LAB8 - 汪汪队坐大牢 - Sprint Plan

cy- 2021-11-20 22:41:42
The Link Your Classhttps://bbs.csdn.net/forums/MUEE308FZ
The Link of Requirement of This Assignmenthttps://bbs.csdn.net/topics/603251839
The Aim of This AssignmentSprint Plan
Team Name汪汪队坐大牢_6B
Teamleader's MU STU ID and FZU STU ID19103565_831901228
Teammate1's MU STU ID and FZU STU ID19103182_831901230
Teammate2's MU STU ID and FZU STU ID19104081_831901229
Teammate3's MU STU ID and FZU STU ID19105037_831901202
Teammate4's MU STU ID and FZU STU ID19104251_831901213

Previous Work Summary

In the past work, our team have completed requirement analysis. In the past reports, we keep finding our strengths and weaknesses and correcting them to improve and perfect our project.


Advantages:

1.We can cooperate efficiently and fix the problems on time.

2. The overall design of the project is in the right direction and can solve the open source applet problem more comprehensively and deeply.

3. We have decided the software to use which is Wechat mini_program.


Disadvantages:

1. Due to the complete lack of soft coding foundation and the low exposure of team members to applet development, there were some difficulties in coding for this project.

2. We don't have much time to do these jobs.

3. The functions are still need to be improved.


Improvements

1. Learn JS, WXML, WXSS required for mini-program development according to WeChat small program development documentation.

2. Improved the functions.


Team division of labor

WorkPerson
Front EndZongYue Zhang
Back EndZheYi Zhu
Ucer Iterface DesignZeLong Fang
BlogYue Chen
VlogShunQing Yang
Interface and DebuggingYue Chen

Github link

https://github.com/EE308LAB8script/wangwangduizuodalao

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内容概要:本文介绍了一个基于Java与Vue的垃圾邮件识别与过滤系统的设计与实现,采用前后端分离架构,后端使用Spring Boot实现邮件接收、文本预处理、特征提取、模型预测与规则过滤等功能,前端通过Vue构建可视化管理界面。系统融合朴素贝叶斯分类模型、TF-IDF特征工程、关键词风险评分与规则引擎,实现对垃圾邮件的高效识别与可解释性判定。通过文本清洗、中文分词、概率计算与多维度风险融合策略,系统能有效应对垃圾内容表达变异、语义重叠等问题,并支持人工反馈驱动的模型持续优化。; 适合人群:具备Java Web开发、Vue前端技术基础,熟悉基本机器学习算法(如朴素贝叶斯、逻辑回归)的1-3年经验研发人员,尤其适合从事内容安全、反欺诈、文本分类相关项目的开发者;; 使用场景及目标:①应用于企业邮件系统、客服工单、站内信等场景中的垃圾信息自动过滤;②构建可解释的智能风控体系,降低误判率与网络诈骗风险;③作为前后端分离、算法集成与系统闭环设计的综合实践案例;; 阅读建议:建议结合提供的代码示例深入理解文本清洗、词频统计、贝叶斯训练与风险融合逻辑,重点关注模型与规则协同工作机制,同时可通过扩展模块(如接入中文分词服务、深度学习模型)进一步提升系统能力。

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