Vehicle-mounted network intelligent switching method and device, vehicle machine and storage medium

Abstract

The invention provides a vehicle-mounted network intelligent switching method and device, a vehicle machine and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: predicting a next target passing place of a vehicle and second driving state information of the vehicle at the target passing place according to acquired first driving state information of the vehicle; determining a target base station from all candidate base stations according to the second driving state information and acquired network environment information about the candidate base stations of which the signals cover the target passing place; and when the vehicle drives into the target passing place, controlling the network connection of the vehicle to be switched to the target base station. According to the method, the vehicle driving state and network environment prediction are fused, the target base station is intelligently determined and switched, the continuity, stability and adaptability of vehicle-mounted network connection are improved, and the internet-of-vehicle communication quality and the user experience in a dynamic scene are remarkably optimized.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method and device for intelligent switching of an in-vehicle network, a vehicle computer, and a storage medium. Background Art

[0002] With the continuous development of intelligent connected vehicle technology, the demand for external communication between vehicles is increasing. Vehicles need to receive and send large amounts of data in real time, including navigation information, audio and video streams, multi-sensor fusion data uploads, remote control commands, etc. These demands have driven the evolution of vehicles towards higher bandwidth, lower latency, and greater reliability.

[0003] In related technologies, vehicles primarily rely on cellular networks (such as 4G and 5G) and local wireless networks (such as Wi-Fi) for data transmission. However, at high speeds, the connection status between the vehicle and the base station frequently changes, and the communication link is unstable. Current network switching strategies are difficult to adapt to such complex and dynamic scenarios. As a result, network quality is prone to fluctuations, especially during handovers, which may lead to delays, disconnections, and jitter, seriously affecting the user experience and the continuity and stability of vehicle network services. Summary of the Invention

[0004] The problem solved by the present invention is: how to improve the continuity and stability of vehicle networks in dynamic scenarios.

[0005] To solve the above problems, the present invention provides a method and device for intelligent switching of an in-vehicle network, a vehicle computer, and a storage medium.

[0006] In a first aspect, the present invention provides a method for intelligent switching of an in-vehicle network, comprising: Predicting a next target waypoint of the vehicle and second driving state information of the vehicle at the target waypoint based on the acquired first driving state information of the vehicle; Determining a target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target transit point; When the vehicle enters the target passing place, the network connection of the vehicle is controlled to switch to the target base station.

[0007] Optionally, the first driving state information and the second driving state information both include a position and a driving direction; the prediction of the next target waypoint of the vehicle based on the acquired first driving state information of the vehicle, and the second driving state information of the vehicle at the target waypoint include: Based on the acquired current position and driving direction of the vehicle, the position to be reached by the vehicle after a preset period is predicted to be the next target passing point, and the driving direction of the vehicle at the target passing point is predicted.

[0008] Optionally, the network environment information includes beam information about a beam transmitted by an antenna of the candidate base station, and determining the target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about the candidate base stations whose signals cover the target en route location includes: The target base station is determined from all the candidate base stations based on the beam information and the driving direction of the vehicle at the target passing point.

[0009] Optionally, the beam information includes a beam direction; and determining the target base station from all the candidate base stations based on the beam information and the driving direction of the vehicle at the target waypoint includes: From all the candidate base stations, the candidate base station corresponding to the target beam direction is selected as the target base station; wherein, the target beam direction is the beam direction with the smallest angle with the driving direction of the vehicle at the target passing point or the angle is less than a preset angle.

[0010] Optionally, the network environment information further includes signal quality and signal strength; The selecting, from all the candidate base stations, the candidate base station corresponding to the target beam direction as the target base station includes: Selecting, from all the candidate base stations, a plurality of the candidate base stations corresponding to the target beam direction as base stations to be selected; The target base station is determined from all the candidate base stations based on the acquired signal qualities and signal strengths of the candidate base stations.

[0011] Optionally, the driving direction includes a first driving direction and a second driving direction, and the first driving direction is different from the second driving direction; The determining the target base station from all the candidate base stations according to the beam information and the driving direction of the vehicle at the target passing point includes: When the predicted driving direction of the vehicle at the target passing point is the first driving direction, determining a first target base station from all the candidate base stations based on the beam information and the first driving direction; When the predicted driving direction of the vehicle at the target passing point is the second driving direction, determining a second target base station from all the candidate base stations based on the beam information and the second driving direction; When the vehicle enters the target passing place, controlling the network connection of the vehicle to switch to the target base station includes: When the vehicle enters the target waypoint in a first driving direction, controlling the network connection of the vehicle to switch to the first target base station; When the vehicle enters the target passing place in the second driving direction, the network connection of the vehicle is controlled to switch to the second target base station.

