Vehicle Remote Diagnosis Using Pre-Generated Script Files
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Solution Overview
Problem
Existing remote diagnostic systems for vehicles face high resource overhead, low operational efficiency, and low diagnostic efficiency due to frequent data retrieval from the diagnostic database by the vehicle main node.
Innovation Solution
The system converts the diagnostic database and sequence into an interpreted diagnostic script file, which is then sent to the remote diagnostic client, reducing the need for frequent database access and parsing of diagnostic sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vehicle frequently retrieves data from the diagnostic database according to the diagnostic sequence, then the diagnostic tasks can be completed, but the resource overhead on the vehicle main node increases and operational efficiency decreases
Solution Approach 1:
The patent pre-generates and stores diagnostic script files on the vehicle side before actual diagnostic tasks are executed. These script files contain pre-processed diagnostic instructions and data retrieval sequences. When a diagnostic task is triggered, the vehicle main node directly executes the pre-generated script file without needing to frequently query the diagnostic database in real-time, thereby reducing resource overhead and improving diagnostic efficiency
Solution Approach 2:
The patent creates copy files (script files) that replicate the essential diagnostic logic and data retrieval sequences from the diagnostic database. These copy files are stored locally on the vehicle and can be executed independently without continuous access to the original database, reducing the need for frequent data retrieval operations and lowering resource consumption
2Productivity
If all diagnostic tasks require the cloud to send diagnostic sequence files and the vehicle to retrieve data and synthesize diagnostic instructions, then centralized control is maintained, but diagnostic efficiency is reduced
Solution Approach 1:
The patent pre-generates diagnostic script files containing complete diagnostic instructions and data retrieval sequences before they are needed. These pre-processed script files are uploaded to the cloud and then pushed to the vehicle in advance. When a diagnostic task is initiated, the vehicle can immediately execute the pre-prepared script file without requiring real-time data retrieval and synthesis operations, significantly reducing diagnostic time and improving efficiency
Solution Approach 2:
The patent enables the vehicle side to autonomously execute diagnostic tasks using locally stored script files. Once the script file is received and stored, the vehicle main node can independently parse and execute the diagnostic instructions without requiring continuous cloud interaction or real-time data synthesis, allowing the system to serve itself during the actual diagnostic process
Data Source
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AI summary
Provided are a fault diagnosis method, system and device, and a medium. The method includes receiving a vehicle diagnostic push message sent by a remote diagnostic server, where the vehicle diagnostic push message includes at least one of the following: a diagnostic task type or diagnostic script information; acquiring a corresponding target diagnostic script file and a corresponding target diagnostic condition based on the diagnostic task type and/or the diagnostic script information, where the target diagnostic script file is obtained from conversion of a diagnostic database and a diagnostic sequence, and the target diagnostic script file is an interpreted language file; and performing a fault diagnosis on a target vehicle according to the target diagnostic condition and the target diagnostic script file. This saves the remote diagnostic client the trouble of parsing the diagnostic sequence and frequently reading the diagnostic database, thereby reducing the load on the remote diagnostic client and improving the execution efficiency of a diagnostic task. Moreover, this improves the diagnostic efficiency by partitioning diagnostic task types.