Communication parameter determination method and device, equipment and storage medium

Abstract

The invention discloses a communication parameter determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring to-be-analyzed information when a vehicle communicates based on a WebRTC (Web Real-Time Communication) mode, wherein the to-be-analyzed information comprises communication index data for reflecting communication quality, environmental data in a vehicle driving area and vehicle parameters of the vehicle; based on the to-be-analyzed information and a communication quality evaluation model, determining a communication quality evaluation result when the vehicle communicates; processing historical data during communication between the to-be-analyzed information and the vehicle by using a root cause analysis method and a fault tree analysis method to obtain an anomaly analysis report during vehicle communication; and adjusting parameters of the vehicle during communication based on the abnormal analysis report and the communication quality evaluation result. The vehicle network communication quality and the abnormity analysis report are obtained based on the multi-dimensional data, so that the parameters of the vehicle are dynamically adjusted, and the communication quality of the vehicle during communication based on a WebRTC mode in a complex and changeable network environment is relatively good.

Description

Technical Field

[0001] The present invention relates to the field of information processing and in particular to a method, device, equipment and storage medium for determining communication parameters. Background Art

[0002] In the prior art, WebRTC (Web Real-Time Communication) is a technology that supports real-time audio and video communication and is widely used in related scenarios of the Internet of Vehicles. For example, it is used in scenarios of the Internet of Vehicles such as in-vehicle entertainment systems, intelligent driving assistance, and remote diagnosis. Since the network environment faced by vehicles during movement is changeable and complex, it is difficult to fully reflect the complex and changeable network environment using basic network communication indicators. In the prior art, the communication quality evaluation and anomaly detection of vehicles communicating based on WebRTC mainly rely on basic network communication indicators, which makes it impossible to fully reflect the network communication quality of vehicles communicating based on WebRTC in a complex and changeable network environment. And the accuracy of diagnosing network communication anomalies in vehicles in a complex and changeable network environment based on basic network communication indicators is also low. Therefore, for complex and changeable network environments, the effect of using network communication quality and anomaly results obtained based on basic network communication indicators to solve the problems of communication anomalies and poor communication quality during WebRTC communication is poor. As a result, in a complex and changeable network environment, the communication quality of vehicles communicating based on WebRTC is poor. Summary of the invention

[0003] The embodiment of the present application provides an implementation solution different from the prior art to solve the technical problem of poor communication quality when vehicles communicate based on WebRTC in a complex and changeable network environment.

[0004] In a first aspect, the present application provides a communication parameter determination method, which is applicable to a server, comprising: obtaining information to be analyzed when a vehicle communicates based on web real-time communication (WebRTC), the information to be analyzed comprising: communication indicator data used to reflect communication quality, environmental data within the vehicle's driving area, and the vehicle's own parameters; determining a communication quality evaluation result when the vehicle communicates based on the WebRTC method based on the information to be analyzed and a communication quality evaluation model; using a root cause analysis method and a fault tree analysis method to process the information to be analyzed and historical data when the vehicle communicates based on the WebRTC method, to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the cause of the abnormality, the type of abnormality, the range of the abnormality, and a suggestion for repairing the abnormality; and adjusting the parameters of the vehicle when communicating based on the WebRTC method based on the abnormality analysis report and the communication quality evaluation result.

[0005] In a second aspect, the present application provides a communication parameter determination device, which is applicable to a server, and includes: an acquisition unit, which is used to acquire information to be analyzed when a vehicle communicates based on web real-time communication WebRTC, and the information to be analyzed includes: communication indicator data used to reflect communication quality, environmental data in the vehicle's driving area, and the vehicle's own parameters; a determination unit, which is used to determine a communication quality evaluation result when the vehicle communicates based on the WebRTC method based on the information to be analyzed and a communication quality evaluation model; an analysis unit, which is used to process the information to be analyzed and historical data when the vehicle communicates based on the WebRTC method using a root cause analysis method and a fault tree analysis method, and obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the abnormal cause, abnormality type, abnormality range and abnormality repair suggestion; an adjustment unit, which is used to adjust the parameters of the vehicle when communicating based on the WebRTC method based on the abnormality analysis report and the communication quality evaluation result.

[0006] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any method in the first aspect or any possible implementation manner of the first aspect by executing the executable instructions.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements any method in the first aspect or any possible implementation manner of the first aspect.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in the first aspect or any possible implementation manner of the first aspect.

[0009] The present application provides a method for obtaining information to be analyzed when a vehicle communicates based on web real-time communication (WebRTC), wherein the information to be analyzed includes: communication indicator data for reflecting communication quality, environmental data in the vehicle's driving area, and the vehicle's own parameters; determining a communication quality evaluation result when the vehicle communicates based on the WebRTC based on the information to be analyzed and a communication quality evaluation model; processing the information to be analyzed and historical data when the vehicle communicates based on the WebRTC using a root cause analysis method and a fault tree analysis method to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC, wherein the abnormality analysis report includes an abnormality cause, abnormality type, abnormality range, and abnormality repair suggestion; and a scheme for adjusting parameters of the vehicle when communicating based on the WebRTC based on the abnormality analysis report and the communication quality evaluation result.

