Mobile communication parameter adaptation method, system and device and readable storage medium
By using driving status and location information to predict and switch network slices, the method addresses inefficiencies in traditional vehicle communication, optimizing network performance in complex traffic environments.
Patent Information
- Application Number
- CN202411311056.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-19
- Publication Date
- 2024-12-13
AI Technical Summary
Traditional network communication methods in vehicles, such as V2X, suffer from low communication efficiency and fail to adapt to complex and changing traffic environments, necessitating improved network adaptive adjustment.
A method involving obtaining a vehicle's driving status and real-time location information, inputting this data into a prediction model to determine target network parameters, and switching to network slices that match these parameters to optimize communication.
Enables adaptive network communication that meets the specific communication needs of vehicles in varying driving states and locations, enhancing communication efficiency in dynamic traffic conditions.
Smart Images
Figure CN119136176A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of traffic technology, and in particular to a mobile communication parameter adaptation method, system, device and readable storage medium. Background Art
[0002] With the rapid development of Internet of Vehicles technology, the demand for communication between vehicles and between vehicles and infrastructure is growing. In addition, the continuous development and maturity of autonomous driving technology has also put higher and higher requirements on vehicle communication performance.
[0003] However, traditional network communication methods (V2X, vehicle to vehicle, vehicle-to-vehicle information exchange) have low communication efficiency and cannot meet the network communication needs in complex and changing traffic environments.
[0004] In summary, how to effectively solve problems such as network adaptive adjustment of vehicle-mounted communications is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention
[0005] The purpose of the present application is to provide a mobile communication parameter adaptation method, system, device and readable storage medium, which can realize network adaptive adjustment of vehicle-mounted communication.
[0006] In order to solve the above technical problems, this application provides the following technical solutions:
[0007] A mobile communication parameter adaptation method, applied to a vehicle system, comprising:
[0008] Obtain the vehicle's driving status information and real-time location information;
[0009] Inputting the driving state information and real-time location information into a preset prediction model to perform network parameter prediction to obtain target network parameters;
[0010] The target network parameters are used to perform network communication.
[0011] Preferably, the driving state information and the real-time location information are input into a preset prediction model to perform network parameter prediction to obtain target network parameters, including:
[0012] Extracting feature data from the driving state information and the real-time location information;
[0013] Based on the mapping relationship between the characteristics and the network parameters, determining the network parameters having a mapping relationship with the characteristic data;
[0014] The network parameter is determined as the target network parameter.
[0015] Preferably, training the prediction model comprises:
[0016] Acquire training samples; wherein the training samples include driving status information, real-time location information, network parameters and communication evaluation data;
[0017] Using the training samples to perform network parameter prediction training on the model, and combining the communication evaluation data to perform loss calculation;
[0018] After the training is completed, the prediction model is obtained.
[0019] Preferably, after performing network communication using the target network parameters, the method further comprises:
[0020] Obtaining real-time driving status information, real-time location information, real-time network parameters and real-time communication evaluation data under the current target network parameters;
[0021] In the case where the real-time communication evaluation data meets the optimization condition, generating an optimization sample using the real-time driving state information, the real-time location information, the real-time network parameters and the real-time communication evaluation data;
[0022] The prediction model is optimized and trained using the optimized samples.
[0023] Preferably, it also includes:
[0024] When the vehicle system is started, several contracted network slices are obtained.
[0025] Preferably, using the target network parameters to perform network communication includes:
[0026] Determine whether the current network slice matches the target network parameters;
[0027] If yes, continue to use the current network slice for network communication;
[0028] If not, network communication is performed using a target network slice that matches the target network parameters.
[0029] Preferably, using a target network slice matching the target network parameters to perform network communication includes:
[0030] Determine a target network slice matching the target network parameters from the network slices;
[0031] Switch the current network slice to the target network slice, and use the target network slice for communication.
