CN-121984951-A - Vehicle audio playing optimization method, device and storage medium

Abstract

The application discloses a vehicle audio playing optimization method, device and storage medium, which comprises the steps of obtaining current data to be uploaded of a vehicle, uploading the data to be uploaded to a cloud server, wherein the cloud server determines network quality level and scene risk value of the vehicle based on the data to be uploaded, feeds back a first data strategy based on the network quality level and the scene risk value, and adjusts audio coding parameters of the vehicle, optimizes an audio transmission link, adjusts a buffer structure and/or adjusts an audio playing mode of the vehicle based on the first data strategy when the first data strategy is received.

Inventors

• LU HEJIE
• HU XINKE
• ZHANG LONG
• ZHOU XIQIN
• CHEN RONGFA
• TIAN CAN
• ZHAO JIAN
• JIANG ZHONGLIN
• CHEN YONG
• CUI YINGJIE

Assignees

• 浙江吉利控股集团有限公司
• 吉利汽车研究院（宁波）有限公司

Dates

Publication Date

20260505

Application Date

20260123

Claims (13)

1. 1. A vehicle audio playback optimization method, characterized in that the vehicle audio playback optimization method comprises: Acquiring current data to be uploaded of a vehicle, wherein the data to be uploaded comprises network state data, terminal resource data, driving state data, road condition data and/or environment data of the vehicle; Uploading the data to be uploaded to a cloud server, wherein the cloud server determines a network quality grade and a scene risk value of the vehicle based on the data to be uploaded and feeds back a first data strategy based on the network quality grade and the scene risk value, and the first data strategy comprises a first coding strategy, a first transmission strategy, a first cache strategy and/or a first playing strategy; when the first data strategy is received, adjusting audio coding parameters of the vehicle, optimizing an audio transmission link, adjusting a buffer structure and/or adjusting an audio playing mode of the vehicle based on the first data strategy.
2. 2. The vehicle audio playback optimization method of claim 1, wherein the uploading the data to be uploaded to a cloud server, wherein the cloud server determines a network quality level and a scene risk value for the vehicle based on the data to be uploaded, and feeds back a first data policy based on the network quality level and the scene risk value, comprises: Uploading the data to be uploaded to a cloud server, wherein the cloud server determines the network quality grade of the vehicle based on the network state data and/or the terminal resource data, determines a scene risk value corresponding to the vehicle based on driving state data, road condition data and/or environment data, and feeds back a first data strategy based on the network quality grade and the scene risk value.
3. 3. The vehicle audio playback optimization method of claim 2, wherein the determining a scene risk value corresponding to the vehicle based on driving state data, road condition data, and environmental data comprises: The driving risk is calculated based on the driving state data, the road condition complexity is calculated based on the road condition data, the environmental risk is determined based on the environmental data, and the scene risk value is calculated based on the driving risk, the road condition complexity and the environmental risk.
4. 4. The vehicle audio playback optimization method of claim 1, wherein upon receiving the first data policy, adjusting audio encoding parameters of the vehicle, optimizing an audio transmission link, adjusting a buffer structure, and/or adjusting an audio playback mode of the vehicle based on the first data policy comprises: Acquiring a second data strategy corresponding to a current audio scene of the vehicle; determining whether a data policy conflict exists currently based on the first data policy and the second data policy; If the data strategy conflict does not exist, adjusting the audio coding parameters of the vehicle, optimizing the audio transmission link, adjusting the cache structure and/or adjusting the audio playing mode of the vehicle based on the first data strategy.
5. 5. The vehicle audio playback optimization method of claim 4, wherein after the step of determining whether there is a data policy conflict based on the first data policy and the second data policy, the vehicle audio playback optimization method further comprises: If the data strategy conflict exists, acquiring a scene risk value and a real-time network state of the vehicle; determining a target data policy based on the first data policy, the second data policy, the scene risk value, and the real-time network state; Adjusting audio encoding parameters of the vehicle, optimizing audio transmission links, adjusting cache structures, and/or adjusting audio playback modes of the vehicle based on the target data policy.
6. 6. The vehicle audio playback optimization method of claim 5, wherein the step of determining the target data policy based on the first data policy, the second data policy, the scene risk value, and the real-time network state comprises: Determining a first target weight corresponding to the first data strategy and a second target weight corresponding to the second data strategy based on the scene risk value and the real-time network state; And determining the target data strategy based on the strategy conflict type, the first data strategy, the second data strategy, the first target weight and the second target weight corresponding to the data strategy conflict.
7. 7. The vehicle audio playback optimization method of claim 6, wherein the step of determining a first target weight corresponding to a first data policy and a second target weight corresponding to a second data policy based on the scene risk value and the real-time network state comprises: determining a first weight corresponding to the first data strategy and a second weight corresponding to the second data strategy based on the scene risk value; determining a weight change value based on a network quality level corresponding to the real-time network state; and determining a first target weight corresponding to the first data strategy and a second target weight corresponding to the second data strategy based on the weight change value, the first weight and the second weight.
8. 8. The vehicle audio playback optimization method of any one of claims 4-7, further comprising: and sending a switching request to a cloud server, wherein the cloud server generates a corresponding second data strategy according to the audio scene corresponding to the switching request and feeds back the second data strategy.
9. 9. The vehicle audio playing optimization method is characterized by being applied to a cloud server, and comprises the following steps: Receiving data to be uploaded sent by a vehicle, wherein the data to be uploaded comprises network state data, terminal resource data, driving state data, road condition data and/or environment data of the vehicle; Determining a network quality grade and a scene risk value of the vehicle based on data to be uploaded, and feeding back a first data strategy based on the network quality grade and the scene risk value, wherein the first data strategy comprises a first coding strategy, a first transmission strategy, a first cache strategy and/or a first playing strategy; and sending the first data strategy to the vehicle, wherein the vehicle adjusts the audio coding parameters of the vehicle, optimizes an audio transmission link, adjusts a buffer structure and/or an audio playing mode based on the first data strategy.
10. 10. The vehicle audio playback optimization method of claim 9, wherein the step of determining the network quality level of the vehicle and the scene risk value based on the data to be uploaded comprises: determining a network quality level of the vehicle based on the network status data and/or the terminal resource data; and determining a scene risk value corresponding to the vehicle based on the driving state data, the road condition data and/or the environment data.
11. 11. The vehicle audio playback optimization method of claim 10, wherein the determining a scene risk value for the vehicle based on driving state data, road condition data, and/or environmental data comprises: calculating driving risk based on the driving state data, calculating road condition complexity based on the road condition data, and determining environmental risk based on the environmental data; and calculating a scene risk value based on the driving risk, the road condition complexity and the environmental risk.
12. 12. A vehicle audio playback optimization apparatus, characterized in that the apparatus comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle audio playback optimization method as claimed in any one of claims 1 to 8 or 9 to 11.
13. 13. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the vehicle audio playback optimization method according to any one of claims 1 to 8 or 9 to 11.

