Vehicle communication network switching methods, devices, equipment, storage media and products

Abstract

This invention discloses a method, apparatus, device, storage medium, and product for switching vehicle communication networks. The method includes: acquiring first network state data of the vehicle's current communication network and second network state data of a backup communication network for a historical time period based on the current vehicle travel time; inputting the first network state data into a pre-trained network state prediction model to obtain predicted network state data for future time periods output by the model; determining whether to switch the vehicle's current communication network based on the predicted network state data; if so, determining the target communication network based on the second network state data of the backup communication network and the network state prediction model; and switching the vehicle's current communication network to the target communication network. This invention reduces the latency of vehicle communication network switching and avoids communication interruptions.

Description

Technical Field

[0001] This invention relates to the field of automotive communication technology, and in particular to a method, apparatus, device, storage medium, and product for switching vehicle communication networks. Background Technology

[0002] Currently, in-vehicle communication systems often rely on a single network connection for communication and interaction with other vehicles, transportation infrastructure, and cloud platforms. While this communication method is effective under certain conditions, it can lead to instability in critical applications such as autonomous driving, vehicle-to-everything (V2X) communication, and remote services because vehicles frequently encounter issues like weak network signals, network congestion, or switching delays while driving.

[0003] Existing vehicle communication systems can support switching between different networks, but most rely on simple rules. This method introduces latency during switching, affecting communication continuity. When switching between multiple networks, communication interruptions or high latency often occur, especially noticeable at high speeds. Summary of the Invention

[0004] This invention provides a method, apparatus, device, storage medium, and product for switching vehicle communication networks, in order to reduce the switching delay of in-vehicle communication networks and avoid communication interruptions.

[0005] According to one aspect of the present invention, a method for switching vehicle communication networks is provided, the method comprising:

[0006] Based on the current vehicle driving time, obtain the first network status data of the current communication network and the second network status data of the backup communication network for the vehicle in the historical time period.

[0007] The first network state data is input into the pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model.

[0008] Based on the predicted network status data, determine whether to switch the current communication network of the vehicle.

[0009] If so, the target communication network is determined based on the second network status data of the backup communication network and the network status prediction model.

[0010] Switch the vehicle's current communication network to the target communication network.

[0011] According to another aspect of the present invention, a vehicle communication network switching device is provided, the device comprising:

[0012] The status data acquisition module is used to acquire the first network status data of the current communication network and the second network status data of the backup communication network of the vehicle under the historical time period, based on the current vehicle driving time.

[0013] The network state prediction module is used to input the first network state data into the pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model.

[0014] The network switching judgment module is used to determine whether to switch the current communication network of its own vehicle based on the predicted network status data.

[0015] The target network determination module is used to determine the target communication network based on the second network status data of the backup communication network and the network status prediction model if the current communication network of its own vehicle is to be switched.

[0016] The network switching module is used to switch the vehicle's current communication network to the target communication network.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle communication network switching method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle communication network switching method according to any embodiment of the present invention.

[0022] The technical solution of this invention obtains first network state data of the vehicle's current communication network and second network state data of the backup communication network for a historical time period based on the current vehicle driving time. The first network state data is input into a pre-trained network state prediction model to obtain predicted network state data for future time periods output by the model. Based on the predicted network state data, it is determined whether to switch the vehicle's current communication network. If so, based on the second network state data of the backup communication network and the network state prediction model, a target communication network is determined. The vehicle's current communication network is then switched to the target communication network. This technical solution enables the prediction of the communication network state for future time periods during vehicle operation, allowing the vehicle to switch communication networks in advance, achieving seamless network switching. This reduces the energy consumption of the in-vehicle communication system, improves the safety and stability of autonomous driving, reduces the latency of in-vehicle communication network switching, and avoids communication interruptions.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a vehicle communication network switching method according to Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of a vehicle communication network switching method according to Embodiment 2 of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of a vehicle communication network switching device according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the vehicle communication network switching method of this invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a vehicle communication network switching method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where communication networks are seamlessly switched during vehicle operation. The method can be executed by a vehicle communication network switching device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110. Based on the current vehicle travel time, obtain the first network status data of the current communication network and the second network status data of the backup communication network for the vehicle under the historical time period.

