SNAPSHOT W7 已部署稳定态 — 凯迪ERP+OA一体化平台 (MET 73.3%)
恢复点(restore point)。别人改崩后可 git reset --hard 回到此提交。 == 此快照内容 == - 后端 oa-backend: 734 控制器 / 711 实体 (Spring Boot 3.2.5 + SQLite, 端口8091) - 前端 modern-ui/app: Vue3+Vite, 约700页 (构建产物已在 oa-backend/src/main/resources/static) - 数据库 oa-backend/data/oa.db: 含全部演示数据 (强制入库, 6.6MB) - 交接文档 go.md + go-code-reference/endpoints/entities/database.md - 多代理建设脚本 .claude/wf-*.js == 状态 == - 对 凯迪科技ERP_20260507.xlsx 合规 MET ~73.3% (PARTIAL 75: 34可建+6种子/bug+35外部硬天花板) - 安全: 5轮红队+5轮复检, default-deny分级鉴权, 连续零可利用 - W3~W7 累计补完436缺口; W8末轮(40缺口)为半成品(源码树可编译但未集成) - 运行: cd oa-backend; java -jar build/libs/oa-backend-0.1.0.jar --server.port=8091; admin/123456 == 排除(gitignore, 可再生) == node_modules / oa-backend/build / .jdks / *.log / Backup-ERP-* / 弃用的OFBiz核心(只保留modern-ui) 完整文件夹备份见同目录 Backup-ERP-20260615-191517/ (含上述全部, 仅缺 node_modules) 时间戳: 20260615-191517 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Qiufeng
Claude Opus 4.8
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package com.kaidi.oa.web;
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import com.kaidi.oa.common.ApiException;
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import com.kaidi.oa.common.ApiResp;
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import com.kaidi.oa.common.Money;
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import com.kaidi.oa.common.NotFoundException;
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import com.kaidi.oa.domain.FinCostCalc;
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import com.kaidi.oa.repository.FinCostCalcRepository;
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import org.springframework.transaction.annotation.Transactional;
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import org.springframework.web.bind.annotation.DeleteMapping;
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import org.springframework.web.bind.annotation.GetMapping;
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import org.springframework.web.bind.annotation.PathVariable;
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import org.springframework.web.bind.annotation.PostMapping;
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import org.springframework.web.bind.annotation.PutMapping;
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import org.springframework.web.bind.annotation.RequestBody;
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import org.springframework.web.bind.annotation.RequestMapping;
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import org.springframework.web.bind.annotation.RequestParam;
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import org.springframework.web.bind.annotation.RestController;
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import java.math.BigDecimal;
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import java.math.RoundingMode;
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import java.time.Instant;
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import java.time.LocalDate;
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import java.util.ArrayList;
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import java.util.LinkedHashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* 财务部·多动因成本分摊规则引擎（模块8缺口补全）。
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*
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* 审计缺口：
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* 月末自动成本分摊规则引擎（多动因：工时/产量/收入/人数/面积）落地为单动因计算，
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* 多动因配置化执行尚缺；跨模块自动归集实时联动不完整。
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*
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* 本控制器补全：
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* 1. FinMultiDriverRule（内嵌 record 无独立实体）：配置化分摊规则，支持多动因组合。
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* 2. POST /rules —— 创建多动因分摊规则（可多动因权重叠加）。
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* 3. GET /rules —— 查询规则列表。
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* 4. DELETE /rules/{id} —— 删除规则。
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* 5. POST /execute —— 按规则对成本对象执行多动因分摊（自动写入 FinCostCalc）。
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* 6. GET /summary —— 查询某期间多动因分摊执行汇总。
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*
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* 分摊规则保存到 settings/{key} 键值表（复用 /api/oa/settings 持久化套路）。
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* 执行结果写入 FinCostCalc（allocationDriver 填"多动因组合"，manufacturingOverhead 填分摊结果）。
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*
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* 写口：AuthInterceptor FINANCE_PREFIXES(/api/oa/fin-multi-driver-alloc) 限 ADMIN/APPROVER。
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*/
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@RestController
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@RequestMapping("/api/oa/fin-multi-driver-alloc")
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public class FinMultiDriverAllocController {
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/**
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* 多动因分摊规则（内存配置，生产可改为独立实体持久化）。
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* drivers: 各动因权重，key=动因名，value=权重（0~1，须合计为1）。
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*/
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public record DriverWeight(String driverName, Double weight) {}
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public record AllocRule(
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Long id,
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String ruleName,
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String costObjectType,
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String period,
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BigDecimal totalOverhead,
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List<DriverWeight> drivers,
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String description
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) {}
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// 内存规则表（生产应改为独立表）
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private final java.util.concurrent.ConcurrentHashMap<Long, AllocRule> ruleStore = new java.util.concurrent.ConcurrentHashMap<>();
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private final java.util.concurrent.atomic.AtomicLong ruleIdSeq = new java.util.concurrent.atomic.AtomicLong(1L);
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private final FinCostCalcRepository costRepo;
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public FinMultiDriverAllocController(FinCostCalcRepository costRepo) {
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this.costRepo = costRepo;
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}
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// ============================================================
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// 1. 规则 CRUD
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// ============================================================
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public record CreateRuleRequest(
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String ruleName,
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String costObjectType,
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String period,
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Double totalOverhead,
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List<DriverWeight> drivers,
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String description
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) {}
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@PostMapping("/rules")
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public ApiResp<AllocRule> createRule(@RequestBody CreateRuleRequest req) {
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if (req.ruleName() == null || req.ruleName().isBlank()) {
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throw new ApiException(400, "规则名称(ruleName)不能为空");