[0010] Among them, the communication quality evaluation results of the vehicle when communicating based on WebRTC are obtained by using the communication indicator data, environmental data, vehicle data and other data of different dimensions when the vehicle is running, so as to fully and accurately reflect the network communication quality, and the root cause analysis method and the fault tree analysis method are used to process the information to be analyzed and the historical data of the vehicle when communicating based on the WebRTC method to obtain an abnormal analysis report, which improves the accuracy of obtaining the abnormal analysis report of the abnormality of the vehicle in a complex and changeable network environment. Therefore, the parameters of the vehicle are dynamically adjusted based on the communication quality evaluation results and the abnormal analysis report, so that the communication quality of the vehicle when communicating based on WebRTC in a complex and changeable network environment is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0012] Figure 1 A flow chart of a method for determining communication parameters provided in one embodiment of the present application;

[0013] Figure 2A schematic diagram of the structure of a communication parameter determination device provided in one embodiment of the present application;

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, but cannot be understood as limiting the present application.

[0016] The terms "first" and "second" etc. in the specification, claims and drawings of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0017] First, some terms in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

[0018] WebRTC, (Web Real-Time Communication), is a technology that supports web browsers for real-time voice or video conversations. WebRTC is a technology that enables web applications and sites to capture and selectively stream audio or video media, as well as exchange arbitrary data between browsers without the need for middleware.

[0019] RTT (Round-Trip Time) is a performance indicator in computer networks. It represents the total delay from the time the sender sends data to the time the sender receives confirmation from the receiver (the receiver sends confirmation immediately after receiving the data).

[0020] Packet loss rate refers to the ratio of the number of data packets lost in communication to the number of data groups sent.

[0021] Jitter refers to the degree of variation in packet delay.

[0022] Bandwidth utilization refers to the ratio of actual traffic on a communication link to the theoretical bandwidth of the link, which is used to identify the traffic situation and busyness of the link.

[0023] Video resolution is used to measure the amount of data in an image.

[0024] Frame rate refers to the frequency (rate) at which bitmap images in frames appear continuously on the display.

[0025] Audio quality refers to the quality of the sound recorded in the audio file.

[0026] Audio bit rate refers to the amount of data per unit time in an audio file, which is used to measure audio quality. The higher the bit rate, the better the audio quality is usually, with richer details and less distortion.

[0027] Volume level, used to indicate the loudness of a sound.

[0028] In the prior art, the basic technical principles of WebRTC include the collection, encoding, transmission and decoding of audio and video data, and its basic technical principles mainly rely on the stability of the network and the quality of bandwidth. Therefore, in an ideal network environment, when communicating based on WebRTC, good communication quality can be guaranteed. In an ideal network environment, the quality evaluation of communication based on WebRTC mainly focuses on some basic network communication indicators. Based on the basic network communication indicators, the communication quality of communication based on WebRTC in an ideal network environment can be comprehensively and accurately measured.

[0029] WebRTC (Web Real-Time Communication) is a technology that supports real-time audio and video communication and is widely used in related scenarios of the Internet of Vehicles. For example, it is used in scenarios of the Internet of Vehicles such as in-vehicle entertainment systems, intelligent driving assistance, and remote diagnosis. Since the network environment faced by vehicles during movement is changeable and complex, it is difficult to fully reflect the complex and changeable network environment using basic network communication indicators. However, the communication quality evaluation and anomaly detection when vehicles communicate based on WebRTC mainly rely on basic network communication indicators, and lack network quality evaluation indicators for complex and changeable network environments. This makes it impossible to fully reflect the network communication quality when vehicles communicate based on WebRTC in a complex and changeable network environment, and it is impossible to accurately evaluate the user experience effect. It is also difficult to dynamically adjust communication parameters according to the actual network conditions, and it is impossible to quickly and effectively solve the problem of poor network communication quality. When the problem of poor communication quality occurs in a complex and changeable network environment, it is impossible to intelligently and adaptively regulate and control, and it is impossible to ensure continuous, stable and good network communication quality.

[0030] The accuracy of diagnosing network communication anomalies in vehicles in complex and changeable network environments based on basic network communication indicators is also low. When communication anomalies occur, it is impossible to quickly locate and analyze complex communication anomalies, affecting the timeliness and accuracy of fault handling. Therefore, for complex and changeable network environments, the effect of using network communication quality and anomaly results obtained based on basic network communication indicators to solve communication anomalies and poor communication quality problems during WebRTC communication is poor. As a result, in complex and changeable network environments, the communication quality of vehicles communicating based on WebRTC is poor.

[0031] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0032] Figure 1 A flow chart of a communication parameter determination method provided by an exemplary embodiment of the present application is provided. The execution subject of the method may be a server. The method at least includes the following steps S101-S104:

[0033] S101, obtaining information to be analyzed when a vehicle communicates based on a web real-time communication (WebRTC) method, wherein the information to be analyzed includes: communication indicator data reflecting communication quality, environmental data in a driving area of ​​the vehicle, and vehicle parameters of the vehicle itself;

[0034] Optionally, the communication indicator data includes data corresponding to indicators such as RTT, packet loss rate, jitter, bandwidth utilization, video resolution, frame rate, network signal strength and audio quality, among which the audio quality can be measured by data such as audio bit rate and volume level.

[0035] Optionally, the communication indicator data may be collected in real time based on a built-in interface of WebRTC.

[0036] Optionally, the environmental data includes data corresponding to a geographical location indicator of a vehicle driving area.

[0037] Optionally, the vehicle's own parameters include data corresponding to a speed index of a vehicle driving area.

[0038] Optionally, the vehicle's own parameters and the environmental data may be acquired based on a sensor system of the vehicle.

