Backend March 2023

HRDA Platform

HRDA research data analysis platform, providing researchers with a platform to upload survey data and analyze it through AI models, supporting multilingual and complex permission management

HRDA Platform

Project Overview

Providing a platform for researchers to upload survey data and analyze it through different AI models. Supporting multilingual functionality with complex data permission management mechanisms, offering powerful data analysis tools for academic research.

Project Type: Research Data Platform

Development Time: March 2023

Main Technologies: Laravel, Livewire

Key Features: Multilingual, AI Integration

Deployment: Docker Containerization

Project Scope

Responsible for developing the platform for researchers to upload data, AI part handled by other teams. The project focuses on building a stable, secure, and user-friendly data collection and management platform, providing a high-quality data foundation for subsequent AI analysis.

Core Features

Research Data Management Platform

Providing researchers with complete survey data upload and management features. The system design emphasizes data accuracy and completeness, ensuring research data can be smoothly delivered to AI analysis models for processing.

Through an intuitive user interface, researchers can easily manage research projects, upload survey data, and view analysis results, significantly improving research efficiency.

HRDA System Screenshots

HRDA data management and analysis features demonstration

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Multilingual Support

Using Laravel-localization to implement complete multilingual functionality, supporting researchers from different countries

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Complex Permission Management

Implementing detailed data permission control to ensure research data security and privacy protection

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AI Model Integration

Providing complete interface with AI analysis models, delivering collected data to AI team for analysis

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Data Visualization

Providing clear data statistics and visualization charts to help researchers understand data distribution

Backend Technical Details

Multilingual Implementation

Worth mentioning is the use of Laravel-localization to handle multilingual functionality. Through this package, the system can automatically detect user language preferences and provide corresponding interface languages, allowing researchers from different countries to smoothly use the platform.

Data Permission Control

The permissions are divided into data permissions, which are slightly more complex. The system needs to ensure that each researcher can only access their own research project data, while also supporting permission sharing mechanisms for team collaboration. Through detailed permission design, data security is ensured while providing flexible collaboration features.

Frontend Technology

Using Livewire to build interactive user interfaces, making the data upload and management process smoother. Livewire's real-time update feature allows users to immediately see operation results, improving overall user experience.

Deployment Architecture

The project uses Laradock for containerized deployment, ensuring consistency between development and production environments. Through Docker containerization technology, the deployment process is simplified, facilitating future expansion and maintenance.

Technologies Used

Server

  • Laradock (Docker Containerization)

Backend

Data Management

  • MySQL Database
  • Complex Permission System
  • Data Validation and Cleaning

Deployment Tools

  • Docker
  • Laradock

Project Features

Academic Research Oriented

The system is specifically designed for academic research, deeply understanding researchers' needs. From data collection, organization to analysis, the entire process is carefully planned to ensure the quality and reliability of research data.

Team Collaboration

Close collaboration with AI team to ensure data format and transmission methods meet AI model requirements. Through clear API interface design, front-end and back-end teams can collaborate efficiently.

Scalability

The system architecture design considers future expansion needs, whether supporting more AI models, adding new data types, or expanding user numbers, all can be smoothly implemented.