<>(current, size, total);
+		if (total == 0) {
+			page.setRecords(new ArrayList<>());
+			return page;
+		}
+
+		int fromIndex = (int) Math.min((current - 1) * size, total);
+		int toIndex = (int) Math.min(fromIndex + size, total);
+		page.setRecords(list.subList(fromIndex, toIndex));
+		return page;
+	}
+
+	/** 四舍五入保留两位小数 */
+	private static double round2(double value) {
+		return Math.round(value * 100.0) / 100.0;
+	}
+
+	// ==================== Inner types ====================
+
+	/**
+	 * 三表关联后的平铺记录，用于 Java 端分组聚合
+	 */
+	private static class RawRecord {
+		private final String city;
+		private final String regionCode;
+		private final String customerName;
+		private final String originRecordResult;
+
+		RawRecord(String city, String regionCode, String customerName, String originRecordResult) {
+			this.city = city;
+			this.regionCode = regionCode;
+			this.customerName = customerName;
+			this.originRecordResult = originRecordResult;
+		}
+
+		String getCity() { return city; }
+		String getRegionCode() { return regionCode; }
+		String getCustomerName() { return customerName; }
+		String getOriginRecordResult() { return originRecordResult; }
+	}
+
+	/**
+	 * 分组聚合累加器，记录该组的采样总数/阳性数/各子项阳性率
+	 */
+	private static class StatsAccumulator {
+		private final String regionName;
+		private final String regionCode;
+		private final String regionLevel;
+		private int totalSamples;
+		private int positiveCount;
+		private final List<Double> subPositiveRates = new ArrayList<>();
+
+		StatsAccumulator(String regionName, String regionCode, String regionLevel) {
+			this.regionName = regionName;
+			this.regionCode = regionCode;
+			this.regionLevel = regionLevel;
+		}
+
+		void add(int total, int positive, double positiveRate) {
+			this.totalSamples += total;
+			this.positiveCount += positive;
+			this.subPositiveRates.add(positiveRate);
+		}
+
+		String getRegionName() { return regionName; }
+		String getRegionCode() { return regionCode; }
+		String getRegionLevel() { return regionLevel; }
+		int getTotalSamples() { return totalSamples; }
+		int getPositiveCount() { return positiveCount; }
+		List<Double> getSubPositiveRates() { return subPositiveRates; }
+	}
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/service/impl/StatsSampleTagServiceImpl.java

@@ -0,0 +1,24 @@
+package org.springblade.stats.service.impl;
+
+import org.springblade.core.mp.base.BaseServiceImpl;
+import org.springblade.lims.entry.StatsSampleTag;
+import org.springblade.stats.mapper.StatsSampleTagMapper;
+import org.springblade.stats.service.IStatsSampleTagService;
+import org.springframework.stereotype.Service;
+
+import java.util.List;
+
+/**
+ * 样品标签 服务实现类
+ *
+ * @author swj
+ * @since 2026-05-29
+ */
+@Service
+public class StatsSampleTagServiceImpl extends BaseServiceImpl<StatsSampleTagMapper, StatsSampleTag> implements IStatsSampleTagService {
+
+	@Override
+	public List<StatsSampleTag> listBySampleSetId(Long sampleSetId) {
+		return lambdaQuery().eq(StatsSampleTag::getSampleSetId, sampleSetId).list();
+	}
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/util/StatsExcelUtil.java

@@ -0,0 +1,43 @@
+package org.springblade.stats.util;
+
+import org.springblade.lims.excel.StatsAnalysisExportExcel;
+import org.springblade.stats.vo.StatsAnalysisVO;
+
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * 统计分析 Excel 转换工具
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+public class StatsExcelUtil {
+
+	/**
+	 * 将 StatsAnalysisVO 列表转换为 StatsAnalysisExportExcel 列表
+	 *
+	 * @param voList 统计分析 VO 列表
+	 * @return Excel 导出 DTO 列表
+	 */
+	public static List<StatsAnalysisExportExcel> toExportList(List<StatsAnalysisVO> voList) {
+		return voList.stream().map(vo -> {
+			StatsAnalysisExportExcel excel = new StatsAnalysisExportExcel();
+			excel.setRegionName(vo.getRegionName());
+			excel.setTotalSamples(vo.getTotalSamples());
+			excel.setPositiveCount(vo.getPositiveCount());
+			excel.setPositiveRate(vo.getPositiveRateDisplay());
+			excel.setMeanValue(vo.getMeanValue() != null ? String.format("%.2f", vo.getMeanValue()) : "");
+			excel.setStdDev(vo.getStdDev() != null ? String.format("%.2f", vo.getStdDev()) : "");
+			excel.setCv(vo.getCvDisplay());
+			excel.setSe(vo.getSe() != null ? String.format("%.2f", vo.getSe()) : "");
+			excel.setAcceptanceNum("");
+			excel.setAnimalType("");
+			excel.setImmuneSituation("");
+			excel.setExamineItem("");
+			excel.setExamineMethod("");
+			excel.setSimpleSource("");
+			return excel;
+		}).collect(Collectors.toList());
+	}
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/util/StatsJsonParser.java