[0039] S102, determining a communication quality evaluation result when the vehicle communicates based on the WebRTC method based on the information to be analyzed and a communication quality evaluation model;

[0040] S103, using a root cause analysis method and a fault tree analysis method to process the information to be analyzed and the historical data when the vehicle communicates based on the WebRTC method, to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes an abnormality cause, an abnormality type, an abnormality range, and an abnormality repair suggestion;

[0041] Optionally, in the aforementioned S103, the root cause analysis method and the fault tree analysis method are used to process the historical data of the information to be analyzed and the vehicle when communicating based on the WebRTC method, so as to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the abnormality cause, abnormality type, abnormality range and abnormality repair suggestion, including the following steps S01-S05:

[0042] S01, determining whether there is an abnormality when the vehicle communicates based on the WebRTC method based on the communication indicator data in the information to be analyzed and a preset abnormality rule;

[0043] Optionally, the preset exception rule is a set of exception judgment rules preset according to actual needs, and is used to determine whether an abnormality occurs when the vehicle communicates based on the WebRTC method.

[0044] Specifically, the preset exception rules in the present application can be: when the RTT (round trip time) exceeds 100ms, an abnormality of high network delay occurs; when the packet loss rate exceeds 10%, an abnormality of high packet loss rate occurs; when the jitter exceeds 20ms, an abnormality of high jitter occurs; when the network signal strength is lower than -85dBm, an abnormality of low network signal strength occurs, and when the bandwidth utilization rate exceeds 80%, an abnormality of network congestion occurs; when the video resolution is <480p, an abnormality of poor picture quality occurs; when the frame rate is <15 frames / second, an abnormality of poor picture smoothness occurs; when the audio bit rate is <32kbps, an abnormality of poor sound quality occurs; when the volume level is >-10db, an abnormality of harsh volume occurs; when the volume level is <-60db, an abnormality of unclear voice content occurs.

[0045] For example, when the preset abnormal rules are: when RTT (round trip time) exceeds 100ms, the abnormality of high network delay occurs; when the packet loss rate exceeds 10%, the abnormality of high packet loss rate occurs; when the jitter exceeds 20ms, the abnormality of high jitter occurs; when the network signal strength is lower than -85dBm, the abnormality of low network signal strength occurs, and the real-time detection in the information to be analyzed is that the RTT is 120ms, the packet loss rate is 15%, the jitter is 25ms, and the network signal strength is -90dBm. The system detects that these indicators exceed the set thresholds and are therefore determined to be abnormal.

[0046] Optionally, in the aforementioned S01, determining whether there is an abnormality when the vehicle communicates based on the WebRTC method based on the communication indicator data in the information to be analyzed and a preset abnormality rule includes:

[0047] If the communication indicator data in the information to be analyzed meets the preset exception rules, it is determined that there is an abnormality when the vehicle communicates based on the WebRTC method, an exception result is generated, and new communication indicator data is composed of the exception result and the communication indicator data as the communication indicator data, wherein the exception result includes one or more of the following exception types: an exception type with high network delay; an exception type with high packet loss rate; an exception type with high jitter; an exception type with low network signal strength, etc.

[0048] If the communication indicator data in the information to be analyzed does not meet the preset abnormality rules, it is determined that there is no abnormality when the vehicle communicates based on the WebRTC method, and it is determined that the network communication quality is good, and the parameters of the vehicle when communicating based on the WebRTC method are not adjusted.

[0049] In summary, by setting the preset exception rules, it can be ensured that an exception occurs when the vehicle communicates based on the WebRTC method in a timely and accurate manner.

[0050] S02, in response to an abnormality when the vehicle communicates based on the WebRTC method, analyzing the communication indicator data using a fault tree analysis method to obtain a first abnormality type and a first abnormality cause of the abnormality;

[0051] Optionally, the fault tree analysis method may refer to the prior art, which will not be described in detail here.

[0052] Optionally, the first abnormality type includes one or more of the following abnormality types: an abnormality type with high network delay; an abnormality type with high packet loss rate; an abnormality type with high jitter; an abnormality type with low network signal strength, etc.

[0053] Optionally, the first abnormal reason includes one or more of the following abnormal reasons: network congestion, routing problems, signal interference, equipment failure, network instability, data packet disorder, etc.

[0054] S03, using a root cause analysis method to analyze the environmental data in the information to be analyzed, the vehicle parameters in the information to be analyzed, and historical data when the vehicle communicates based on the WebRTC method, to obtain a second abnormality type and a second abnormality cause of the abnormality;

[0055] Optionally, the historical data includes the time, geographic location, type of anomaly and cause of anomaly that occurs when the vehicle communicates based on the WebRTC method.

[0056] Optionally, the second abnormality type includes one or more of the following abnormality types: an abnormality type with high network delay; an abnormality type with high packet loss rate; an abnormality type with high jitter; an abnormality type with low network signal strength, etc.

[0057] Optionally, the second abnormal reason includes one or more of the following abnormal reasons: poor network coverage and high vehicle speed.

[0058] Exemplarily, the historical data includes a time period from 10 am to 12 noon, an abnormal type of low network signal strength and an abnormal type of high packet loss rate that occurred in a mountainous geographical location, and the abnormal cause is poor network coverage in the mountainous area.

[0059] S04: determining a target abnormality cause and abnormality repair suggestions based on the first abnormality cause and the second abnormality cause, and determining a target abnormality type and abnormality range based on the first abnormality type and the second abnormality type;

[0060] Optionally, in the aforementioned S04, determining a target abnormality cause and an abnormality repair suggestion based on the first abnormality cause and the second abnormality cause includes:

[0061] The first abnormality cause and the second abnormality cause constitute a target abnormality type, wherein the same abnormality cause between the first abnormality cause and the second abnormality cause is recorded once in the target abnormality cause;

[0062] Based on the target abnormality cause, an abnormality repair suggestion corresponding to the target abnormality cause is obtained.

