CN-122022828-A - Vehicle adjusting method and device, processor, electronic equipment and vehicle

CN122022828ACN 122022828 ACN122022828 ACN 122022828ACN-122022828-A

Abstract

The application discloses a vehicle adjusting method, a device, a processor, electronic equipment and a vehicle. The method comprises the steps of obtaining at least one evaluation data of a vehicle, determining risk data of the evaluation data, wherein the evaluation data are used for representing the running state of the vehicle in an evaluation dimension, the risk data are used for representing the risk degree of the vehicle in the running state, responding to the risk data to represent the risk degree which is greater than or equal to a risk degree threshold, determining a target adjustment strategy based on resource consumption data of the vehicle, wherein the resource consumption data are used for representing the degree of consumed resources required for adjusting the vehicle, the target adjustment strategy is used for representing the rule for adjusting the vehicle, and adjusting the vehicle according to the target adjustment strategy to obtain the adjusted vehicle, wherein the risk data of the adjusted vehicle are used for representing the risk degree which is smaller than the risk degree threshold. The application solves the technical problem of low adjustment accuracy of the vehicle.

Inventors

DU YUMENG
XU HAIQIANG
CHEN YUN
MEN XIN
DU HONG
LI BOSEN

Assignees

中国第一汽车股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260214

Claims (13)

1. A method of adjusting a vehicle, comprising: Acquiring at least one evaluation data of the vehicle and determining risk data of the evaluation data, wherein the evaluation data are used for representing the running state of the vehicle in an evaluation dimension, and the risk data are used for representing the risk degree of the vehicle in the running state; Determining a target adjustment policy based on resource consumption data of the vehicle in response to the risk data representing the degree of risk greater than or equal to a risk degree threshold, wherein the resource consumption data is used for representing the degree of consumed resources required for adjusting the vehicle, and the target adjustment policy is used for representing a rule for adjusting the vehicle; and adjusting the vehicle according to the target adjustment strategy to obtain the adjusted vehicle, wherein the risk data of the adjusted vehicle is used for representing the risk degree smaller than the risk degree threshold.
2. The method of claim 1, wherein obtaining at least one profile of the vehicle and determining risk data for the profile comprises: Responding to the acquired evaluation data set of the vehicle, and extracting a fault data set from the evaluation data set by using a large language model; the risk data is determined based on the fault dataset.
3. The method of claim 2, wherein determining a target adjustment strategy based on the resource consumption data of the vehicle in response to the risk data representing the risk level greater than or equal to a risk level threshold comprises: Constructing a fault tree of the vehicle based on the evaluation data of which the risk level is greater than or equal to a risk level threshold in response to the risk data representing the risk level greater than or equal to the risk level threshold, wherein the fault tree is used for determining that the evaluation data generates a target factor of which the risk level is greater than or equal to the risk level threshold; the target adjustment policy is determined based on the resource consumption data and the fault tree.
4. A method according to claim 3, wherein responsive to the risk data representing the risk level greater than or equal to a risk level threshold, constructing a fault tree for the vehicle based on the evaluation data for the risk level greater than or equal to the risk level threshold, comprises: In response to the risk data representing the risk level greater than or equal to a risk level threshold, constructing the fault tree based on the fault dataset using a large language model.
5. The method of claim 3, wherein determining the target adjustment policy based on the resource consumption data and the fault tree comprises: Acquiring a candidate adjustment strategy set of the vehicle based on the fault tree, wherein the candidate adjustment strategy set comprises at least one candidate adjustment strategy, and the candidate adjustment strategy is used for representing adjustment of the vehicle so as to process the target factors; the target adjustment strategy is determined from the candidate adjustment strategy set based on the resource consumption data of the vehicle.
6. The method of claim 5, wherein determining the target adjustment strategy from the candidate adjustment strategy set based on the resource consumption data of the vehicle comprises: and the candidate adjustment strategies with the minimum resource consumption data are collected and determined to be the target adjustment strategy.
7. The method according to any one of claims 1 to 6, further comprising: acquiring an adjusted result of the vehicle, wherein the adjusted result is used for representing the running state of the vehicle after adjustment; and updating the failure mode and influence analysis library of the vehicle based on the adjustment result.
8. An adjustment device for a vehicle, comprising: The first determining unit is used for obtaining at least one evaluation data of the vehicle and determining risk data of the evaluation data, wherein the evaluation data are used for representing the running state of the vehicle in an evaluation dimension, and the risk data are used for representing the risk degree of the vehicle in the running state; A second determining unit configured to determine, based on resource consumption data of the vehicle, a target adjustment policy in response to the risk data representing the degree of risk greater than or equal to a risk degree threshold, wherein the resource consumption data is used to represent a degree of consumed resources required to adjust the vehicle, and the target adjustment policy is used to represent a rule of adjusting the vehicle; and the adjusting unit is used for adjusting the vehicle according to the target adjusting strategy to obtain the adjusted vehicle, wherein the risk data of the adjusted vehicle is used for representing the risk degree smaller than the risk degree threshold value.
9. A processor for running a program, wherein the program when run performs the method of any one of claims 1 to 7.
10. An electronic device comprising a memory in which a computer program is stored and a processor arranged to run the computer program to perform the method of any of claims 1 to 7.
11. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer readable storage medium is located to perform the method of any one of claims 1 to 7.
12. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.
13. A vehicle comprising a memory storing an executable program and a processor for executing the program, wherein the program is executed to perform the method of any one of claims 1 to 7.