[0032] A vehicle computer system, comprising:
[0033] An information acquisition module is used to obtain the driving status information and real-time location information of the vehicle;
[0034] A parameter prediction module, used for inputting the driving state information and the real-time location information into a preset prediction model to perform network parameter prediction and obtain target network parameters;
[0035] The mobile communication module is used to perform network communication using the target network parameters.
[0036] An electronic device, comprising:
[0037] Memory for storing computer programs;
[0038] The processor is used to implement the steps of the above-mentioned mobile communication parameter adaptation method when executing the computer program.
[0039] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the mobile communication parameter adaptation method are implemented.
[0040] The method provided in the embodiment of the present application is applied in the vehicle system to obtain the vehicle's driving status information and real-time location information; the driving status information and real-time location information are input into a preset prediction model to predict network parameters and obtain target network parameters; and the target network parameters are used to perform network communication.
[0041] The vehicle system has different communication requirements in different driving states and different geographical locations. In order to enable the network communication to be adaptively adjusted, in this application, the vehicle's driving state and real-time location information are first obtained, and then the driving state information and real-time location information are input into a preset prediction model for network parameter prediction to obtain the target network parameters. Then, communication is performed based on the target network parameters, so that the network communication of the vehicle system can match the communication requirements corresponding to the actual driving state and real-time location information.
[0042] Technical effect: This application can realize adaptive adjustment of the vehicle communication network based on the vehicle's driving status information and real-time location information to meet the network communication needs in complex and changeable traffic environments.
[0043] Correspondingly, the embodiments of the present application also provide a vehicle system, device and readable storage medium corresponding to the above-mentioned mobile communication parameter adaptation method, which have the above-mentioned technical effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0045] Figure 1 This is a flowchart of an implementation method of a mobile communication parameter adaptation method in an embodiment of the present application;
[0046] Figure 2 This is a specific implementation flow chart of a mobile communication parameter adaptation method in an embodiment of the present application;
[0047] Figure 3 This is a schematic diagram of a model training in an embodiment of the present application;
[0048] Figure 4 This is a structural schematic diagram of a vehicle system in an embodiment of the present application;
[0049] Figure 5 A schematic diagram of the structure of an electronic device in an embodiment of the present application
[0050] Figure 6 This is a schematic diagram of the specific structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION
[0051] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.
[0052] Please refer to Figure 1 , Figure 1 This is a flow chart of a mobile communication parameter adaptation method in an embodiment of the present application. The method is applied to a vehicle system. The method includes the following steps:
[0053] S101. Obtain driving status information and real-time location information of a vehicle.
[0054] In the embodiment of the present application, the vehicle's driving status information and real-time location information can be collected in real time through on-board sensors, GPS positioning, road condition monitoring and other equipment, and synchronized to the vehicle system.
[0055] The driving status information includes but is not limited to speed, acceleration, direction, braking status, etc. The real-time location information may be a real-time location, such as a certain road in a certain district of a certain city.
[0056] Generally speaking, people have different communication needs for the car machine when driving in different geographical locations. For example, when driving in the city, facing complex traffic conditions, the car machine needs to display traffic information in real time and provide route navigation; while when driving in the suburbs, the road conditions become relatively simple, and the car machine may be expected to output relaxing music. Obviously, real-time update and display of maps and navigation and music playback have completely different network communication requirements.
[0057] In addition, people have different communication requirements for the vehicle computer under different driving conditions. For example, when the vehicle computer is in a high-speed driving state, the vehicle computer needs to update the road condition map in real time, but when the vehicle is driving slowly or even stopped, there is no need to update the road condition map in real time.
[0058] In actual scenarios, the interweaving of driving status and different location situations will lead to various specific communication needs. Therefore, in this application, in order to clarify the actual user's communication needs, it is necessary to obtain driving status information and real-time location information.
[0059] S102: Input the driving status information and the real-time location information into a preset prediction model to perform network parameter prediction to obtain target network parameters.