[0034] The historical time period can be a historical time within a certain past time range, with the current vehicle's travel time as the time base. For example, if the current vehicle's travel time is 2024 / 10 / 18 / 09:00, then the historical time period can be 2024 / 10 / 18 / 00:00~2024 / 10 / 18 / 08:59.

[0035] The current communication network can be the communication network currently used by the vehicle's in-vehicle communication system; the backup communication network can be other communication networks supported by the vehicle's in-vehicle communication system besides the current communication network. The communication networks supported by the in-vehicle communication system can include wireless communication networks, mobile communication networks, and satellite communication networks, etc. For example, if the vehicle's current communication network during the current driving time period is a wireless communication network, then the backup communication network can include mobile communication networks and satellite communication networks, etc.

[0036] Network status data can include network signal strength, network latency, and network bandwidth. The first network status data is the vehicle's own network status data under the current communication network; the second network status data is the network status data of the vehicle's own backup communication network.

[0037] S120. Input the first network state data into the pre-trained network state prediction model to obtain the predicted network state data for the future time period output by the model.

[0038] The network state prediction model can be a model pre-trained by relevant technical personnel for predicting network state over future time periods. Specifically, it can be used for predicting network latency, network signal strength, and network bandwidth.

[0039] The network state prediction model can include a signal strength prediction model, a network latency prediction model, and a network bandwidth prediction model. The signal strength prediction model is used to predict network signal strength; the network latency prediction model is used to predict network latency; and the network bandwidth prediction model is used to predict network bandwidth.

[0040] In one optional embodiment, the first network state data includes network signal strength, network latency, and network bandwidth; the network state prediction model includes a signal strength prediction model, a network latency prediction model, and a network bandwidth prediction model.

[0041] Among them, the signal strength prediction model, network latency prediction model, and network bandwidth prediction model can be pre-trained by relevant technical personnel. Taking the signal strength prediction model as an example, the training method for the signal strength prediction model can be as follows:

[0042] Historical signal strength data for different time periods is acquired; this data is time-series data. The historical signal strength data is then input into a pre-built network model, such as an LSTM (Long Short-Term Memory) model, to obtain the model's prediction output. Based on the model's predictions and the corresponding actual results from the historical signal strength data, the network model is trained until a preset threshold of iterations is reached, or until the loss value no longer changes.

[0043] The training methods for the network latency prediction model and the network bandwidth prediction model are the same as those for the signal strength prediction model, using historical network latency data and historical bandwidth data for model training, respectively. This embodiment will not elaborate further on this.

[0044] Accordingly, the first network state data is input into the pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model, including:

[0045] Step a1: Input the network signal strength into the signal strength prediction model to obtain the predicted signal strength data for the future time period output by the model.

[0046] Step a2: Input the network latency into the network latency prediction model to obtain the predicted network latency data for the future time period output by the model.

[0047] Step a3: Input the network bandwidth into the network bandwidth prediction model to obtain the predicted network bandwidth data for the future time period output by the model.

[0048] Step a4: Generate predicted network state data, including predicted signal strength data, predicted network latency data, and predicted network bandwidth data.

[0049] S130. Based on the predicted network status data, determine whether to switch the current communication network of the vehicle itself.

[0050] In one optional embodiment, determining whether to switch the current communication network of the vehicle based on the predicted network status data includes: switching the current communication network of the vehicle if the predicted signal strength data is less than a preset signal strength threshold; and / or switching the current communication network of the vehicle if the predicted network latency data is greater than a preset network latency threshold; and / or switching the current communication network of the vehicle if the predicted network bandwidth data is less than a preset network bandwidth threshold.

[0051] Among them, the signal strength threshold, network latency threshold, and network bandwidth threshold can be preset by relevant technical personnel according to actual needs.

[0052] Optionally, if the predicted signal strength data is not less than the preset signal strength threshold, the predicted network delay data is not greater than the preset network delay threshold, and the predicted network bandwidth data is not less than the preset network bandwidth threshold, then the current communication network of the vehicle itself will not be switched.

