Cloud-Edge Vehicle Diagnosis for Low-Latency Remote Diagnostics
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Solution Overview
Problem
Existing automobile diagnosis applications on terminal devices suffer from insufficient computing power, which limits their ability to effectively diagnose complex modern vehicles.
Innovation Solution
A system comprising a cloud server, edge nodes, and end-side devices that leverage edge computing to distribute diagnosis tasks, with the cloud server generating complex diagnosis logic and sending it to the nearest edge node for interaction with the vehicle, enhancing computing power and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If diagnosis applications run on terminal devices, then ease of operation is improved, but computing power is insufficient
Solution Approach 1:
The system segments the diagnosis application into multiple components: terminal device (for user interaction), edge node (for real-time data processing), and cloud server (for complex computation). This segmentation allows each component to perform its specialized function, resolving the contradiction between ease of operation on terminal devices and sufficient computing power through distributed architecture.
Solution Approach 2:
The edge node acts as an intermediary between the terminal device and the cloud server. It receives data from the terminal, performs preliminary processing, and communicates with the cloud server for complex computations. This intermediary structure enables the terminal to remain simple and easy to operate while leveraging the powerful computing resources of the cloud through the edge node's mediation.
2Power
If cloud server processes all diagnosis tasks, then computing power is improved, but communication delay increases
Solution Approach 1:
The system implements local quality by deploying edge nodes closer to the terminal devices. These edge nodes perform real-time data processing and filtering locally, reducing the volume of data that needs to be transmitted to the cloud server. This local processing capability reduces communication delay while maintaining the cloud server's powerful computing resources for complex tasks.
Solution Approach 2:
The edge node performs preliminary action by pre-processing diagnostic data before it reaches the cloud server. It filters, aggregates, and prepares data in advance, so that when data is transmitted to the cloud server, the computational work is already partially completed. This preliminary processing reduces the time required for cloud-based computation and minimizes overall communication delay.
3Speed
If complex diagnosis logic is executed locally, then response speed is improved, but device complexity increases
Solution Approach 1:
The system applies dynamics by making the diagnosis logic distribution flexible and adaptive. Simple diagnostic tasks are executed locally at the edge node for fast response, while complex diagnostic logic is dynamically offloaded to the cloud server. This dynamic allocation of computation tasks allows the system to optimize response speed for simple cases while avoiding the device complexity of implementing all diagnostic functions locally.
Data Source
AI summary
An automobile diagnosis system and method, and a cloud server. The system includes a cloud server, an end-side device, and at least one edge node; wherein, the cloud server is in communication connection with each edge node, and is used for acquiring diagnosis instructions to generate diagnosis data and vehicle communication logics, and sending the diagnosis data and the vehicle communication logics to an edge node having the minimum communication delay with the end-side device; the edge node is used for performing data interaction with the end-side device to acquire diagnosis results and send the diagnosis results to the cloud server; and the end-side device is in communication connection with the edge node and a vehicle, and is used for sending vehicle data of the vehicle and the diagnosis results to the edge node.