[0060] After obtaining the driving status information and real-time location information, this information can be input into a preset prediction model for network parameter prediction, thereby obtaining target network parameters that are adapted to the communication requirements in the specific scenarios corresponding to the driving status information and real-time location information.
[0061] In this embodiment, the prediction model can be pre-trained based on the training samples so that it can predict the input driving status information and real-time location information to obtain the adapted target network parameters. The training samples may include driving status information, real-time location information, target network parameters and communication satisfaction. For example: the prediction model can predict the driving status information and real-time location information in the training samples to obtain the target network parameters, calculate the loss value based on the communication satisfaction corresponding to the target network parameters, and adjust the parameters of the prediction model based on the loss value.
[0062] In a specific implementation of the present application, the driving state information and the real-time location information are input into a preset prediction model to perform network parameter prediction to obtain target network parameters, including:
[0063] Step 1: Extract feature data from driving status information and real-time location information;
[0064] Step 2: Based on the mapping relationship between the characteristics and the network parameters, determine the network parameters that have a mapping relationship with the characteristic data;
[0065] Step 3: Determine the network parameters as target network parameters.
[0066] For ease of description, the above steps are combined for explanation below.
[0067] That is to say, in the prediction model, feature data is first extracted from the driving status information and the real-time location information. The feature data can be represented by a vector or a matrix, and each element in the vector or the matrix represents the value of a feature. Then, based on the pre-established mapping relationship between the feature and the network parameter, the network parameter with which the feature data has a mapping relationship is determined. The network parameter is the target network parameter.
[0068] In a specific implementation of the present application, training a prediction model includes:
[0069] Step 1: Obtain training samples; wherein the training samples include driving status information, real-time location information, network parameters and communication evaluation data;
[0070] Step 2: Use the training samples to train the model for network parameter prediction and calculate the loss based on the communication evaluation data;
[0071] Step 3: After the training is completed, the prediction model is obtained.
[0072] For ease of description, the above steps are combined for explanation below.
[0073] The vehicle's driving status information, real-time location information, and current user's communication evaluation data on the network (e.g., whether communication requirements are met, or the degree to which communication requirements are met is represented by a score) are collected in real time through on-board sensors, GPS positioning, road condition monitoring and other equipment, and synchronized to the vehicle system.
[0074] It can also obtain real-time traffic information (such as road congestion, traffic signal status, traffic accident information, etc.) and synchronize it to the vehicle system.
[0075] The above information is used as training samples and passed to the NSM (network slice manager) module. Using data analysis technology, the collected driving status information, road condition information and communication evaluation data are processed and analyzed in real time to establish a mapping relationship between feature data and network slice performance.
[0076] Among them, network slicing is an on-demand networking method that allows operators to separate multiple virtual end-to-end networks on a unified infrastructure. Each network slice is logically isolated from the path from the wireless access network to the bearer network and then to the core network to adapt to various types of applications. In a network slice, it can be divided into at least three parts: wireless network sub-slices, bearer network sub-slices, and core network sub-slices.
[0077] The collected vehicle driving status and road condition information is screened and quantified to extract key parameters that affect the network slice configuration, such as vehicle speed, vehicle location, road congestion and other key information.
[0078] Select a suitable machine learning algorithm as the basis, extract feature vectors from training samples according to the requirements of network slice configuration, and use them for model training.
[0079] The model is trained using training set data made with feature vectors, so that it can learn the relationship between vehicle driving status and road condition information and network slicing performance.
[0080] In other words, after the prediction model is trained, the vehicle's driving trajectory and communication needs in the future can be predicted based on the mapping relationship. At the same time, combined with the vehicle's real-time location and destination information, the required network slice configuration, including bandwidth, latency, throughput and other parameters, can be predicted.
[0081] S103: Use the target network parameters to perform network communication.