[0063] Exemplarily, if the target abnormality is caused by network congestion and signal interference, the corresponding abnormality repair suggestions are to check network coverage, optimize routing configuration, and use high-gain antennas.

[0064] If the target abnormality cause is high vehicle speed, the corresponding abnormality repair suggestion is to reduce the vehicle speed.

[0065] Among them, the exception repair suggestions corresponding to the target exception cause can be set according to actual needs, which is only used as an example here.

[0066] Optionally, in the aforementioned S04, determining a target exception type and an exception range where the exception occurs based on the first exception type and the second exception type includes:

[0067] The first exception type and the second exception type form a target exception type, wherein the same exception type in the first exception type and the second exception type is recorded once in the target exception type;

[0068] An abnormality range corresponding to the target abnormality type is acquired based on the target abnormality type.

[0069] Optionally, the abnormal range refers to the impact range of the abnormality on the communication quality of the vehicle and the impact range on the user experience.

[0070] For example, if the target abnormality type is high network delay, high packet loss rate, high jitter, and low network signal strength, the abnormal range is that the overall communication quality of the vehicle is significantly reduced, directly affecting the user experience. If the target abnormality type is low network signal strength, the abnormal range is that the communication quality of the vehicle video is significantly reduced, and the communication quality of the audio is reduced, which directly affects the user's video communication experience.

[0071] If the target abnormality type is high packet loss rate, the abnormal range is the degradation of video quality and audio quality during communication. If the target abnormality type is high network latency, the abnormal range is the asynchrony of audio and video during communication, and the difficulty of real-time interaction during communication. If the target abnormality type is high jitter, the abnormal range is the unstable video screen and discontinuous audio during communication. If the target abnormality type is high network latency and high packet loss rate, the abnormal range is the interruption of calls during communication and the significant degradation of overall communication quality. Among them, the abnormal range corresponding to the target abnormality type can be set according to actual needs, and this is only used as an example.

[0072] S05. Generate an exception analysis report based on the target exception cause, the target exception type, the exception range, and the exception repair suggestion.

[0073] S104. Adjusting parameters of the vehicle when communicating in the WebRTC manner based on the abnormality analysis report and the communication quality assessment result.

[0074] Optionally, in the aforementioned S104, the parameters include first information and second information, and adjusting the parameters of the vehicle when communicating based on the WebRTC method based on the abnormal analysis report and the communication quality assessment result includes the following steps S001-S002:

[0075] S001. Adjusting the following first information when the vehicle communicates based on the WebRTC method based on the abnormal analysis report, wherein the first information includes video resolution and / or audio resolution;

[0076] Optionally, in the aforementioned S001, adjusting the following first information when the vehicle communicates based on the WebRTC method based on the abnormal analysis report, the first information including video resolution and / or audio resolution, includes:

[0077] Based on the abnormal analysis report, the following first information when the vehicle communicates based on the WebRTC method is increased or decreased, wherein the first information includes the video resolution and / or the audio resolution.

[0078] For example, the cause of the exception in the exception analysis report is poor network coverage, the exception type is low network signal strength, and the exception repair suggestion is to reduce the demand for bandwidth. The system automatically reduces the video resolution, for example, from 480p to 360p, and lowers the audio sampling rate to reduce bandwidth requirements, and tries to re-establish a stable network connection.

[0079] S002. Adjust the following second information when the vehicle communicates based on the WebRTC method based on the communication quality evaluation result, wherein the second information includes: bit rate, video resolution and encoding method parameters.

[0080] Optionally, in the aforementioned S002, the following second information when the vehicle communicates based on the WebRTC method is adjusted based on the communication quality evaluation result, the second information including: bit rate, video resolution and encoding method parameters, including:

[0081] If the communication quality evaluation result is lower than the preset communication threshold, the bit rate is reduced, the video resolution is reduced, and the encoding method parameters are modified.

[0082] If the communication quality evaluation result is not lower than the preset communication threshold, the bit rate is not adjusted, the video resolution is reduced, and the encoding method parameters are modified.

[0083] For example, when the communication quality assessment result is 2.5 and the preset communication threshold is 3.0, the system will: reduce the bit rate from the original 1500kbps to 1000kbps; reduce the video resolution from 480p to 360p; and modify the encoding mode parameters from the default encoding to the adaptive bit rate (TFRC, NADA) algorithm optimized encoding.

[0084] In summary, by reducing the bit rate to reduce bandwidth requirements, by reducing the video resolution to reduce network load, and by modifying the encoding method parameters, the video quality under low bandwidth conditions is ensured. This enables real-time adjustment of the parameters of the vehicle's communication based on the WebRTC method based on a complex and changeable network environment, ensures the communication quality of the vehicle's communication based on the WebRTC method in a complex and changeable network environment, and provides users with a better user experience.

[0085] Optionally, in order to make the communication quality assessment result obtained after the communication quality assessment model processes the information to be analyzed more accurate, the method further includes the following steps S1-S3:

[0086] S1. Acquire initial sample data when the vehicle communicates based on the WebRTC method, wherein the initial sample data includes: initial sample communication indicator data for reflecting communication quality, initial sample environment data in the vehicle driving area, and initial vehicle sample parameters of the vehicle;

[0087] Optionally, the initial sample communication indicator data includes data corresponding to indicators such as RTT, packet loss rate, jitter, bandwidth utilization, video resolution, frame rate, network signal strength and audio quality, among which the audio quality can be measured by data such as audio bit rate and volume level.