Description

Vehicle adjusting method and device, processor, electronic equipment and vehicle Technical Field The application relates to the technical field of after-sale service of vehicles, in particular to a vehicle adjusting method, a device, a processor, electronic equipment and a vehicle. Background At present, after-sales quality management of vehicles mainly depends on a traditional manual analysis mode, and the method comprises the specific processes of collecting problems, collecting quality problems fed back by users by an after-sales service department, such as incapability of starting vehicles, failure of brakes and the like, manually standing, determining whether standing is subjected to deep analysis based on the user feedback, manually verifying whether the problems are solved after correction is implemented, and manually closing problem records after the problems are solved and verified. However, the fault of the vehicle cannot be completely and accurately located only through manual experience, so that the technical problem of low adjustment accuracy of the vehicle still exists. In view of the above technical problems, no effective solution has been proposed at present. Disclosure of Invention The embodiment of the application provides a vehicle adjusting method, a device, a processor, electronic equipment and a vehicle, which are used for at least solving the technical problem of low vehicle adjusting accuracy. According to an aspect of an embodiment of the present application, there is provided a method of adjusting a vehicle. The method comprises the steps of obtaining at least one evaluation data of a vehicle, determining risk data of the evaluation data, wherein the evaluation data are used for representing the running state of the vehicle in an evaluation dimension, the risk data are used for representing the risk degree of the vehicle in the running state, responding to the risk data to represent the risk degree which is greater than or equal to a risk degree threshold, determining a target adjustment strategy based on resource consumption data of the vehicle, wherein the resource consumption data are used for representing the degree of consumed resources required for adjusting the vehicle, the target adjustment strategy is used for representing a rule for adjusting the vehicle, and adjusting the vehicle according to the target adjustment strategy to obtain the adjusted vehicle, wherein the risk data of the vehicle after adjustment are used for representing the risk degree which is smaller than the risk degree threshold. Optionally, acquiring at least one evaluation data of the vehicle and determining risk data of the evaluation data comprises extracting a fault data set from the evaluation data set by using a large language model in response to acquiring the evaluation data set of the vehicle, and determining the risk data based on the fault data set. Optionally, determining the target adjustment strategy based on the resource consumption data of the vehicle in response to the risk data representing a risk degree greater than or equal to a risk degree threshold comprises constructing a fault tree of the vehicle based on evaluation data with a risk degree greater than or equal to the risk degree threshold in response to the risk data representing a risk degree greater than or equal to the risk degree threshold, wherein the fault tree is used for determining target factors with the risk degree greater than or equal to the risk degree threshold of the evaluation data, and determining the target adjustment strategy based on the resource consumption data and the fault tree. Optionally, constructing the fault tree of the vehicle based on the evaluation data with the risk degree greater than or equal to the risk degree threshold in response to the risk data representing the risk degree greater than or equal to the risk degree threshold comprises constructing the fault tree based on the fault dataset using the large language model in response to the risk data representing the risk degree greater than or equal to the risk degree threshold. Optionally, determining the target adjustment strategy based on the resource consumption data and the fault tree comprises obtaining a candidate adjustment strategy set of the vehicle based on the fault tree, wherein the candidate adjustment strategy set comprises at least one candidate adjustment strategy, the candidate adjustment strategy is used for representing adjustment of the vehicle so as to process the target factor, and determining the target adjustment strategy from the candidate adjustment strategy set based on the resource consumption data of the vehicle. Optionally, determining the target adjustment policy from the set of candidate adjustment policies based on the resource consumption data of the vehicle includes determining the candidate adjustment policy with the smallest resource consumption data in the set of candidate adjustment policies as the targ