EE308FZ_Third assignment_Demand Analysis

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400人浏览 · 2026-02-02 23:17:22

2301_81752640 · 2026-02-02 23:17:22 发布

Assignmrnt 3	Demand Analysis
Course	EE308FZ[A] -Software Engineering
Team name	FZU Meteorological Bureau
Objective	The purpose of this blog is to serve as a showcase portal and a process documentation report for our team’s entire requirement analysis phase. It is ateam work log" that truthfully records how we divided tasks, collaborated, encountered challenges, and how we resolved them, to reflect our team’s thinking, collaboration, and growth the software engineering process.

1.Introduction

1.1 Overview of the assignment and objectives

Our aim is to develop a platform for urban air quality inquiry and early warning, and at the same time, it can also integrate a volunteer service platform. By organically integrating air quality inquiry and early warning services with volunteer service management, precise and efficient services are provided for different user groups.

1.2 Purpose of the blog

The purpose of this blog is to serve as a showcase portal and a process documentation report for our team’s entire requirement analysis phase. It is a “team work log” that truthfully records how we divided tasks, collaborated, encountered challenges, and how we resolved them, to reflect our team’s thinking, collaboration, and growth the software engineering process.

2.Team Work

2.1 Team Contribution

Name	Main responsibilities	Proportion of workload
Chen Ye	Be responsible for the overall planning of the project, progress management and team coordination. Lead the writing, integration and finalization of all project documents	9%
Wang Jiarui	Be responsible for the front-end routing design and the implementation of permission control logic	8%
Li Yifei	up the scaffolding and development environment for the Vue project	8%
Zhang Shimin	Responsible for the development of page components and the implementation of styles; Customize the system theme and layout; Complete the user interaction function	8%
Sun Chenen	Build a multi-module project architecture of Spring Boot	8%
ChenXueting	Implement the user login authentication module and complete the permission menu management function	8%
Zhang Zhikai	Complete the development of core CRUD functions for the business. Write unit test cases; Ensure code quality	8%
Liu Zhongbo	Integrate Redis caching function; Realize file upload and download services; Optimize the thread pool configuration	8%
Chen Hongyu	Execute functional test cases; Track and verify Bug fixes; Write a test report	7%
Lin Juntian	Conduct system performance tests; Organize user acceptance tests; Collect user feedback	7%
Li Yuxin	Design the table structure of the database; Maintain the data dictionary	7%
Lin Qixuan	Optimize the database index configuration; Build a master-slave replication architecture; Formulate a data backup strategy	7%

2.2 Team Collaboration Methods

Our team adopts a collaborative model that combines the “project manager responsibility system” with “functional module grouping” to ensure the efficient advancement of the project.

Hierarchical responsibility mechanism: The project manager is responsible for overall planning and progress control. Each functional module (front-end group, back-end group, testing group, database group) has one team leader who is responsible for task allocation and technical solution implementation within the module.
Regular synchronous meetings: A station meeting is held once a week, where each module progresses synchronously. Any blockage issues are reported on the same day and resources are coordinated to solve them.
Development tool collaboration: Use Tencent Meeting/DingTalk for online communication and solution review; Use online documents for task tracking and meeting minutes.

3.Possible Key Points and Challenges

Section	Explaination	Challenge
Data support	This section aims to ensure the uniformity and accuracy of the data presented to users and used for system analysis by eliminating the differences in data statistical standards among various apis.	The statistical standards of different third-party apis vary, making it difficult to unify the data.
Intelligent interaction	This section aims to build a smooth natural language interaction interface, using LLM to accurately identify requirements and provide professional responses. Thereby enhancing the user experience.	During peak hours, a large number of users logging in simultaneously may cause the server load to be too high, resulting in the risk of page loading lag.
Personalized service	This section aims to build multi-dimensional user profiles, customize content based on user identities, health conditions, living habits and other information, and thereby create exclusive services for different groups of people.	If users’ health data and location information are not properly encrypted during storage, it is easy to cause privacy leakage problems.
Multi-scenario adaptation	This section aims to design differentiated functional modules for different scenarios, thereby achieving the universal value of the project.	If the functional modules of each scenario are designed independently and lack interaction, it will reduce the overall value of the platform.

4.Schedule

The Next Schedule for Urban Air Quality Query and Warning Platform

Week 9:Requirement analysis

We will conduct a discussion on the requirements for the entire project to ensure more efficient achievement of the objectives.

User requirements: The general public needs intuitive access to air quality data, pollution interpretation and health advice via LLM, and accurate alert notifications and activity planning references; special groups require scenario-specific protection recommendations and simplified operation interfaces, with alerts focusing on safety and practicality.
Functional Requirements: Provide users with graphical data display, regional comparison, LLM-powered intelligent Q&A, multi-level targeted warning push, and customizable settings. Additionally, provide administrators with a dedicated backend for user management, data statistics, and system configuration.
Data Requirements: Data sources include authoritative APIs such as the Ministry of Ecology and Environment, user-submitted information, and volunteer service records. Air quality data is in JSON format, user and volunteer data are stored in structured databases, and LLM dialogues are archived in a “question-answer-time” format.

Week 10-11:Frontend Development (Core Modules)

Develop core pages: Build a homepage to display real-time air quality data such as AQI and PM2.5, create an LLM chat interface supporting text input and response display, and construct a user page with registration and profile editing functions.
Design and implement UI: Complete the design and development of the alert notification UI, ensuring clear visual presentation and compliance with the requirements of distinguishing alert levels.

Week 12-13:Backend Development (Core Logic)

Develop Core APIs: Build an air quality data API to fetch real-time and historical data by calling public interfaces, develop user management APIs, and establish personalized recommendation APIs by linking user profiles with air quality data to generate customized suggestions.
Conduct API testing: Utilize professional tools to perform comprehensive tests on all developed APIs, including functionality, response, and exception handling, ensuring the availability and stability of APIs.

Week14: Integration and Functional Testing

Complete front-end and back-end integration: connect the front-end interface with back-end APIs to ensure smooth data interaction and effective operational logic linkage.
Functionality Testing and Bug Fixing: Verify core features such as real-time data updates, promptly identify and resolve issues discovered during testing.