找出问题，解决问题，
用数字证明。Find the problem. Fix it.
Prove it with numbers.

我做了什么WHAT I DID

• 拆业务流程：拆成字段定义、业务规则和页面需求，覆盖风险发现、月度计划、周度履约、查询追溯 4 个环节。Process design: broke the workflow into field definitions, business rules and page requirements across risk detection, monthly planning, weekly fulfilment and traceability.
• 造数据、验系统：演示数据覆盖 5 个渠道、6 个产品分类、18 个 SKU；测试场景参考实习中的设计，模拟缺货、到货延迟、库存积压等情况。Data and testing: demo data spans 5 channels, 6 categories and 18 SKUs; scenarios modelled on my internship test design cover stock-outs, late arrivals and overstock.
• 查问题、修问题：抽样复算修正预测口径、汇率换算和回溯逻辑；核对数据发现两处隐藏口径错误，误判的异常 SKU 从 18 个降到 8 个。Finding and fixing: sample recalculations corrected forecast, FX and trace logic; reconciliation exposed two hidden definition errors, cutting falsely flagged SKUs from 18 to 8.
• 脱敏与重构：内部编码统一换成通用业务术语，改动 212 个文件，31 张表、2,267 行结果逐字段一致。Refactor: replaced internal codes with generic business terms across 212 files; all 31 tables and 2,267 rows matched field by field.

我做了什么WHAT I DID

• 需求追溯：对照 UML 设计与新要求，找出状态模型、角色职责、入所流程 3 处冲突并制定方案；拆出权限矩阵、状态流转规则和 20 余项验收标准。Requirements tracing: found 3 conflicts between the UML design and new requirements and resolved them; defined a permission matrix, transition rules and 20+ acceptance criteria.
• 流程设计："上架准备清单"按负责人拆分前置条件，员工和兽医可并行推进；按儿童、现有宠物、独处时长、租房许可等条件给申请人打分；确认领养后自动关闭其他申请并安排回访。Workflow design: a readiness checklist split by owner so staff and vets work in parallel; applicant scoring on children, pets, hours alone and landlord permission; finalising closes other applications and books a follow-up.
• 测试验收：17 只动物、12 个账号、42 个品种的演示数据；自动化测试覆盖 22 项越权检查和完整领养流程；修复 Safari 点击错位、表单选项被重置等问题。Testing: demo data with 17 animals, 12 accounts and 42 breeds; automated tests for 22 access checks and the full adoption flow; fixed a Safari row-click bug and form resets.
• 体验与交付：按角色定制首页看板与待办，代码脱敏，中英双语文档与流程图。Delivery: role-specific dashboards, de-identified code, bilingual docs and flow diagrams.

体现的能力WHAT IT SHOWS

• 在大量数据里找到关键线索：假供应商、假员工、假客户都登记在同一个住址，顺着这条线串起三类舞弊。Finding the thread: fake vendors, ghost employees and a fake customer shared one home address, which linked three schemes.
• 从数字规律里发现问题：最高一笔 9,969 美元，全部"刚好"低于审批线，说明有人系统性地拆单绕过审批。Reading the pattern: every payment sat just under the limit, a sign of deliberate splitting to avoid approval.
• 追到根本原因：没有审批权的前台人员批准了 143 笔付款，财务负责人给自己的工资审批了 104 次。Root cause: a receptionist with no approval rights approved 143 payments, and the CFO approved her own pay 104 times.
• 把一次检查变成长期机制：6 条检查规则做成季度自动监控，异常直接报董事会。From one-off to ongoing: 6 checks turned into quarterly automated monitoring reported straight to the board.
• 用数字支撑决策：每条结论附金额和笔数，支撑"冻结权限、外部调查"的建议。Numbers that drive decisions: every finding carries amounts and counts to back an immediate access freeze and external investigation.

报告还包括ALSO IN THE REPORT

IT 治理评估 · 内部控制评估（备份失败率 29.4%，行业标准为 5% 以内）· 客户数据共享的伦理建议 · 工具：SQL（PostgreSQL）、舞弊三角模型、COSO 内控框架IT governance review · internal control review (29.4% backup failure rate vs a 5% norm) · ethics advice on sharing customer data · Tools: SQL (PostgreSQL), fraud triangle, COSO

我做了什么WHAT I DID

• 梳理分析需求：从运营视角明确 4 类问题：销售趋势、商品表现、客户结构、供应商管理。Analysis needs: framed four operating questions: sales trends, product performance, customer mix, supplier management.
• 数据治理：用 SQL Server 和 SSIS 完成清洗、匹配与关联，统一商品、客户、供应商等关键字段的口径。Data governance: cleaned, matched and joined the data with SQL Server and SSIS, aligning key fields for products, customers and suppliers.
• 搭建看板：用 Power BI 做 4 个模块，支持按时间、商品、供应商筛选，并用 R 做补充分析。Dashboard: four Power BI modules filterable by time, product and supplier, with supporting analysis in R.

我负责的部分MY PART

• 立项与分工：组织需求分析和技术选型，把产品拆成地图、路线、后端、规划等模块，完成 7 人分工。Scoping: ran requirements and tech selection, split the product into map, routing, backend and planner modules, and assigned 7 people.
• 让 7 个人并行开发：搭建代码库和工程骨架，"先定接口、用模拟数据解耦"，持续协调地图、路线、后端模块的数据格式。Parallel delivery: set up the repo and skeleton, agreed interfaces first with mock data, and kept map, routing and backend formats aligned.
• 借助 AI 开发前端：用 Next.js 串联天气、高德地图和规划算法，行程列表与地图联动，适配手机。Front end with AI: built the Next.js app linking weather, AMap and the planner, with list–map sync and mobile support.
• 为"出错"做设计：外部数据不可用时切换演示数据，并在页面上明确标注，不把模拟信息当真实信息展示。Designing for failure: when a data source is down the app switches to clearly labelled demo data instead of passing it off as real.

团队共同实现BUILT BY THE TEAM

高德地理编码与景点、酒店检索 · K-Means 分天与 TSP 路线优化 · Python 后端与实验性的大模型规划流程AMap geocoding and POI search · K-Means day clustering and TSP routing · Python backend and an experimental LLM planner