[0082] After the target network parameters are predicted, network communication can be performed based on the target network parameters. Since the target network parameters are automatically optimized based on the vehicle's driving status information and real-time location information, network communication based on the target network parameters can enable the vehicle computer to provide users with communication services that are adapted to the current driving status and location, thereby meeting the user's communication needs.
[0083] In a specific implementation of the present application, using target network parameters to perform network communication includes:
[0084] Step 1: Determine whether the current network slice matches the target network parameters;
[0085] Step 2: If yes, continue to use the current network slice for network communication;
[0086] Step 3: If not, use the target network slice that matches the target network parameters to conduct network communication.
[0087] For ease of description, the above three steps are combined for explanation below.
[0088] Please refer to Figure 2 Different network slices have different service capabilities or service features. For example, some network slices have low latency, and some network slices have large bandwidth. Therefore, after determining the target network parameters, first determine whether the current network slice matches the target network parameters. For example, if the target network parameters correspond to low latency requirements, and the current network slice corresponds to large bandwidth, the two do not match; if the target network parameters correspond to low latency requirements, and the current network slice corresponds to low latency, the two match.
[0089] If the target network parameters match the current network slice, there is no need to change the network slice. Network communication can continue based on the current network slice. You only need to adjust the parameters of the current network slice.
[0090] If the target network parameters do not match the current network slice, it is necessary to switch the current network slice to a target network slice that matches the target network parameters, and use the switched target network slice for network communication.
[0091] In a specific implementation of the present application, network communication is performed using a target network slice that matches a target network parameter, including:
[0092] Step 1: Determine a target network slice that matches the target network parameters from the network slices;
[0093] Step 2: Switch the current network slice to the target network slice and use the target network slice for communication.
[0094] For ease of description, the above steps are combined for explanation below.
[0095] In this application, a target network slice that matches the target network parameters can be determined from the network slice. Specifically, the best matching target network slice can be found by comparing the bandwidth, delay, throughput and other parameters of the network slice with the target network parameters.
[0096] Specifically, in order to determine the best matching target network slice, several signed network slices can be obtained when the vehicle system is started. Specifically, when the vehicle system is started, the network passes the signed network slice to the Modem through AllowedNSSAI. If the slice configuration is updated, the update policy can be triggered on the network side.
[0097] After the target network slice is determined, the current network slice can be switched to the target network slice, so that communication can be carried out based on the target network slice.
[0098] That is to say, based on the prediction results of the model, the vehicle system can adaptively adjust the selection of network slices and select S-NSSAI that is more suitable for the application scenario to establish a PDU connection, ensuring that the vehicle can obtain the best network performance under different driving states and road conditions.
[0099] In a specific implementation of the present application, after the target network parameters are used for network communication, the prediction model is continuously optimized, and the process includes:
[0100] Step 1: Obtain real-time driving status information, real-time location information, real-time network parameters and real-time communication evaluation data under current target network parameters;
[0101] Step 2: When the real-time communication evaluation data meets the optimization conditions, the real-time driving status information, the real-time location information, the real-time network parameters and the real-time communication evaluation data are used to generate an optimization sample;
[0102] Step 3: Use optimized samples to optimize the prediction model.
[0103] For ease of description, the above steps are combined for explanation below.
[0104] That is, please refer to Figure 3 In this embodiment, after the target network parameters are determined and network communication is performed based on the target network parameters, the performance of the network slice can be continuously monitored, feedback data can be collected, and the mapping relationship model can be iteratively optimized to improve the accuracy of prediction and optimization. The continuous optimization iterative process is similar to the initial training of the prediction model, which can be referred to the previous description and will not be repeated here.
[0105] Apply the method provided in the embodiment of the present application to obtain the vehicle's driving status information and real-time location information; input the driving status information and real-time location information into a preset prediction model to predict network parameters and obtain target network parameters; and use the target network parameters to perform network communication.