@@ -0,0 +1,218 @@
+package org.springblade.stats.util;
+
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.JSONArray;
+import com.alibaba.fastjson.JSONObject;
+import lombok.AllArgsConstructor;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * 解析 ExamineResult.originRecordResult JSON 字符串的工具类。
+ * <p>
+ * 处理三种 JSON 结构：
+ * <ul>
+ *   <li><b>Type A JSONArray 模式</b> — Brucella/PCR/血凝/生化鉴定/普通 ELISA 检测</li>
+ *   <li><b>Type B JSONObject 多板模式</b> — 口蹄疫 templateType=2/3，包含 "data" 键</li>
+ *   <li><b>Type C 深层嵌套 Map 模式</b> — 复杂 ELISA 试剂公式，key 为 "g1", "g2" 等</li>
+ * </ul>
+ * <p>
+ * 线程安全 — 无实例状态，仅使用局部变量。
+ */
+public class StatsJsonParser {
+
+	@Data
+	@AllArgsConstructor
+	@NoArgsConstructor
+	public static class SampleResultEntry {
+		/** 原始编号（检测编号） */
+		private String originalNum;
+		/** 样品名称 */
+		private String sampleName;
+		/** 检测结果：阳性/阴性/可疑 */
+		private String result;
+		/** 序号 */
+		private String order;
+	}
+
+	@Data
+	@AllArgsConstructor
+	@NoArgsConstructor
+	public static class SampleCount {
+		/** 总样品数 */
+		private int total;
+		/** 阳性样品数（result = "阳性"） */
+		private int positive;
+	}
+
+	/**
+	 * 解析 originRecordResult JSON 字符串，提取所有样品级结果条目。
+	 *
+	 * @param originRecordResult JSON 字符串（可为 null 或空）
+	 * @return 非 null 的 SampleResultEntry 列表
+	 */
+	public static List<SampleResultEntry> parseEntries(String originRecordResult) {
+		if (originRecordResult == null || originRecordResult.trim().isEmpty()) {
+			return new ArrayList<>();
+		}
+		try {
+			Object parsed = JSON.parse(originRecordResult);
+			List<SampleResultEntry> entries = new ArrayList<>();
+			if (parsed instanceof JSONArray) {
+				// Type A: JSONArray 模式
+				parseJSONArray((JSONArray) parsed, entries);
+			} else if (parsed instanceof JSONObject) {
+				JSONObject obj = (JSONObject) parsed;
+				if (obj.containsKey("data")) {
+					// Type B: 口蹄疫多板模式，包含 "data" key
+					// data → {g1: [...], g2: [...]}
+					traverseForResults(obj, entries);
+				} else {
+					// Type C: 深层嵌套 Map 模式（或 Type B 无 data 的兜底）
+					traverseForResults(obj, entries);
+				}
+			}
+			return entries;
+		} catch (Exception e) {
+			// 任何解析异常 → 返回空列表
+			return new ArrayList<>();
+		}
+	}
+
+	/**
+	 * 遍历 JSONArray，提取每个 JSONObject 中的样品结果。
+	 */
+	private static void parseJSONArray(JSONArray arr, List<SampleResultEntry> entries) {
+		for (int i = 0; i < arr.size(); i++) {
+			Object item = arr.get(i);
+			if (item instanceof JSONObject) {
+				processLeaf((JSONObject) item, entries);
+			}
+		}
+	}
+
+	/**
+	 * 递归遍历 JSON 结构，查找含有 "result" 字段的叶子节点。
+	 * <p>
+	 * 支持 Type B（多层嵌套含 data 键）和 Type C（g1/g2 分组）两种结构。
+	 */
+	private static void traverseForResults(Object obj, List<SampleResultEntry> entries) {
+		if (obj instanceof JSONObject) {
+			JSONObject jsonObj = (JSONObject) obj;
+			// 判断是否为叶子节点（存在 result 字段）
+			if (jsonObj.containsKey("result") || jsonObj.containsKey("Result")) {
+				processLeaf(jsonObj, entries);
+				return; // 叶子节点不再继续深入
+			}
+			// 遍历所有 value，跳过 data 键的值（data 下可能也有 result 结构，但视为叶子处理）
+			for (Map.Entry<String, Object> entry : jsonObj.entrySet()) {
+				Object value = entry.getValue();
+				if (value instanceof JSONArray || value instanceof JSONObject) {
+					traverseForResults(value, entries);
+				}
+			}
+		} else if (obj instanceof JSONArray) {
+			JSONArray arr = (JSONArray) obj;
+			for (int i = 0; i < arr.size(); i++) {
+				traverseForResults(arr.get(i), entries);
+			}
+		}
+	}
+
+	/**
+	 * 从 JSONObject 叶子节点提取样品结果字段，构造 SampleResultEntry。
+	 * <p>
+	 * 兼容不同 JSON 结构的字段命名差异：
+	 * <ul>
+	 *   <li>result/Result — 检测结果</li>
+	 *   <li>originalNum/num/experieNum/original_num — 原始编号</li>
+	 *   <li>simpleName/sampleName — 样品名称</li>
+	 *   <li>order — 序号（String 或 int）</li>
+	 * </ul>
+	 */
+	private static void processLeaf(JSONObject leaf, List<SampleResultEntry> entries) {
+		String result = getStringSafe(leaf, "result");
+		if (result == null) {
+			result = getStringSafe(leaf, "Result");
+		}
+		if (result != null) {
+			result = result.trim();
+		}
+		if (result == null || result.isEmpty()) {
+			return; // 没有 result 字段的叶子节点不计为样品（可能是对照孔或空位）
+		}
+
+		String num = getStringSafe(leaf, "originalNum");
+		if (num == null) {
+			num = getStringSafe(leaf, "num");
+		}
+		if (num == null) {
+			num = getStringSafe(leaf, "experieNum");
+		}
+		if (num == null) {
+			num = getStringSafe(leaf, "original_num");
+		}
+
+		String sampleName = getStringSafe(leaf, "simpleName");
+		if (sampleName == null) {
+			sampleName = getStringSafe(leaf, "sampleName");
+		}
+
+		String order = getStringSafe(leaf, "order");
+		if (order == null) {
+			// 尝试读取 int 类型的 order
+			int orderInt = getIntSafe(leaf, "order");
+			if (orderInt != 0 || leaf.containsKey("order")) {
+				order = String.valueOf(orderInt);
+			}
+		}
+
+		entries.add(new SampleResultEntry(
+			num != null ? num : "",
+			sampleName != null ? sampleName : "",
+			result,
+			order != null ? order : ""
+		));
+	}
+
+	/**
+	 * 安全地从 JSONObject 获取 String 值，异常时返回 null。
+	 */
+	private static String getStringSafe(JSONObject obj, String key) {
+		try {
+			return obj.getString(key);
+		} catch (Exception e) {
+			return null;
+		}
+	}
+
+	/**
+	 * 安全地从 JSONObject 获取 int 值，异常时返回 0。
+	 */
+	private static int getIntSafe(JSONObject obj, String key) {
+		try {
+			return obj.getIntValue(key);
+		} catch (Exception e) {
+			return 0;
+		}
+	}
+
+	/**
+	 * 统计 originRecordResult 中的总样品数和阳性样品数。
+	 *
+	 * @param originRecordResult JSON 字符串
+	 * @return SampleCount 对象（total 和 positive），不会为 null
+	 */
+	public static SampleCount countSamples(String originRecordResult) {
+		List<SampleResultEntry> entries = parseEntries(originRecordResult);
+		int total = entries.size();
+		int positive = (int) entries.stream()
+			.filter(e -> "阳性".equals(e.getResult()))
+			.count();
+		return new SampleCount(total, positive);
+	}
+}

