MGDAAS Agricultural Optimal Mechanism Control Platform

Project Background

In the current digital and refined transformation of smart agriculture and facility breeding scenarios, traditional manual control modes face the following pain points:

Low Efficiency: Manual inspection and operation are time-consuming and labor-intensive
Delayed Response: Environmental changes are not addressed in a timely manner
Data Silos: Data from various stages is isolated, making it difficult to form decision-making basis

These issues cannot meet the core needs of modern production for precise control and scientific decision-making.

The MGDAAS Agricultural Optimal Mechanism Control Platform focuses on two core goals: integrated equipment control and real-time environmental data perception. It integrates manual/automatic control, data collection, and intelligent linkage functions to create an efficient, convenient, and precise full-scenario control solution.

Project Introduction

Core Positioning

"Controllable Equipment, Perceptible Data, Efficient Management"

Relying on core technologies such as IoT and data transmission, we build a "Control + Perception" dual-core system covering the entire production process control needs.

Applicable Scenarios

✅ Greenhouse Facilities
✅ Large-scale Bases
✅ Featured Planting Parks

Platform Advantages

Advantage	Description
Low Cost	Reduce equipment investment and operation costs
Easy Operation	Simple and intuitive operation interface
High Adaptability	Adapt to various agricultural scenarios
Scalable	Support flexible expansion of functional modules

Core Features

Feature Module	Description
Dashboard	Aggregates core platform data and operational overview, intuitively displaying overall farm environmental status, equipment online status, crop growth progress, and various data statistics, providing one-stop access to global farming information.
Digital Large Screen	Visual intelligent data dashboard integrating environmental monitoring data, equipment operation status, park planting distribution, and growth data, presented in charts and real-time views, suitable for park large screen display, achieving real-time visual control of farm operation status.
Crop Management	Unified entry and classification management of various planting crop information, improving crop basic archives, planting attributes, growth cycles, and maintenance standards, achieving standardized filing and refined classification coordination of crop categories.
Planting Management	Coordinates the entire process of planting agricultural operations, covering planting planning, plot division, agricultural records, water and fertilizer scheme configuration, and growth stage control, standardizing planting processes and implementing standardized farming procedures.
Authorization Management	Builds a hierarchical permission control system, flexibly assigning different roles' operation permissions, access permissions, and management scope, clarifying personnel functions, and ensuring platform data security and clear park management responsibilities.
Equipment Types	Categorizes and archives various intelligent agricultural equipment in the park, uniformly classifying equipment models, functional attributes, and applicable scenarios, improving standardized management of equipment categories for later coordination and maintenance.
Equipment Management	Centralized control of all intelligent farming equipment in the field, supporting equipment binding, status monitoring, remote start/stop, parameter debugging, fault viewing, and operation and maintenance management, achieving integrated centralized control of all agricultural intelligent equipment.
Video Monitoring	Connects to monitoring camera equipment throughout the park, supporting real-time video viewing, remote playback, point management, and video retrieval functions, enabling remote inspection of field scenes anytime, anywhere, and real-time grasp of on-site conditions.
Operation Logs	Automatically records all personnel operations, equipment control, data modifications, and agricultural operations on the platform, with full traceability for query and tracking, facilitating operation and maintenance verification and agricultural traceability management.
Growth Model Library	Built-in optimal mechanism growth models for crops such as tomatoes, strawberries, cucumbers, and watermelons, relying on scientific crop growth data to build a standardized growth system, precisely matching suitable environmental parameters, growth rhythms, and maintenance standards for each crop, intelligently guiding environmental regulation and agricultural management, helping improve crop quality and yield, and achieving scientific and standardized smart planting.