Graduation Project • HUST EEE

Reward-Based Collaboration Platform

The system standardizes online service needs into campaigns and micro-tasks. Participants submit completion proof, the backend verifies and awards points, and points can be redeemed to request services—forming a measurable, auditable workflow loop.

Campaign → Micro-tasksProof-based verificationMariaDB + MongoDBOn-prem + Tunnel

Project Snapshot

Core modulesA / B / C
FrontendsWeb / App
Backend stackNode / PHP / Go / Python
DeploymentHybrid

Intended outcomes

  • • Standardize workflows into campaigns and tasks.
  • • Keep verification auditable with proof and history.
  • • Operate efficiently with maintainable deployment.

Core Modules

What the platform delivers

The platform is designed around a closed-loop flow: campaigns are created, split into tasks, completed by participants, verified through proof, and rewarded by points. This creates a consistent way to manage collaboration at scale.

Module A

Campaign & Task Orchestration

Convert a request into structured tasks and distribute them to participants with basic constraints and status tracking.

  • • Campaign lifecycle & task queue
  • • Requester / Worker roles
  • • Status + history (audit)
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Module A — Campaign orchestration and scaling workflow
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Module B

Proof-Based Completion & Rewards

Participants submit image/video proof; the system records, reviews, and credits points according to task rules.

  • • Proof upload and storage paths
  • • Review workflow and audit trail
  • • Points → redemption loop
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Figure 3.7 — Module B (rating with proof)
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Module C

Risk Lookup & Safety Alerts

A lightweight lookup module helps users check identifiers and shows warnings when risk signals are detected.

  • • Fast lookup + result scoring
  • • Community reports (moderated)
  • • Clear warnings in UI
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Figure 3.8 / 3.9 — Risk lookup & processing pipeline
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Infrastructure

On-prem server core (prototype deployment)

The system is deployed on a physical server to keep operating costs low while maintaining stable performance for long-running services. This setup supports modular services, file-based proof storage, and future AI extensions.

  • • Multi-service runtime: Node.js / PHP / Go / Python
  • • Room for caching and parallel processing
  • • Designed for continuous operation with planned backups

Image notes

You can replace this with the final hardware table and photos in the thesis.

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On-prem server configuration
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Architecture

Modular services & hybrid data layer

The system follows a client–server model with modular services. APIs are standardized for multi-platform clients, while data is stored using a hybrid approach: relational tables for core transactions and document storage for flexible records/logs.

Data contracts & API gateway

Consistent JSON contracts reduce coupling and simplify long-term extension.

MariaDB + MongoDB

SQL stores transactional records; NoSQL stores semi-structured content and operational data.

Proof storage

Proof files can be stored on dedicated disks or storage services; the DB stores references for auditing.

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Backend services and data layer map
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Deployment & Ops

Low-cost, maintainable operations

The prototype emphasizes practical operation: on-prem servers, protected ingress via a tunnel/reverse proxy, and lightweight automation for backup and updates.

  • • Tunnel-based access to hide origin infrastructure.
  • • Scripted service start/stop in correct order.
  • • Periodic sync updates and automated backups.
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Hybrid deployment using tunnel and reverse proxy
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Figure 0.1 — Application functions
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Figure 0.1 — Application functions

High-level feature map of the platform: campaigns, tasks, proof submission, rewards, and supporting modules.

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Figure 1.1 — System technical diagram
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Figure 1.1 — System technical diagram

Client requests are handled by the server core, routed to modular services, and stored across dedicated data layers with update automation.

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Figure 2.2 — Server structure
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Figure 2.2 — Server structure

On-prem server resources used to run long-lived services and handle proof/media workloads in the prototype.

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Figure 2.4 — Hybrid network architecture
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Figure 2.4 — Hybrid network architecture

Hybrid access model using tunnel/reverse proxy to protect the origin server while keeping deployment practical.

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Figure 2.6 — AI assistant in VS Code
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Figure 2.6 — AI assistant in VS Code

AI-assisted development used to speed up implementation while keeping review and testing in the loop.

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Figure 2.7 — Multi-platform frontend
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Figure 2.7 — Multi-platform frontend

Frontend architecture overview across surfaces, focused on consistent UX and interoperable data contracts.

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Figure 2.10 — Backend overview
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Figure 2.10 — Backend overview

Service layout showing modular responsibilities across Node.js/PHP/Go/Python with shared API endpoints.

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Figure 3.x — Requester/Worker flow
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Figure 3.x — Requester/Worker flow

Requester (A) creates campaigns; workers (B) execute tasks, submit proof, and receive points after review.

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Figure 4.1 — Initial release architecture
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Figure 4.1 — Initial release architecture

First App Store deployment flow for the prototype release and its hybrid client strategy.

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Figure 4.2 — Current release architecture
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Figure 4.2 — Current release architecture

Current deployment model with a React Native shell, WebView core, deep links, and runtime updates.

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Figure 4.5 — Performance monitoring overview
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Figure 4.5 — Performance monitoring overview

Operational monitoring of CPU/RAM/disk/network combined with simple alerts to support stability.

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Figure 4.6 — Admin dashboard overview
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Figure 4.6 — Admin dashboard overview

Admin interface for user/order management, proof moderation, and operational KPIs.

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Figure 4.7 — Backup and update workflow
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Figure 4.7 — Backup and update workflow

Automated backups and lightweight CI/CD sync to keep deployments recoverable and maintainable.