[0053] It should be noted that, in order to further improve the accuracy of determining whether the vehicle's current communication network needs to be switched, the vehicle's speed and location can also be considered.

[0054] In one optional embodiment, vehicle driving data for the vehicle under historical time periods is obtained based on the current vehicle driving time; the vehicle driving data includes vehicle driving speed and vehicle geographical location; the vehicle driving speed and vehicle geographical location are input into a pre-trained network stability prediction model to obtain predicted network stability data for future time periods output by the model; correspondingly, the step of determining whether to switch the current communication network of the vehicle based on the predicted network state data includes: determining whether to switch the current communication network of the vehicle based on the predicted network state data and the predicted network stability data.

[0055] The network stability prediction model can be pre-trained by relevant technical personnel. Specifically, the training method for the network stability prediction model can be as follows:

[0056] We collect vehicle speeds and geographical locations over historical time periods, along with network stability index parameters for each corresponding speed and location. We then construct a training set of samples in time series format. A higher network stability value indicates a more stable network state, while a lower value indicates a less stable network state. For example, the training set can be represented as shown in Table 1.

[0057] Table 1

[0058] Timestamp longitude latitude speed Network stability 2024-10-01 08:00 116.404 39.915 50km / h 50 2024-10-01 08:05 116.405 39.916 55km / h 51 …… …… …… …… ……

[0059] The sample training set is input into a pre-built LSTM network model for model training until the model training completion condition is met, thus obtaining the network stability prediction model.

[0060] S140. If so, then the target communication network is determined based on the second network status data of the backup communication network and the network status prediction model.

[0061] If it is determined that the current communication network of the vehicle needs to be switched, the target communication network is determined based on the second network status data of the backup communication network and the network status prediction model. The number of backup communication networks can be at least one.

[0062] In one optional embodiment, determining the target communication network based on the second network state data of the backup communication network and a network state prediction model includes: inputting the second network state data of the backup communication network into the network state prediction model to obtain the predicted network state data of the backup communication network in the future time period output by the model; and determining the target communication network based on the predicted network state data of the backup communication network in the future time period.

[0063] Specifically, the second network state of the backup communication network includes network signal strength, network latency, and network bandwidth. The network signal strength of the backup communication network is input into a signal strength prediction model to obtain predicted signal strength data for future time periods; the network latency of the backup communication network is input into a network latency prediction model to obtain predicted network latency data for future time periods; and the network bandwidth of the backup communication network is input into a network bandwidth prediction model to obtain predicted network bandwidth data for future time periods.

[0064] The target communication network is determined based on the predicted signal strength data, predicted network delay data, and predicted network bandwidth data of the backup communication network. Specifically, a backup communication network with high predicted signal strength, low delay, and large bandwidth can be selected as the target communication network.

[0065] S150: Switch the vehicle's current communication network to the target communication network.

[0066] The technical solution of this invention obtains first network state data of the vehicle's current communication network and second network state data of the backup communication network for a historical time period based on the current vehicle driving time. The first network state data is input into a pre-trained network state prediction model to obtain predicted network state data for future time periods output by the model. Based on the predicted network state data, it is determined whether to switch the vehicle's current communication network. If so, based on the second network state data of the backup communication network and the network state prediction model, a target communication network is determined. The vehicle's current communication network is then switched to the target communication network. This technical solution enables the prediction of the communication network state for future time periods during vehicle operation, allowing the vehicle to switch communication networks in advance, achieving seamless network switching. This reduces the energy consumption of the in-vehicle communication system, improves the safety and stability of autonomous driving, reduces the latency of in-vehicle communication network switching, and avoids communication interruptions.

[0067] Example 2

[0068] Figure 2 This is a flowchart illustrating a vehicle communication network switching method according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides a preferred example.

[0069] like Figure 2 As shown, the method includes the following specific steps:

[0070] S21. Based on the current vehicle travel time, obtain the network signal strength, network latency, and network bandwidth of the vehicle's current communication network during historical time periods.