Description

Vehicle audio playing optimization method, device and storage medium Technical Field The present application relates to the field of audio technologies, and in particular, to a method and apparatus for optimizing audio playing of a vehicle, and a storage medium. Background Along with the deep integration of the internet of vehicles and the intelligent cabin technology, the vehicle-mounted system has evolved from a single music playing function to a comprehensive service carrier integrating multi-source audio such as navigation map broadcasting, online music, radio stations, voice interaction response, emergency notification and the like. Most car owners rely on car audio services in the driving process, wherein the continuity of the core functions such as map navigation broadcasting, real-time voice interaction and the like is directly related to driving safety. However, the smoothness of audio playing is still limited by a few problems, such as dynamic network environments which are inevitably faced in the running process of vehicles, severe fluctuation of network bandwidth is commonly existed in scenes such as urban tunnels, suburban highways, high-rise dense areas and the like, the current vehicle system generally supports concurrent operation of multiple audio sources, when the weak network bandwidth is insufficient, safety audio is often blocked or delayed due to the occupation of the bandwidth of entertainment audio, serious potential safety hazards exist, the inherent limitation of audio-video hybrid coding is caused, when the network bandwidth is insufficient, the coding system can only perform code rate compression or transmission suspension on the whole audio and video, transmission of audio streams cannot be ensured independently, the existing anti-weak network technology is insufficient in adaptation of vehicle-mounted scenes, a dynamic state feedback mechanism is lacking between a cloud coding server and a vehicle terminal, the cloud cannot sense key information such as the network bandwidth, the cache occupation rate, the current audio source type and the like of the vehicle in real time, the vehicle cannot actively push certain audio to the cloud request or adjust coding parameters according to fixed strategy data, and resource allocation and actual demand are not saved. Therefore, how to improve the smoothness and stability of the audio playing of the vehicle is a problem to be solved. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a vehicle audio playing optimization method, device and storage medium, which aim to solve the technical problem of how to improve the fluency and stability of vehicle audio playing. In order to achieve the above object, the present application provides a vehicle audio playing optimization method, which includes: Acquiring current data to be uploaded of a vehicle, wherein the data to be uploaded comprises network state data, terminal resource data, driving state data, road condition data and/or environment data of the vehicle; Uploading the data to be uploaded to a cloud server, wherein the cloud server determines a network quality grade and a scene risk value of the vehicle based on the data to be uploaded and feeds back a first data strategy based on the network quality grade and the scene risk value, and the first data strategy comprises a first coding strategy, a first transmission strategy, a first cache strategy and/or a first playing strategy; when the first data strategy is received, adjusting audio coding parameters of the vehicle, optimizing an audio transmission link, adjusting a buffer structure and/or adjusting an audio playing mode of the vehicle based on the first data strategy. Further, the step of uploading the data to be uploaded to a cloud server, wherein the cloud server determines a network quality level and a scene risk value of the vehicle based on the data to be uploaded, and feeds back a first data policy based on the network quality level and the scene risk value includes: Uploading the data to be uploaded to a cloud server, wherein the cloud server determines the network quality grade of the vehicle based on the network state data and/or the terminal resource data, determines a scene risk value corresponding to the vehicle based on driving state data, road condition data and/or environment data, and feeds back a first data strategy based on the network quality grade and the scene risk value. Further, the determining the scene risk value corresponding to the vehicle based on the driving state data, the road condition data and the environmental data includes: The driving risk is calculated based on the driving state data, the road condition complexity is calculated based on the road condition data, the environmental