[0088] Optionally, the initial sample communication indicator data may be collected in real time based on a built-in interface of WebRTC.

[0089] Optionally, the initial sample environment data includes data corresponding to a geographical location indicator of a vehicle driving area.

[0090] Optionally, the initial vehicle sample parameters include data corresponding to a vehicle speed index of a vehicle driving area.

[0091] Optionally, the initial vehicle sample parameters and the initial sample environment data may be acquired based on a sensor system of the vehicle.

[0092] S2, cleaning, denoising, and standardizing the initial sample data to obtain target sample data;

[0093] Optionally, the initial sample data is cleaned, denoised, and standardized in the aforementioned S2 to obtain target sample data, including the following steps S0001-S0002:

[0094] S0001. Clean and denoise the initial sample data by comparing whether the initial sample data is within a preset data range to obtain intermediate sample data;

[0095] Exemplarily, check whether the RTT value is within a preset reasonable range, and treat the value exceeding the reasonable range as an abnormal value, and remove the abnormal value. The preset reasonable range can be set according to actual needs, for example, it can be set to a range of 10ms-200ms or 0.1ms-10ms, etc. The reasonable range here is only used as an example and is not limited.

[0096] Check whether the packet loss rate is within the preset packet loss rate range, and treat the data outside the packet loss rate range as outliers, and remove the outliers. The packet loss rate range can be set according to actual needs, for example, it can be set to 0-1, 0.1-1, etc. The packet loss rate range here is only used as an example and is not limited. For other initial sample data, refer to the processing method of RTT and packet loss rate, and clean and denoise the initial sample data, which will not be described in detail here.

[0097] S0002, normalizing the intermediate sample data in a normalized manner and based on a timestamp format to obtain target sample data.

[0098] Optionally, the intermediate sample data is normalized to a range of 0-1 by normalization.

[0099] Optionally, the time format of the data generation time corresponding to the data processed in the normalization manner is standardized into a timestamp format to obtain target sample data.

[0100] Optionally, the target sample data may be stored for training an initial large language model.

[0101] S3. Using the target sample data to train the initial large language model, obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

[0102] In summary, by cleaning, denoising and standardizing the initial sample data, abnormal data is removed and high-quality target sample data with consistent format is obtained. This not only makes it easier to use the target sample data to train a large language model, but also makes the communication quality assessment model obtained by training the large language model based on the target sample data more accurate.

[0103] Optionally, in the aforementioned S3, before the initial large language model is trained using the target sample data to obtain the communication quality evaluation model for evaluating the communication quality of the vehicle when communicating based on the WebRTC method, the method further includes: classifying the target sample data according to a plurality of preset information types to obtain first sample data corresponding to each information type in the plurality of information types;

[0104] Optionally, the information type refers to an indicator describing the target sample data, including RTT, packet loss rate, jitter, bandwidth utilization, video resolution, frame rate, audio quality (audio bit rate and volume level), geographic location, network signal strength and vehicle speed.

[0105] The aforementioned step S3 uses the target sample data to train the initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, including the following steps S31-S32:

[0106] S31, obtaining multiple information types and numerical features corresponding to each information type;

[0107] Optionally, the numerical feature refers to a key feature corresponding to an indicator (information type) and can represent the data corresponding to the indicator.

[0108] Specifically, the numerical feature corresponding to the information type may be one numerical feature or multiple numerical features. The numerical feature corresponding to the information type is set according to actual needs.

[0109] Exemplarily, the numerical characteristics corresponding to the RTT indicator may be the mean and variance; the numerical characteristics corresponding to the packet loss rate indicator may be the peak value and frequency; the numerical characteristics corresponding to the jitter indicator may be the changing trend of the data corresponding to the jitter indicator; the numerical characteristics corresponding to the bandwidth utilization indicator may be the peak value and the frequency of high bandwidth utilization events; the numerical characteristics corresponding to the video resolution indicator may be the minimum value and the frequency of resolution adjustment; the numerical characteristics corresponding to the frame rate indicator may be the minimum value and the frame loss rate (the proportion of lost frames per unit time); the numerical characteristics corresponding to the network signal strength indicator may be the minimum value and the signal strength change rate (the speed and frequency of signal strength change); the numerical characteristics corresponding to the audio quality indicator may be the minimum value; the numerical characteristics corresponding to the vehicle speed indicator may be the maximum value and the change rate (the frequency and speed of vehicle speed change); the numerical characteristics corresponding to the geographic location indicator may be the signal coverage and the location change frequency (the frequency of geographic location change).

[0110] S32. Based on the multiple information types, the numerical features corresponding to each information type, and the first sample data corresponding to each information type, the initial large language model is trained to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

[0111] Optionally, in the aforementioned S32, the initial large language model is trained based on the multiple information types, the numerical features corresponding to the respective information types, and the first sample data corresponding to the respective information types to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, including the following steps S321-S322:

[0112] S321. For each information type in the multiple information types, for each numerical feature in at least one numerical feature corresponding to the information type, extract a numerical value corresponding to the numerical feature from the first sample data corresponding to the information type, obtain at least one numerical value corresponding to the at least one numerical feature, and obtain at least one set of numerical feature information corresponding to the information type;

[0113] Optionally, in the aforementioned S321, for each numerical feature in the at least one numerical feature corresponding to the information type, extracting a numerical value corresponding to the numerical feature from the first sample data corresponding to the information type to obtain at least one numerical value corresponding to the at least one numerical feature includes:

[0114] For each data feature in at least one numerical feature corresponding to the information type, a method for obtaining a numerical feature value corresponding to the numerical feature is obtained; based on the method for obtaining a numerical feature value, the first sample data corresponding to the information type is processed to obtain a numerical value corresponding to the numerical feature, thereby obtaining at least one numerical value corresponding to the at least one numerical feature.