[0071] S22. Input the network signal strength into the signal strength prediction model to obtain the predicted signal strength for the future time period output by the model.

[0072] S23. Determine whether the predicted signal strength is greater than the preset signal strength threshold. If yes, proceed to S24; otherwise, proceed to S29.

[0073] S24. Input the network latency into the network latency prediction model to obtain the predicted network latency for future time periods output by the model.

[0074] S25. Determine whether the predicted network latency is less than the preset network latency threshold. If yes, proceed to S26; otherwise, proceed to S29.

[0075] S26. Input the network bandwidth into the network bandwidth prediction model to obtain the predicted network bandwidth for the future time period output by the model.

[0076] S27. Determine whether the predicted network bandwidth is greater than the preset network bandwidth threshold. If yes, proceed to S28; otherwise, proceed to S29.

[0077] S28. Do not switch networks, and allow the above model update to enter the next detection cycle;

[0078] S29. Obtain the network signal strength, network delay, and network bandwidth of the backup communication network, and determine the target communication network based on the network signal strength, network delay, and network bandwidth of the backup communication network, and switch the current communication network to the target communication network.

[0079] Example 3

[0080] Figure 3This is a schematic diagram of a vehicle communication network switching device provided in Embodiment 3 of the present invention. The vehicle communication network switching device provided in this embodiment of the present invention is applicable to situations requiring seamless switching of communication networks during vehicle operation. This vehicle communication network switching device can be implemented in hardware and / or software, such as... Figure 3 As shown, the device specifically includes: a status data acquisition module 301, a network status prediction module 302, a network switching judgment module 303, a target network determination module 304, and a network switching module 305. Among them,

[0081] The status data acquisition module 301 is used to acquire, based on the current vehicle driving time, the first network status data of the current communication network of the vehicle and the second network status data of the backup communication network under the historical time period.

[0082] Network state prediction module 302 is used to input the first network state data into a pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model.

[0083] The network switching judgment module 303 is used to determine whether to switch the current communication network of its own vehicle based on the predicted network status data.

[0084] The target network determination module 304 is used to determine the target communication network based on the second network status data of the backup communication network and the network status prediction model if the current communication network of its own vehicle is to be switched.

[0085] The network switching module 305 is used to switch the current communication network of the vehicle to the target communication network.

[0086] The technical solution of this invention obtains first network state data of the vehicle's current communication network and second network state data of the backup communication network for a historical time period based on the current vehicle driving time. The first network state data is input into a pre-trained network state prediction model to obtain predicted network state data for future time periods output by the model. Based on the predicted network state data, it is determined whether to switch the vehicle's current communication network. If so, based on the second network state data of the backup communication network and the network state prediction model, a target communication network is determined. The vehicle's current communication network is then switched to the target communication network. This technical solution enables the prediction of the communication network state for future time periods during vehicle operation, allowing the vehicle to switch communication networks in advance, achieving seamless network switching. This reduces the energy consumption of the in-vehicle communication system, improves the safety and stability of autonomous driving, reduces the latency of in-vehicle communication network switching, and avoids communication interruptions.

[0087] Optionally, the first network state data includes network signal strength, network latency, and network bandwidth; the network state prediction model includes a signal strength prediction model, a network latency prediction model, and a network bandwidth prediction model.

[0088] Correspondingly, the network state prediction module 302 is specifically used for:

[0089] The network signal strength is input into the signal strength prediction model to obtain the predicted signal strength data for future time periods output by the model; and...

[0090] The network latency is input into the network latency prediction model to obtain the predicted network latency data for future time periods output by the model; and...

[0091] The network bandwidth is input into the network bandwidth prediction model to obtain the predicted network bandwidth data for the future time period output by the model.

[0092] Generate predicted network state data, which includes the predicted signal strength data, the predicted network delay data, and the predicted network bandwidth data.

[0093] Optionally, the network switching judgment module 303 includes:

[0094] The first network switching unit is configured to switch the current communication network of its own vehicle if the predicted signal strength data is less than a preset signal strength threshold; and / or,

[0095] The second network switching unit is configured to switch the current communication network of its own vehicle if the predicted network latency data is greater than a preset network latency threshold; and / or,

[0096] The third network switching unit is used to switch the current communication network of its own vehicle if the predicted network bandwidth data is less than a preset network bandwidth threshold.

