East Wind - Sprint Blog Day2

Miracle_Han 2021-11-15 22:57:13
The Link Your Classhttps://bbs.csdn.net/forums/MUEE308FZ
The Link of Requirement of This Assignmenthttps://bbs.csdn.net/topics/603251839
The Name of TeamEast Wind
The Goal of This AssignmentAlpha Sprint
Leader's MU STU ID and Name19104375 & 831902106 Xiaolong Han
Teammate 1's MU STU ID and Name19104600 & 831902123 Yongquan Wang
Teammate 2's MU STU ID and Name19104847 & 831902119 Jiatong Zhang
Teammate 3's MU STU ID and Name19104821 & 831902118 Shuoyuan Chen
Teammate 4's MU STU ID and Name19104952 & 831901329 Xiaotong Shao
Group number6 - A

📜 Catalog

目录

  • 1. Expected task arrangement and completion status
  • 👨‍🎓Leader: Xiaolong Han__ Miracle
  • 👩‍🎓Teammate 1: Yongquan Wang __ Kerio
  • 👨‍✈️Teammate 2: Jiatong Zhang __ Allen
  • 👨‍⚖️Teammate 3: Shuoyuan Chen __ Dio
  • 👩‍⚖️Teammate 4: Xiaotong Shao __
  • 👨🏿‍🤝‍👨🏿Team member contribution details
  • 2. Project burnout diagram
  • 3. Github check-in logs and screenshots of the project running today show the progress of the development
  • Summary:
  • 4. System UML design diagram and system module class diagram
  • Today's child UML diagram
  • UML design diagrams
  • Class diagram
  • Activity diagram

1. Expected task arrangement and completion status

Everyone's progress today

👨‍🎓Leader: Xiaolong Han__ Miracle
Completed taskTask Completion DurationProblems with completing tasksHandling
Write a daily Blog45mins
👩‍🎓Teammate 1: Yongquan Wang __ Kerio
👨‍✈️Teammate 2: Jiatong Zhang __ Allen
  1. They did the same thing they did yesterday because the back end takes a lot of time to learn
  2. Sorted out the various parts of the knowledge points need to learn, according to the knowledge points download configuration of the relevant procedures and servers
Completed taskTask Completion DurationProblems with completing tasksHandling
Learn about the database SpringBoot and try writing interfacesAll day longThere are so many problems that I don't know how to start. It takes a lot of time to configure the software environment, and it is difficult to implement several teaching videos in practical operation
Learn how to use Java for applets call interfaces

🏫This is the record of our study in Bilibili University

👨‍⚖️Teammate 3: Shuoyuan Chen __ Dio
Completed taskTask Completion DurationProblems with completing tasksHandling
Design homepage 2.060minsDon't know how to code a lot of desired effectsConsult CSDN and many other articles, find the answer from it, and imitate the production principle according to the ink knife prototype.
Vlog record15mins

img

👩‍⚖️Teammate 4: Xiaotong Shao __

Since we have not entered the testing stage, Mr.Shao has not been used at this stage, so we are more asking him to help other teammates

Completed taskTask Completion DurationProblems with completing tasksHandling
Assist in Blog shooting15mins
Assist in Vlog shooting60mins
👨🏿‍🤝‍👨🏿Team member contribution details
MemberContribution DegreeContribution Details
Xiaolong Han20%Blog writing
Yongquan Wang20%Learn about the database SpringBoot and try writing interfaces
Jiatong Zhang20%Learn about the database SpringBoot and try writing interfaces
Shuoyuan Chen21%Set up the Github Library and take vlogs
Xiaotong Shao19%Assist team members in writing Blog and shooting Vlog

2. Project burnout diagram

img

3. Github check-in logs and screenshots of the project running today show the progress of the development

Here is our Github check-in record.

img

Summary:
  • Today is a busy day, but it still seems to be in the same place, the configuration of the server is also continuing to do, the front-end design for a simpler design, so the designer has also changed.
  • The experiment was far more difficult than we thought.

4. System UML design diagram and system module class diagram

Today's child UML diagram

img

UML design diagrams

img

Class diagram

img

Activity diagram

img


#####Sequence diagram

img

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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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