A method for identifying abnormal software modification in an on-board diagnostic system

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By using neural network models in the on-board diagnostic system to calculate the similarity of emission data to be confirmed and predicted, and determining whether there are abnormal software modifications, the problem of abnormal modification of exhaust emission data in the on-board diagnostic system is solved, and the safety of detection and the authenticity of data are improved.

CN119065880BActive Publication Date: 2025-03-21ZHILIAN WANWEI TECH CO LTD

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Patent Information

Application Number: CN202411079921.1
Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner
Filing Date: 2024-08-07
Publication Date: 2025-03-21
Estimated Expiration: 2044-08-07

Application Information

Patent Timeline

07 Aug 2024

Application

CN202411079921.1

21 Mar 2025

Publication

CN119065880B

IPC: G06F11/07; G06F18/22; G06N3/08

CPC: G06F11/0793; G06F11/079; G06F11/0739; G06F18/22; G06N3/08

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Application Domain

Non-redundant fault processing Neural learning methods

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AI Technical Summary

Technical Problem

How to determine whether the exhaust emissions detected by the on-board diagnostic system OBD are abnormally modified by other software to prevent false health status reports.

Method used

By obtaining the emission data to be confirmed and driving information of the target vehicle, input the neural network model to obtain the predicted emission data, and calculate the similarity between the confirmed and predicted emission data. If the similarity is low, it is judged that there is abnormal software modification.

Benefits of technology

It improves the safety of the on-board diagnostic system detection, ensures the authenticity of exhaust emission data, and prevents false health status reports caused by abnormal software modifications.

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Abstract

The present invention provides a method for identifying abnormal software modification in an on-board diagnostic system, and relates to the field of on-board system safety. The method comprises: obtaining a list of emission data to be confirmed uploaded by a target on-board diagnostic system of a target vehicle, obtaining a target driving information list and inputting the target driving information list into a target neural network model, obtaining a target predicted emission data list, obtaining target similarity between the emission data to be confirmed and the target predicted emission data, thereby obtaining a target similarity list, and determining based on the similarity list whether there is abnormal software modification in the target on-board diagnostic system of the target vehicle; the present invention determines whether there is abnormal software modification by the similarity between the emission data to be confirmed and the target predicted emission data, so as to improve the safety of detection of the target on-board diagnostic system.

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