[0097] Optionally, the network switching judgment module 303 includes:

[0098] The fourth network switching unit is configured to not switch the current communication network of its own vehicle if the predicted signal strength data is not less than a preset signal strength threshold, the predicted network delay data is not greater than a preset network delay threshold, and the predicted network bandwidth data is not less than a preset network bandwidth threshold.

[0099] Optionally, the device further includes:

[0100] The driving data acquisition module is used to acquire the vehicle driving data of its own vehicle in historical time periods based on the current vehicle driving time; the vehicle driving data includes vehicle driving speed and vehicle geographical location.

[0101] The stability prediction module is used to input the vehicle's driving speed and geographical location into a pre-trained network stability prediction model to obtain the predicted network stability data for future time periods output by the model.

[0102] Accordingly, the network switching determination module 303 includes:

[0103] The fifth network switching unit is used to determine whether to switch the current communication network of its own vehicle based on the predicted network status data and the predicted network stability data.

[0104] Optionally, the target network determination module 304 is specifically used for:

[0105] The second network state data of the backup communication network is input into the network state prediction model to obtain the predicted network state data of the backup communication network in future time periods output by the model.

[0106] The target communication network is determined based on the predicted network status data of the backup communication network over future time periods.

[0107] The vehicle communication network switching device provided in this embodiment of the invention can execute the vehicle communication network switching method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0108] Example 4

[0109] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0110] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0111] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0112] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as vehicle communication network switching methods.

[0113] In some embodiments, the vehicle communication network switching method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the vehicle communication network switching method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the vehicle communication network switching method by any other suitable means (e.g., by means of firmware).

[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0120] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for switching vehicle communication networks, characterized in that, include: Based on the current vehicle driving time, obtain the first network status data of the current communication network and the second network status data of the backup communication network for the vehicle in the historical time period. The first network state data is input into the pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model. Based on the predicted network status data, determine whether to switch the current communication network of the vehicle. If so, the target communication network is determined based on the second network status data of the backup communication network and the network status prediction model. Switch the vehicle's current communication network to the target communication network; The first network status data includes network signal strength, network latency, and network bandwidth; the network status prediction model includes a signal strength prediction model, a network latency prediction model, and a network bandwidth prediction model. Accordingly, the step of inputting the first network state data into a pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model includes: The network signal strength is input into the signal strength prediction model to obtain the predicted signal strength data for future time periods output by the model; and... The network latency is input into the network latency prediction model to obtain the predicted network latency data for future time periods output by the model; and... The network bandwidth is input into the network bandwidth prediction model to obtain the predicted network bandwidth data for the future time period output by the model. Generate predicted network state data including the predicted signal strength data, the predicted network delay data, and the predicted network bandwidth data; The signal strength prediction model, network latency prediction model, and network bandwidth prediction model are pre-trained. The training methods for the network latency prediction model and the network bandwidth prediction model are the same as those for the signal strength prediction model, using historical network latency data and historical bandwidth data respectively. The training method for the signal strength prediction model is as follows: Historical signal strength data for a given time period is obtained, wherein the historical signal strength data is time series data; the historical signal strength data of the time series is input into a pre-built network model to obtain the prediction result output by the model; the network model is trained according to the prediction result output by the model and the actual result corresponding to the historical signal strength data until a preset iteration number threshold is reached, or until the loss value no longer changes; The step of determining whether to switch the vehicle's current communication network based on the predicted network status data includes: If the predicted signal strength data is less than a preset signal strength threshold, then the vehicle's current communication network is switched; and / or, If the predicted network latency data is greater than a preset network latency threshold, then the vehicle's current communication network is switched; and / or, If the predicted network bandwidth data is less than the preset network bandwidth threshold, then the current communication network of the vehicle will be switched. The method further includes: obtaining vehicle driving data of the vehicle itself under historical time periods based on the current vehicle driving time; the vehicle driving data includes vehicle driving speed and vehicle geographical location; The vehicle's speed and geographical location are input into a pre-trained network stability prediction model to obtain the predicted network stability data for future time periods output by the model. Accordingly, determining whether to switch the vehicle's current communication network based on the predicted network status data includes: Based on the predicted network status data and the predicted network stability data, determine whether to switch the current communication network of the vehicle.