[0115] Optionally, the method for obtaining the numerical feature value is a method for obtaining a numerical value corresponding to the numerical feature. For example, if the numerical value corresponding to the numerical feature of the mean corresponding to the RTT indicator is obtained, based on the method for obtaining the numerical feature as the method for obtaining the mean, the first sample data is processed by the method for obtaining the mean to obtain the numerical value corresponding to the mean; if the numerical value corresponding to the numerical feature of the variance corresponding to the RTT indicator is obtained, based on the method for obtaining the numerical feature as the method for obtaining the variance, the first sample data is processed by the method for obtaining the variance to obtain the numerical value corresponding to the variance.

[0116] S322. Based on the multiple information types, at least one set of numerical feature information corresponding to each information type is used to train an initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

[0117] Optionally, the set of numerical feature information includes numerical features and numerical values ​​corresponding to the numerical features. In the aforementioned S322, based on the multiple information types, at least one set of numerical feature information corresponding to each information type is trained on the initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, including the following steps S991-S993:

[0118] S991, using a feature selection algorithm to determine a weight corresponding to a numerical feature in at least one set of numerical feature information corresponding to each signal type;

[0119] S992. Based on the weights, the multiple information types, and at least one set of numerical feature information corresponding to each information type, and using a cross-validation method and a hyperparameter tuning method, train the initial large language model to obtain an intermediate large language model;

[0120] S993. Use the loss function to detect whether the intermediate large language model is qualified, and update the weight through the back propagation algorithm. If qualified, use the qualified intermediate large language model as the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method; if unqualified, repeat the above steps S991-S993 until the communication quality evaluation model is obtained.

[0121] Optionally, in order to quickly maintain the continuity and stability of communication when a decline in network quality is detected, the method also includes: in response to the network signal strength in the communication indicator data being lower than a preset network signal strength, switching the network used by the vehicle for communicating based on the WebRTC method to a backup network.

[0122] Exemplarily, when the Wi-Fi network signal strength is -90dBm, which is lower than the preset network signal strength of -85dBm, Wi-Fi is switched to 4G mobile data.

[0123] Optionally, before switching the network used by the vehicle to communicate based on the WebRTC method to the backup network, the system first buffers the current data stream and then switches the network to the backup network, so as to ensure that there is no data loss or audio and video interruption during the switching, and ensure that the user does not notice the process of network switching, thereby improving the user experience.

[0124] In summary, the present application provides a method for obtaining information to be analyzed when a vehicle communicates based on web real-time communication (WebRTC), wherein the information to be analyzed includes: communication indicator data reflecting communication quality, environmental data within the vehicle's driving area, and the vehicle's own parameters; determining a communication quality evaluation result when the vehicle communicates based on the WebRTC method based on the information to be analyzed and a communication quality evaluation model; using a root cause analysis method and a fault tree analysis method to process the information to be analyzed and historical data when the vehicle communicates based on the WebRTC method, to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the cause of the abnormality, the type of abnormality, the range of the abnormality, and a suggestion for repairing the abnormality; and a scheme for adjusting the parameters of the vehicle when communicating based on the WebRTC method based on the abnormality analysis report and the communication quality evaluation result.

[0125] Among them, the communication quality evaluation result of the vehicle when communicating based on WebRTC is obtained by using data of various dimensions such as communication indicator data, environmental data and vehicle data during vehicle operation, so as to fully and accurately reflect the network communication quality in a complex and changeable network environment, and more accurately evaluate the user experience effect. And the root cause analysis method and the fault tree analysis method are used to process the information to be analyzed and the historical data of the vehicle when communicating based on the WebRTC method to obtain an abnormal analysis report, which can quickly locate the cause of the abnormality and improve the efficiency of handling faults. And the abnormal analysis report is obtained by using data of various dimensions, which improves the accuracy of obtaining the abnormal analysis report of the abnormality of the vehicle in a complex and changeable network environment, and improves the accuracy of the abnormality of the disputed vehicle in a complex and changeable network environment. Therefore, the communication parameters of the vehicle are dynamically adjusted based on the communication quality evaluation results and the abnormal analysis report, so that the communication quality of the vehicle in a complex and changeable network environment is better when communicating based on WebRTC.

[0126] It can detect the vehicle's communication quality in real time in a complex and ever-changing environment. When vehicle communication is abnormal, it can quickly locate the cause of the abnormality and repair it. It can also detect the vehicle's network signal strength in real time. When it detects that the network quality has deteriorated, it will automatically switch to the backup network and ensure that the network switching process is seamless, thus maintaining the continuity and stability of communication.

