CN-121982801-A - Vehicle fault visualization guiding method, system, equipment and storage medium

Abstract

The application discloses a vehicle fault visualized guiding method, a system, equipment and a storage medium, which belong to the technical field of vehicles, wherein the vehicle fault visualized guiding method comprises the steps of determining that a vehicle has a fault and generating a target fault identifier corresponding to the fault under the condition that the current running state data of the vehicle meets a preset fault judging condition; the method comprises the steps of determining a target prompt word of a target fault identifier according to the target fault identifier, a user category label of a vehicle, a preset fault identifier and an operation guide mapping relation, determining a target style according to the user category label and a preset corresponding relation between a user and a style label, generating a model according to the target prompt word, the target style and a target video, generating a target video corresponding to the target fault identifier, and outputting the target video. The application can realize the dynamic visualization of the vehicle faults.

Inventors

• ZHOU XIAOBEI

Assignees

• 浙江凌艾未来科技有限公司
• 浙江零跑科技股份有限公司

Dates

Publication Date

20260505

Application Date

20251231

Claims (10)

1. 1. A vehicle fault visualization guiding method, characterized by comprising the following steps: Under the condition that the current running state data of the vehicle meets the preset fault judging condition, determining that the vehicle has a fault, and generating a target fault identifier corresponding to the fault; determining a target prompt word of the target fault identifier according to the target fault identifier, the user category label of the vehicle and a mapping relation between a preset fault identifier and an operation guide; determining a target style according to the user category label and the corresponding relation between the preset user and the style label; Generating a target video corresponding to the target fault identifier according to the target prompt word and the target style by using a target video generation model; and outputting the target video.
2. 2. The method of claim 1, wherein the mapping relationship between the preset fault identification and the operation guidance comprises a preset fault identification instruction association rule base, a preset basic operation instruction base and a preset user image adaptation rule base; The determining the target prompting word of the target fault identifier according to the target fault identifier, the user category label of the vehicle and the mapping relation between the preset fault identifier and the operation guidance comprises the following steps: determining a target natural language paragraph according to the target fault identification, the preset fault identification instruction association rule base and the preset basic operation instruction base; And converting the target natural language paragraph into the target prompt word according to the user category label and a preset user image adaptation rule base.
3. 3. The method of claim 2, wherein said determining a target natural language paragraph from said target fault identification, said preset fault identification instruction association rule base, and said preset base operation instruction base comprises: searching the preset fault identification instruction association rule base according to the target fault identification to determine a target instruction rule; And translating the target fault identification into a target natural language paragraph according to the target instruction rule and the preset basic operation instruction library.
4. 4. The method of claim 1, wherein the target video generation model comprises an encoder, an adapter and a diffusion model, wherein the generating the target video corresponding to the target fault identifier according to the target cue word and the target style using the target video generation model comprises: analyzing the target prompt word through the encoder to extract a conditional embedding vector; configuring target weights corresponding to the target styles through the adapter to obtain style feature vectors; and carrying out iterative denoising on the conditional embedded vector and the style feature vector through the diffusion model until the target video is generated under the condition of meeting the preset iteration times.
5. 5. The method of claim 4, wherein the target video generation model further comprises a temporal attention layer and a temporal convolution layer, the method further comprising: and performing time dimension processing on the conditional embedded vector and the style feature vector by using the time sequence attention layer and the time sequence convolution layer.
6. 6. The method of claim 1, wherein outputting the target video comprises: performing compression coding on the target video, and cutting the coded target video into a plurality of video fragments by adopting a preset streaming media transmission protocol; And outputting each video clip.
7. 7. The method of claim 1, wherein the target fault identification includes at least a system to which the fault belongs, a fault type, and a number.
8. 8. A vehicle fault visualization guidance system, comprising: The first determining module is used for determining that the vehicle breaks down under the condition that the current running state data of the vehicle meets the preset fault judging condition; The first generation module is used for generating a target fault identifier corresponding to the fault; the second determining module is used for determining a target prompt word of the target fault identifier according to the target fault identifier, the user category label of the vehicle and the mapping relation between the preset fault identifier and the operation guide; The third determining module is used for determining a target style according to the user category label and the corresponding relation between the preset user and the style label; The second generation module is used for generating a target video corresponding to the target fault identifier according to the target prompt word and the target style by using a target video generation model And the output module is used for outputting the target video.
9. 9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, implements the method of any of claims 1-7.
10. 10. A computer readable storage medium, having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the method according to any of claims 1-7.