2. The method according to claim 1, characterized in that, The step of determining whether to switch the vehicle's current communication network based on the predicted network status data includes: If the predicted signal strength data is not less than a preset signal strength threshold, the predicted network delay data is not greater than a preset network delay threshold, and the predicted network bandwidth data is not less than a preset network bandwidth threshold, then the vehicle will not switch to its current communication network.

3. The method according to claim 1, characterized in that, The step of determining the target communication network based on the second network state data of the backup communication network and the network state prediction model includes: The second network state data of the backup communication network is input into the network state prediction model to obtain the predicted network state data of the backup communication network in future time periods output by the model. The target communication network is determined based on the predicted network status data of the backup communication network over future time periods.

4. A vehicle communication network switching device, characterized in that, include: The status data acquisition module is used to acquire the first network status data of the current communication network and the second network status data of the backup communication network of the vehicle under the historical time period, based on the current vehicle driving time. The network state prediction module is used to input the first network state data into the pre-trained network state prediction model to obtain the predicted network state data for future time periods output by the model. The network switching judgment module is used to determine whether to switch the current communication network of its own vehicle based on the predicted network status data. The target network determination module is used to determine the target communication network based on the second network status data of the backup communication network and the network status prediction model if the current communication network of its own vehicle is to be switched. A network switching module is used to switch the vehicle's current communication network to the target communication network; The first network status data includes network signal strength, network latency, and network bandwidth; the network status prediction model includes a signal strength prediction model, a network latency prediction model, and a network bandwidth prediction model. Accordingly, the network state prediction module is specifically used for: The network signal strength is input into the signal strength prediction model to obtain the predicted signal strength data for future time periods output by the model; and... The network latency is input into the network latency prediction model to obtain the predicted network latency data for future time periods output by the model. as well as, The network bandwidth is input into the network bandwidth prediction model to obtain the predicted network bandwidth data for the future time period output by the model. Generate predicted network state data including the predicted signal strength data, the predicted network delay data, and the predicted network bandwidth data; The signal strength prediction model, network latency prediction model, and network bandwidth prediction model are pre-trained. The training methods for the network latency prediction model and the network bandwidth prediction model are the same as those for the signal strength prediction model, using historical network latency data and historical bandwidth data respectively. The training method for the signal strength prediction model is as follows: Historical signal strength data for a given time period is obtained, wherein the historical signal strength data is time series data; the historical signal strength data of the time series is input into a pre-built network model to obtain the prediction result output by the model; the network model is trained according to the prediction result output by the model and the actual result corresponding to the historical signal strength data until a preset iteration number threshold is reached, or until the loss value no longer changes; The network switching determination module includes: The first network switching unit is configured to switch the current communication network of its own vehicle if the predicted signal strength data is less than a preset signal strength threshold; and / or, The second network switching unit is configured to switch the current communication network of its own vehicle if the predicted network latency data is greater than a preset network latency threshold; and / or, The third network switching unit is used to switch the current communication network of its own vehicle if the predicted network bandwidth data is less than a preset network bandwidth threshold. The device further includes: The driving data acquisition module is used to acquire the vehicle driving data of its own vehicle in historical time periods based on the current vehicle driving time; the vehicle driving data includes vehicle driving speed and vehicle geographical location. The stability prediction module is used to input the vehicle's driving speed and geographical location into a pre-trained network stability prediction model to obtain the predicted network stability data for future time periods output by the model. Correspondingly, the network switching determination module further includes: The fifth network switching unit is used to determine whether to switch the current communication network of its own vehicle based on the predicted network status data and the predicted network stability data.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle communication network switching method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the vehicle communication network switching method of any one of claims 1-3.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the vehicle communication network switching method according to any one of claims 1-3.

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