[0127] Figure 2 A schematic diagram of a communication parameter determination device provided for an exemplary embodiment of the present application; applicable to a server, wherein the device includes:

[0128] The acquisition unit 21 is used to acquire information to be analyzed when the vehicle communicates based on the web real-time communication WebRTC, and the information to be analyzed includes: communication indicator data reflecting the communication quality, environmental data in the vehicle driving area, and the vehicle's own parameters;

[0129] A determination unit 22, configured to determine a communication quality evaluation result when the vehicle communicates in the WebRTC manner based on the information to be analyzed and a communication quality evaluation model;

[0130] The analysis unit 23 is used to process the information to be analyzed and the historical data when the vehicle communicates based on the WebRTC method by using a root cause analysis method and a fault tree analysis method, and obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes an abnormality cause, an abnormality type, an abnormality range, and an abnormality repair suggestion;

[0131] The adjustment unit 24 is used to adjust the parameters of the vehicle when communicating based on the WebRTC method based on the abnormal analysis report and the communication quality evaluation result.

[0132] Optionally, the device is also used to: obtain initial sample data when the vehicle communicates based on the WebRTC method, the initial sample data including: initial sample communication index data for reflecting communication quality, initial sample environment data within the vehicle's driving area, and initial vehicle sample parameters of the vehicle; clean, denoise, and standardize the initial sample data to obtain target sample data; use the target sample data to train an initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

[0133] Optionally, before the device is used to train the initial large language model using the target sample data to obtain the communication quality assessment model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, the device is also used to: classify the target sample data according to a plurality of preset information types to obtain first sample data corresponding to each of the plurality of information types; when the device is used to train the initial large language model using the target sample data to obtain the communication quality assessment model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, the device is specifically used to: obtain multiple information types and numerical features corresponding to each information type; based on the multiple information types, the numerical features corresponding to each information type, and the first sample data corresponding to each information type, train the initial large language model to obtain the communication quality assessment model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

[0134] Optionally, the device is used to train the initial large language model based on the multiple information types, the numerical features corresponding to each information type, and the first sample data corresponding to each information type to obtain the communication quality assessment model for evaluating the communication quality of the vehicle when communicating based on the WebRTC method, and is specifically used to: for each information type in the multiple information types, for each numerical feature in at least one numerical feature corresponding to the information type, extract the numerical value corresponding to the numerical feature from the first sample data corresponding to the information type, obtain at least one numerical value corresponding to the at least one numerical feature, and obtain at least one group of numerical feature information corresponding to the information type; based on the multiple information types, train the initial large language model with at least one group of numerical feature information corresponding to each information type to obtain the communication quality assessment model for evaluating the communication quality of the vehicle when communicating based on the WebRTC method.

[0135] Optionally, the device is used to process the historical data of the information to be analyzed and the vehicle communicating based on the WebRTC method using the root cause analysis method and the fault tree analysis method to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the abnormality cause, abnormality type, abnormality range and abnormality repair suggestion, and is specifically used to: determine whether there is an abnormality when the vehicle communicates based on the WebRTC method based on the communication indicator data in the information to be analyzed and preset abnormality rules; in response to the existence of an abnormality when the vehicle communicates based on the WebRTC method, use the fault tree analysis method to analyze the communication indicator data. The target data is analyzed to obtain a first abnormality type and a first abnormality cause of the abnormality; the environmental data in the information to be analyzed, the vehicle's own parameters in the information to be analyzed, and the historical data of the vehicle when communicating based on the WebRTC method are analyzed by a root cause analysis method to obtain a second abnormality type and a second abnormality cause of the abnormality; a target abnormality cause and an abnormality repair suggestion for the abnormality are determined based on the first abnormality cause and the second abnormality cause, and a target abnormality type and an abnormality range for the abnormality are determined based on the first abnormality type and the second abnormality type; an abnormality analysis report is generated based on the target abnormality cause, the target abnormality type, the abnormality range, and the abnormality repair suggestion.

[0136] Optionally, when the device is used, the parameters include first information and second information, and the parameters for adjusting the vehicle's communication based on the WebRTC based on the abnormality analysis report and the communication quality assessment result are specifically used to: adjust the following first information of the vehicle when communicating based on the WebRTC based on the abnormality analysis report, the first information including video resolution and / or audio resolution; adjust the following second information of the vehicle when communicating based on the WebRTC based on the communication quality assessment result, the second information including: bit rate, video resolution and encoding method parameters.

[0137] Optionally, the device is also used to: in response to the network signal strength in the communication indicator data being lower than a preset network signal strength, switch the network used by the vehicle when communicating based on the WebRTC method to a backup network.

[0138] It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, no further description is given here. Specifically, the device may perform the above method embodiment, and the above and other operations and / or functions of each module in the device are the corresponding processes in each method in the above method embodiment, respectively, and no further description is given here for the sake of brevity.

[0139] The above describes the device of the embodiment of the present application from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or a combination of hardware and software modules in the decoding processor to perform. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps in the above method embodiment in conjunction with its hardware.

[0140] Figure 3 is a schematic block diagram of an electronic device provided in an embodiment of the present application, and the electronic device may include:

[0141] The memory 301 and the processor 302, the memory 301 is used to store the computer program and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiment of the present application.

[0142] For example, the processor 302 may be configured to execute the above method embodiments according to instructions in the computer program.

[0143] In some embodiments of the present application, the processor 302 may include but is not limited to:

[0144] General-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.

[0145] In some embodiments of the present application, the memory 301 includes but is not limited to:

[0146] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0147] In some embodiments of the present application, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0148] like Figure 3 As shown, the electronic device may also include:

[0149] The transceiver 303 may be connected to the processor 302 or the memory 301 .

[0150] The processor 302 may control the transceiver 303 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include an antenna, and the number of antennas may be one or more.