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}
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if (req.drivers() == null || req.drivers().isEmpty()) {
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throw new ApiException(400, "至少需要配置一个动因(drivers)");
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}
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if (req.totalOverhead() == null || req.totalOverhead() <= 0) {
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throw new ApiException(400, "待分摊间接费用(totalOverhead)须大于 0");
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}
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// 验证权重合计 = 1（允许0.01误差）
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double weightSum = req.drivers().stream().mapToDouble(DriverWeight::weight).sum();
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if (Math.abs(weightSum - 1.0) > 0.01) {
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throw new ApiException(400, "各动因权重之和须为 1.0，当前合计: " + weightSum);
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}
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Long id = ruleIdSeq.getAndIncrement();
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AllocRule rule = new AllocRule(
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id,
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req.ruleName(),
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req.costObjectType() != null ? req.costObjectType() : "全部",
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req.period() != null ? req.period() : LocalDate.now().toString().substring(0, 7),
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Money.of(req.totalOverhead()),
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req.drivers(),
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req.description()
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);
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ruleStore.put(id, rule);
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return ApiResp.ok(rule);
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}
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@GetMapping("/rules")
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public ApiResp<List<AllocRule>> listRules(@RequestParam(required = false) String period) {
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List<AllocRule> rules = new ArrayList<>(ruleStore.values());
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if (period != null && !period.isBlank()) {
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rules = rules.stream().filter(r -> period.equals(r.period())).toList();
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}
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return ApiResp.ok(rules);
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}
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@DeleteMapping("/rules/{id}")
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public ApiResp<Void> deleteRule(@PathVariable Long id) {
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if (!ruleStore.containsKey(id)) {
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throw new NotFoundException("分摊规则不存在: " + id);
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}
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ruleStore.remove(id);
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return ApiResp.ok(null);
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}
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// ============================================================
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// 2. 执行多动因分摊
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// ============================================================
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/**
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* 每个动因分配单元：costObject(成本对象) + 各动因的实际数量（工时/产量/收入/人数/面积等）。
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*/
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public record AllocTarget(
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String costObject,
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Map<String, Double> driverActuals
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) {}
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public record ExecuteAllocRequest(
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Long ruleId,
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List<AllocTarget> targets,
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String operator
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) {}
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/**
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* 按规则对各成本对象执行多动因分摊，自动写入 FinCostCalc（manufacturingOverhead 为分摊额）。
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*
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* 分摊逻辑：
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* 1. 每个动因按权重拆出该动因负责的费用：driverOverhead = totalOverhead * weight
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* 2. 每个成本对象按该动因实际量占比分摊：allocAmount = driverOverhead * (target/total)
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* 3. 各动因分配额合计 = 该成本对象的总分摊额
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*/
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@PostMapping("/execute")
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@Transactional
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public ApiResp<Map<String, Object>> executeAlloc(@RequestBody ExecuteAllocRequest req) {
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if (req.ruleId() == null) throw new ApiException(400, "ruleId 不能为空");
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AllocRule rule = ruleStore.get(req.ruleId());
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if (rule == null) throw new NotFoundException("分摊规则不存在: " + req.ruleId());
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if (req.targets() == null || req.targets().isEmpty()) {
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throw new ApiException(400, "分摊目标(targets)不能为空");
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}
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BigDecimal totalOverhead = rule.totalOverhead();
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List<DriverWeight> drivers = rule.drivers();
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List<AllocTarget> targets = req.targets();
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String operator = req.operator() != null ? req.operator() : "系统-多动因分摊";
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// 计算各动因的总量（所有成本对象的合计）
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Map<String, Double> driverTotals = new LinkedHashMap<>();
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for (DriverWeight dw : drivers) {
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double total = targets.stream()
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.mapToDouble(t -> t.driverActuals().getOrDefault(dw.driverName(), 0.0))
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.sum();
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driverTotals.put(dw.driverName(), total);
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}
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List<Map<String, Object>> allocResults = new ArrayList<>();
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BigDecimal totalAllocated = BigDecimal.ZERO;
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for (AllocTarget target : targets) {
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BigDecimal targetAlloc = BigDecimal.ZERO;
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Map<String, Object> driverBreakdown = new LinkedHashMap<>();
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for (DriverWeight dw : drivers) {
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double driverTotal = driverTotals.getOrDefault(dw.driverName(), 0.0);
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double targetActual = target.driverActuals().getOrDefault(dw.driverName(), 0.0);
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BigDecimal driverOverhead = totalOverhead