lab-service/lab-lims/src/main/java/org/springblade/stats/vo/StatsAnalysisVO.java

@@ -0,0 +1,46 @@
+package org.springblade.stats.vo;
+
+import lombok.Data;
+import java.io.Serializable;
+
+/**
+ * 统计分析结果 VO
+ *
+ * @author blade
+ * @since 2026-05-29
+ */
+@Data
+public class StatsAnalysisVO implements Serializable {
+	private static final long serialVersionUID = 1L;
+
+	/** 地区/场所名称 */
+	private String regionName;
+	/** 区域级别: city/district/customer */
+	private String regionLevel;
+	/** 总采样数 */
+	private Integer totalSamples;
+	/** 阳性数 */
+	private Integer positiveCount;
+	/** 阳性率 (%) */
+	private Double positiveRate;
+	/** 均值 (Mean) */
+	private Double meanValue;
+	/** 标准差 (SD) */
+	private Double stdDev;
+	/** 变异系数 (CV%) */
+	private Double cv;
+	/** 标准误 (SE) */
+	private Double se;
+	/** 是否有下级数据 */
+	private Boolean hasChildren;
+	/** 区域编码 (cityCode/districtCode) */
+	private String regionCode;
+
+	// Display-friendly computed fields
+	public String getPositiveRateDisplay() {
+		return positiveRate != null ? String.format("%.2f%%", positiveRate) : "0.00%";
+	}
+	public String getCvDisplay() {
+		return cv != null ? String.format("%.2f%%", cv) : "0.00%";
+	}
+}