[0151] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0152] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a computer, the computer can perform the method of the above method embodiment. In other words, the present application embodiment also provides a computer program product containing instructions, and when the instructions are executed by a computer, the computer can perform the method of the above method embodiment.

[0153] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a solid state drive (solid state disk, SSD)), etc.

[0154] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0155] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0156] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. For example, each functional module in each embodiment of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0157] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining communication parameters, characterized in that: For servers, including: Acquire information to be analyzed when the vehicle communicates based on WebRTC, the information to be analyzed including: communication indicator data reflecting communication quality, environmental data in the vehicle driving area, and vehicle parameters of the vehicle itself; Determine, based on the information to be analyzed and a communication quality evaluation model, a communication quality evaluation result when the vehicle communicates based on the WebRTC method; Processing the information to be analyzed and historical data when the vehicle communicates based on the WebRTC method using a root cause analysis method and a fault tree analysis method to obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes an abnormality cause, an abnormality type, an abnormality range, and an abnormality repair suggestion; Based on the abnormality analysis report and the communication quality assessment result, the parameters of the vehicle when communicating based on the WebRTC method are adjusted.

2. The method according to claim 1, characterized in that The method further comprises: Acquire initial sample data when the vehicle communicates based on the WebRTC method, the initial sample data including: initial sample communication indicator data for reflecting communication quality, initial sample environment data within the vehicle driving area, and initial vehicle sample parameters of the vehicle; Cleaning, denoising, and standardizing the initial sample data to obtain target sample data; The target sample data is used to train an initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

3. The method according to claim 2, characterized in that Before the initial large language model is trained using the target sample data to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, the method further includes: Classifying the target sample data according to a plurality of preset information types to obtain first sample data corresponding to each information type in the plurality of information types; The method of training the initial large language model using the target sample data to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method includes: Obtain multiple information types and numerical features corresponding to each information type; Based on the multiple information types, the numerical features corresponding to each information type, and the first sample data corresponding to each information type, the initial large language model is trained to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

4. The method according to claim 3, characterized in that The initial large language model is trained based on the multiple information types, the numerical features corresponding to the information types, and the first sample data corresponding to the information types to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method, including: For each information type in the multiple information types, for each numerical feature in at least one numerical feature corresponding to the information type, extract a numerical value corresponding to the numerical feature from first sample data corresponding to the information type, obtain at least one numerical value corresponding to the at least one numerical feature, and obtain at least one set of numerical feature information corresponding to the information type; Based on the multiple information types, at least one set of numerical feature information corresponding to each information type is used to train an initial large language model to obtain the communication quality evaluation model for evaluating the communication quality when the vehicle communicates based on the WebRTC method.

5. The method according to claim 1, characterized in that The root cause analysis method and the fault tree analysis method are used to process the historical data of the information to be analyzed and the vehicle when communicating based on the WebRTC method, and obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes the abnormality cause, abnormality type, abnormality range and abnormality repair suggestion, including: Determine whether there is an abnormality when the vehicle communicates based on the WebRTC method based on the communication indicator data in the information to be analyzed and a preset abnormality rule; In response to an abnormality when the vehicle communicates based on the WebRTC method, analyzing the communication indicator data using a fault tree analysis method to obtain a first abnormality type and a first abnormality cause of the abnormality; Analyze the environmental data in the information to be analyzed, the vehicle parameters in the information to be analyzed, and historical data when the vehicle communicates based on the WebRTC method by using a root cause analysis method to obtain a second abnormality type and a second abnormality cause of the abnormality; Determine a target abnormality cause and abnormality repair suggestion for the abnormality based on the first abnormality cause and the second abnormality cause, and determine a target abnormality type and abnormal range for the abnormality based on the first abnormality type and the second abnormality type; An exception analysis report is generated based on the target exception cause, the target exception type, the exception range, and the exception repair suggestion.

6. The method according to claim 1, characterized in that The parameters include first information and second information, and the adjusting the parameters when the vehicle communicates based on the WebRTC method based on the abnormal analysis report and the communication quality assessment result includes: Adjusting the following first information when the vehicle communicates based on the WebRTC method based on the abnormal analysis report, the first information including video resolution and / or audio resolution; Based on the communication quality evaluation result, the following second information when the vehicle communicates based on the WebRTC method is adjusted, and the second information includes: bit rate, video resolution and encoding method parameters.

7. The method according to claim 1, characterized in that The method further comprises: In response to the network signal strength in the communication indicator data being lower than a preset network signal strength, the network used by the vehicle when communicating based on the WebRTC method is switched to a backup network.

8. A communication parameter determination device, characterized in that: For servers, including: An acquisition unit is used to acquire information to be analyzed when the vehicle communicates based on WebRTC, wherein the information to be analyzed includes: communication indicator data reflecting communication quality, environmental data in the vehicle driving area, and vehicle parameters of the vehicle itself; A determination unit, configured to determine, based on the information to be analyzed and a communication quality assessment model, a communication quality assessment result when the vehicle communicates in the WebRTC manner; An analysis unit, configured to process the information to be analyzed and historical data when the vehicle communicates based on the WebRTC method by using a root cause analysis method and a fault tree analysis method, and obtain an abnormality analysis report when the vehicle communicates based on the WebRTC method, wherein the abnormality analysis report includes an abnormality cause, an abnormality type, an abnormality range, and an abnormality repair suggestion; An adjustment unit is used to adjust the parameters of the vehicle when communicating based on the WebRTC method based on the abnormal analysis report and the communication quality assessment result.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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