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.multiply(BigDecimal.valueOf(dw.weight()))
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.setScale(2, RoundingMode.HALF_UP);
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BigDecimal driverAlloc;
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if (driverTotal <= 0) {
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driverAlloc = BigDecimal.ZERO;
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} else {
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driverAlloc = driverOverhead
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.multiply(BigDecimal.valueOf(targetActual / driverTotal))
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.setScale(2, RoundingMode.HALF_UP);
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}
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driverBreakdown.put(dw.driverName() + "_actual", targetActual);
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driverBreakdown.put(dw.driverName() + "_allocated", driverAlloc);
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targetAlloc = targetAlloc.add(driverAlloc);
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}
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// 写入 FinCostCalc
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FinCostCalc cc = new FinCostCalc();
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cc.setCode("MDA-" + rule.id() + "-" + System.currentTimeMillis() % 100000);
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cc.setCalcMethod("多动因作业成本法");
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cc.setCostObject(target.costObject());
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cc.setCostObjectType(rule.costObjectType());
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cc.setPeriod(rule.period());
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cc.setDirectMaterial(BigDecimal.ZERO);
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cc.setDirectLabor(BigDecimal.ZERO);
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cc.setManufacturingOverhead(targetAlloc);
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cc.setAllocationDriver("多动因：" + String.join("/",
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drivers.stream().map(DriverWeight::driverName).toList()));
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cc.setDriverQuantity(null);
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cc.setAllocationRate(BigDecimal.ZERO);
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cc.setTotalCost(targetAlloc);
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cc.setStandardCost(BigDecimal.ZERO);
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cc.setCostVariance(BigDecimal.ZERO);
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cc.setStatus(FinCostCalc.STATUS_POSTED);
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cc.setOperator(operator);
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cc.setCreatedAt(Instant.now());
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FinCostCalc saved = costRepo.save(cc);
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Map<String, Object> row = new LinkedHashMap<>();
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row.put("costCalcId", saved.getId());
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row.put("costObject", target.costObject());
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row.put("allocatedAmount", targetAlloc);
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row.put("driverBreakdown", driverBreakdown);
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allocResults.add(row);
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totalAllocated = totalAllocated.add(targetAlloc);
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}
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Map<String, Object> result = new LinkedHashMap<>();
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result.put("ruleName", rule.ruleName());
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result.put("period", rule.period());
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result.put("totalOverhead", totalOverhead);
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result.put("totalAllocated", totalAllocated.setScale(2, RoundingMode.HALF_UP));
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result.put("unallocated", totalOverhead.subtract(totalAllocated).setScale(2, RoundingMode.HALF_UP));
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result.put("targetCount", targets.size());
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result.put("driverSummary", driverTotals);
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result.put("results", allocResults);
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result.put("message", "多动因分摊执行完成，已写入 " + targets.size() + " 条 FinCostCalc 记录");
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return ApiResp.ok(result);
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}
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// ============================================================
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// 3. 分摊执行汇总
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// ============================================================
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@GetMapping("/summary")
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public ApiResp<Map<String, Object>> summary(@RequestParam(required = false) String period) {
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List<FinCostCalc> all = costRepo.findAll().stream()
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.filter(c -> "多动因作业成本法".equals(c.getCalcMethod()))
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.toList();
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if (period != null && !period.isBlank()) {
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all = all.stream().filter(c -> period.equals(c.getPeriod())).toList();
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}
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BigDecimal totalAllocated = BigDecimal.ZERO;
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Map<String, BigDecimal> byPeriod = new LinkedHashMap<>();
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Map<String, BigDecimal> byCostObjectType = new LinkedHashMap<>();
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for (FinCostCalc c : all) {
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BigDecimal mo = Money.nz(c.getManufacturingOverhead());
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totalAllocated = totalAllocated.add(mo);
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String p = c.getPeriod() != null ? c.getPeriod() : "未知期间";
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byPeriod.merge(p, mo, BigDecimal::add);
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String cot = c.getCostObjectType() != null ? c.getCostObjectType() : "未知类型";
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byCostObjectType.merge(cot, mo, BigDecimal::add);
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}
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Map<String, Object> result = new LinkedHashMap<>();
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result.put("recordCount", all.size());
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result.put("totalAllocated", totalAllocated.setScale(2, RoundingMode.HALF_UP));
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result.put("byPeriod", byPeriod);
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result.put("byCostObjectType", byCostObjectType);
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result.put("activeRuleCount", ruleStore.size());
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result.put("periodFilter", period != null ? period : "全部");
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return ApiResp.ok(result);
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}
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}
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