Robotic Mower Edge Control for Real-Time Cloud Collaboration
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Solution Overview
Problem
Existing power tool systems face challenges in managing large volumes of data with high real-time performance and accuracy requirements, necessitating improved data collection, processing, and control instruction generation.
Innovation Solution
A robotic mower system and power tool management system utilizing cloud-edge-end collaboration, involving terminal devices, edge nodes, and cloud platforms for data collection, processing, and control instruction generation, with edge nodes performing local computations and cloud platforms providing overall management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is collected and processed locally at the terminal device, then real-time control performance is improved, but device complexity and processing capability are insufficient
Solution Approach 1:
The system segments data processing across three levels: terminal device (local real-time control), edge node (regional data processing and model updating), and cloud platform (overall management and model training). This segmentation allows real-time control to be handled locally while complex processing is distributed to higher levels, resolving the contradiction between real-time performance and processing capability.
Solution Approach 2:
The edge node acts as an intermediary between the terminal device and cloud platform. It receives data from terminal devices, performs local processing and model updates, then transmits processed data and models to the cloud platform. This intermediary structure enables real-time local control while leveraging cloud computing power for complex tasks.
2Measurement precision
If massive data is transmitted to the cloud platform for processing, then analysis accuracy is improved, but communication time and network dependency increase
Solution Approach 1:
The system implements local quality by enabling edge nodes to process data locally and update control models without requiring constant cloud communication. Terminal devices use locally updated models for real-time control, reducing communication frequency while maintaining accuracy through periodic cloud-based model retraining.
Solution Approach 2:
The cloud platform performs preliminary model training offline, then deploys trained models to edge nodes. Edge nodes use these pre-trained models for local data processing and control decisions, avoiding the need to transmit all raw data to the cloud in real-time, thus reducing communication time while maintaining analysis accuracy.
3Productivity
If control models are continuously updated with local data, then operational optimization is improved, but system complexity and training resources increase
Solution Approach 1:
The system adds a hierarchical dimension to model updates: terminal devices generate local data, edge nodes perform incremental model updates using this data, and cloud platforms perform overall model training and management. This hierarchical approach distributes complexity across different levels, enabling continuous optimization without overwhelming any single component.
Solution Approach 2:
The system implements feedback loops where terminal devices transmit operational data to edge nodes, which update control models and transmit improvements back to terminal devices. The cloud platform periodically retrains models using aggregated data from multiple edge nodes and redistributes updated models. This feedback mechanism enables continuous optimization while distributing the computational burden.
Data Source
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AI summary
Provided are a robotic mower system and a power tool management system based on cloud-edge-end collaboration. The robotic mower system includes: a terminal device including a robotic mower and configured to collect at least local data; an edge node communicatively connected to the terminal device, where the edge node is configured to acquire the local data from the terminal device and update a control program of the terminal device by using the local data; and a cloud platform communicatively connected to the edge node, where the cloud platform is configured to acquire the control program from the edge node and perform overall management on control programs.