[0106] The vehicle system has different communication requirements in different driving states and different geographical locations. In order to enable the network communication to be adaptively adjusted, in this application, the vehicle's driving state and real-time location information are first obtained, and then the driving state information and real-time location information are input into a preset prediction model for network parameter prediction to obtain the target network parameters. Then, communication is performed based on the target network parameters, so that the network communication of the vehicle system can match the communication requirements corresponding to the actual driving state and real-time location information.
[0107] Technical effect: This application can realize adaptive adjustment of the vehicle communication network based on the vehicle's driving status information and real-time location information to meet the network communication needs in complex and changeable traffic environments.
[0108] Corresponding to the above method embodiment, the embodiment of the present application further provides a vehicle system. The vehicle system described below and the mobile communication parameter adaptation method described above can refer to each other.
[0109] See also Figure 4 As shown, the system includes the following modules:
[0110] The information acquisition module 101 is used to acquire the driving status information and real-time location information of the vehicle;
[0111] The parameter prediction module 102 is used to input the driving state information and the real-time location information into a preset prediction model to perform network parameter prediction and obtain target network parameters;
[0112] The mobile communication module 103 is used to perform network communication using the target network parameters.
[0113] The vehicle-mounted system provided in the embodiment of the present application is applied to obtain the vehicle's driving status information and real-time location information; the driving status information and real-time location information are input into a preset prediction model to predict network parameters and obtain target network parameters; and network communication is performed using the target network parameters.
[0114] The vehicle system has different communication requirements in different driving states and different geographical locations. In order to enable the network communication to be adaptively adjusted, in this application, the vehicle's driving state and real-time location information are first obtained, and then the driving state information and real-time location information are input into a preset prediction model for network parameter prediction to obtain the target network parameters. Then, communication is performed based on the target network parameters, so that the network communication of the vehicle system can match the communication requirements corresponding to the actual driving state and real-time location information.
[0115] Technical effect: This application can realize adaptive adjustment of the vehicle communication network based on the vehicle's driving status information and real-time location information to meet the network communication needs in complex and changeable traffic environments.
[0116] In a specific embodiment of the present application, the parameter prediction module is specifically used to extract feature data from driving status information and real-time location information; based on the mapping relationship between the feature and the network parameter, determine the network parameter having a mapping relationship with the feature data; and determine the network parameter as the target network parameter.
[0117] In a specific implementation of the present application, the model training module is used to obtain training samples; wherein the training samples include driving status information, real-time location information, network parameters and communication evaluation data;
[0118] Use training samples to train the model's network parameters and calculate losses based on communication evaluation data;
[0119] After the training is completed, the prediction model is obtained.
[0120] In a specific implementation of the present application, it also includes:
[0121] An optimization training module is used to obtain real-time driving status information, real-time location information, real-time network parameters and real-time communication evaluation data under the current target network parameters after network communication using the target network parameters;
[0122] When the real-time communication evaluation data meets the optimization conditions, the optimization samples are generated by using the real-time driving status information, the real-time location information, the real-time network parameters and the real-time communication evaluation data;
[0123] Use optimized samples to optimize the prediction model.
[0124] In a specific implementation of the present application, it also includes:
[0125] The network slice acquisition module is used to obtain several contracted network slices when the vehicle system is started.
[0126] In a specific implementation of the present application, the mobile communication module is specifically used to determine whether the current network slice matches the target network parameters;
[0127] If yes, continue to use the current network slice for network communication;
[0128] If not, network communication is performed using the target network slice that matches the target network parameters.
[0129] In a specific implementation of the present application, the mobile communication module is specifically used to determine a target network slice matching the target network parameters from the network slices;
[0130] Switch the current network slice to the target network slice and use the target network slice for communication.
[0131] Corresponding to the above method embodiment, an embodiment of the present application further provides an electronic device. The electronic device described below and the mobile communication parameter adaptation method described above can refer to each other.
[0132] See also Figure 5As shown, the electronic device includes:
[0133] A memory 332, for storing computer programs;
[0134] The processor 322 is configured to implement the steps of the mobile communication parameter adaptation method of the above method embodiment when executing a computer program.