Description

Vehicle fault visualization guiding method, system, equipment and storage medium Technical Field The application relates to the technical field of vehicles, in particular to a vehicle fault visualized guiding method, a system, equipment and a storage medium. Background When a vehicle breaks down, the vehicle timely reminds the fault, and helps a user to quickly know the fault, the fault treatment and the like. However, related art fault cues commonly employ static icons or text cues (e.g., dial fault lights). These approaches then fail to dynamically visualize the failure in real time, affecting the user experience. Disclosure of Invention The vehicle fault visualization guiding method, system, equipment and storage medium are provided to realize real-time dynamic visualization of faults and improve user experience. In a first aspect, a vehicle fault visualization guiding method is provided, including the following steps: Under the condition that the current running state data of the vehicle meets the preset fault judging condition, determining that the vehicle has a fault, and generating a target fault identifier corresponding to the fault; determining a target prompt word of the target fault identification according to the target fault identification, the user class label of the vehicle and the mapping relation between the preset fault identification and the operation guidance; Determining a target style according to the user category label and the corresponding relation between a preset user and the style label; And generating a target video corresponding to the target fault identifier according to the target prompt word and the target style by using the target video generation model. In some embodiments, the mapping relation between the preset fault identification and the operation guidance comprises a preset fault identification instruction association rule base, a preset basic operation instruction base and a preset user image adaptation rule base; Determining a target prompt word of the target fault identifier according to the target fault identifier, a user class label of the vehicle and a mapping relation between the preset fault identifier and the operation guide, wherein the method comprises the following steps: determining a target natural language paragraph according to the target fault identification, a preset fault identification instruction association rule base and a preset basic operation instruction base; and converting the target natural language paragraph into a target prompt word according to the user category label and a preset user image adaptation rule base. In some embodiments, determining the target natural language paragraph according to the target fault identification, the preset fault identification instruction association rule base and the preset basic operation instruction base includes: Searching a preset fault identification instruction association rule base according to the target fault identification to determine a target instruction rule; And translating the target fault identification into a target natural language paragraph according to the target instruction rule and a preset basic operation instruction library. In some embodiments, the target video generation model comprises an encoder, an adapter and a diffusion model, and the target video generation model is utilized to generate target video corresponding to the target fault identification according to the target prompt word and the target style, and comprises the following steps: analyzing the target prompt word through an encoder to extract a conditional embedding vector; configuring target weights corresponding to the target styles through an adapter to obtain style feature vectors; and carrying out iterative denoising on the conditional embedded vector and the style feature vector through the diffusion model until the condition of meeting the preset iteration times, and generating a target video. In some embodiments, the target video generation model further comprises a temporal attention layer and a temporal convolution layer, the method further comprising: and performing time dimension processing on the conditional embedded vector and the style feature vector by using the time sequence attention layer and the time sequence convolution layer. In some embodiments, outputting the target video includes: Performing compression coding on the target video, and cutting the coded target video into a plurality of video fragments by adopting a preset streaming media transmission protocol; Each video clip is output. In some embodiments, the target fault identification includes at least the system to which the fault belongs, the fault type, and the number. In a second aspect, the present application also provides a vehicle fault visualization guide system, including: the first determining module is used for determining that the vehicle breaks down under the condition that the current running state data of the vehicle meet