[0135] For details, please refer to Figure 6 , Figure 6 A schematic diagram of the specific structure of an electronic device provided for this embodiment, which may have relatively large differences due to different configurations or performances, may include one or more processors (central processing units, CPU) (for example, one or more processors) and a memory 332, and the memory 332 stores one or more computer programs 342 or data 344. Among them, the memory 332 can be a temporary storage or a permanent storage. The program stored in the memory 332 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the data processing device. Furthermore, the processor 322 can be configured to communicate with the memory 332 to execute a series of instruction operations in the memory 332 on the electronic device 301.
[0136] The electronic device 301 may further include one or more power supplies 326 , one or more wired or wireless network interfaces 350 , one or more input and output interfaces 358 , and / or one or more operating systems 341 .
[0137] The steps in the mobile communication parameter adaptation method described above can be implemented by the structure of an electronic device.
[0138] Corresponding to the above method embodiment, the embodiment of the present application further provides a readable storage medium. The readable storage medium described below and the mobile communication parameter adaptation method described above can refer to each other.
[0139] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the mobile communication parameter adaptation method of the above method embodiment are implemented.
[0140] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.
[0141] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0142] Those skilled in the art may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may 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.
[0143] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0144] Finally, it should be noted that, in this article, relationships such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms include, include or any other variations are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0145] Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of the present application. At the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
Claims
1. A mobile communication parameter adaptation method, characterized in that: Applied to vehicle systems, including: Obtain the vehicle's driving status information and real-time location information; Inputting the driving state information and real-time location information into a preset prediction model to perform network parameter prediction to obtain target network parameters; The target network parameters are used to perform network communication.
2. The method according to claim 1, characterized in that: The driving state information and real-time location information are input into a preset prediction model to predict network parameters and obtain target network parameters, including: Extracting feature data from the driving state information and the real-time location information; Based on the mapping relationship between the characteristics and the network parameters, determining the network parameters having a mapping relationship with the characteristic data; The network parameter is determined as the target network parameter.
3. The method according to claim 1, characterized in that Training the prediction model comprises: Acquire training samples; wherein the training samples include driving status information, real-time location information, network parameters and communication evaluation data; Using the training samples to perform network parameter prediction training on the model, and combining the communication evaluation data to perform loss calculation; After the training is completed, the prediction model is obtained.
4. The method according to claim 1, characterized in that: After the target network parameters are used to perform network communication, the method further comprises: Obtaining real-time driving status information, real-time location information, real-time network parameters and real-time communication evaluation data under the current target network parameters; In the case where the real-time communication evaluation data meets the optimization condition, generating an optimization sample using the real-time driving state information, the real-time location information, the real-time network parameters and the real-time communication evaluation data; The prediction model is optimized and trained using the optimized samples.
5. The method according to claim 1, characterized in that Also includes: When the vehicle system is started, several contracted network slices are obtained.
6. The method according to any one of claims 1 to 5, characterized in that: Using the target network parameters to perform network communication includes: Determine whether the current network slice matches the target network parameters; If yes, continue to use the current network slice for network communication; If not, network communication is performed using a target network slice that matches the target network parameters.
7. The method according to claim 6, characterized in that Using a target network slice that matches the target network parameters to perform network communication includes: Determine a target network slice matching the target network parameters from the network slices; Switch the current network slice to the target network slice, and use the target network slice for communication.
8. A vehicle computer system, characterized in that: include: An information acquisition module is used to obtain the driving status information and real-time location information of the vehicle; A parameter prediction module, used for inputting the driving state information and the real-time location information into a preset prediction model to perform network parameter prediction and obtain target network parameters; The mobile communication module is used to perform network communication using the target network parameters.
9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the mobile communication parameter adaptation method according to any one of claims 1 to 7 when executing the computer program.
10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the mobile communication parameter adaptation method according to any one of claims 1 to 7 are implemented.
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