[0012] Optionally, the predicting of the next target waypoint of the vehicle based on the acquired first driving state information of the vehicle and the second driving state information of the vehicle at the target waypoint includes: Based on the first driving state information of the vehicle that has been obtained, a preset model is used to predict the next target route of the vehicle and the second driving state information of the vehicle at the target route.

[0013] In a second aspect, the present invention provides an in-vehicle network intelligent switching device, comprising: A prediction module, configured to predict the next target waypoint of the vehicle and second driving state information of the vehicle at the target waypoint based on the acquired first driving state information of the vehicle; a selection module, configured to determine a target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about the candidate base stations whose signals cover the target transit point; A control module is used to control the network connection of the vehicle to switch to the target base station when the vehicle enters the target passing place.

[0014] In a third aspect, the present invention provides a vehicle computer, comprising a memory and a processor; The memory is used to store computer programs; The processor is used to implement the vehicle network intelligent switching method as described in the first aspect when executing the computer program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program. When the computer program is read and executed by a processor, the method for intelligent switching of the in-vehicle network as described in the first aspect is implemented.

[0016] The beneficial effects of the vehicle network intelligent switching method and device, vehicle computer, and storage medium of the present invention are as follows: the present invention predicts the target route of the vehicle and its second driving status information at the target route based on the acquired first driving state information of the vehicle. On the basis of ensuring the accuracy and reliability of the prediction results, it has good foresight and dynamic adaptability, which helps to improve the overall network connection stability and continuity of the vehicle during driving; by combining the acquired network environment information of each candidate base station in the target route, it determines the appropriate target base station in advance, and completes the network switching when the vehicle enters the target route, effectively avoiding the signal interruption, increased latency or service interruption problems caused by traditional passive switching, significantly enhancing the communication guarantee capability of the vehicle in dynamic scenarios, and improving the continuity and stability of the vehicle network in dynamic scenarios. Overall, the present invention integrates the comprehensive prediction of the vehicle driving state and the network environment to realize an intelligent base station switching strategy with strong adaptability and high continuity, which can significantly improve the connection quality and user experience of the vehicle network, and is particularly suitable for vehicle network application scenarios with high network performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a vehicle network intelligent switching method according to an embodiment of the present invention; Figure 2 This is a structural block diagram of an in-vehicle network intelligent switching device according to an embodiment of the present invention; Figure 3 Schematic diagram of the communication connection between the memory and processor of the vehicle computer in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0019] It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present invention described herein can be practiced in an order other than that illustrated or described herein.

[0020] Combine Figure 1 As shown, an embodiment of the present invention provides a method for intelligent switching of an in-vehicle network, comprising the following steps: Step 100: Based on the acquired first driving state information of the vehicle, predict the next target destination of the vehicle and the second driving state information of the vehicle at the target destination.

[0021] The method of this embodiment is used to predict the network status in advance and realize intelligent switching of base stations while the vehicle is traveling, thereby improving the communication stability and service continuity of the vehicle and enhancing the user experience.

[0022] Specifically, step 100 obtains the vehicle's current driving state information (referred to as first driving state information), such as the vehicle's current location, driving direction, historical driving trajectory, and speed. Based on the obtained first driving state information, a prediction is made of the next stopover point (referred to as a target stopover point) that the vehicle will reach within a certain period of time. The vehicle's driving state information at the target stopover point (referred to as second driving state information) is also predicted, such as the vehicle's driving direction at the target stopover point. The target stopover point is a location or a specific area.

[0023] In this way, by predicting the target route and the second driving status information, input parameters are provided for subsequent network switching, making the network switching strategy forward-looking and dynamically adaptable, which helps to improve the overall network connection stability and continuity of the vehicle during driving.

[0024] Step 200: Determine a target base station from all candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target transit point.

[0025] Specifically, in step 200, based on a list of base stations in the area corresponding to the target route (i.e., all base stations with signal coverage of the target route), which is pre-stored or obtained through the cloud, network environment information about these base stations (referred to as candidate base stations) is determined, including information such as each base station's geographic location, antenna type (e.g., active antenna or non-omnidirectional antenna), signal beam coverage, beam width, main beam direction (i.e., beam center pointing angle), current signal quality (e.g., signal-to-noise ratio, packet loss rate, etc.), and network load. Combining the second driving state information with the aforementioned network environment information, the availability and connection quality of each candidate base station are analyzed and evaluated, thereby determining a target base station from among all candidate base stations as the future switching target for the vehicle at the target route.

[0026] Step 300: When the vehicle enters the target passing point, control the vehicle's network connection to switch to the target base station.

[0027] Specifically, in step 300, based on the vehicle's location, when the vehicle enters the target transit point (which can be confirmed through GPS location matching), the vehicle's network connection is controlled to switch to the target base station determined in step 200. In this way, the method of this embodiment completes the screening and handover preparation of the target base station in advance before the vehicle actually arrives at the network quality-sensitive area (i.e., the target transit point), thereby effectively reducing the signal interruption, increased latency, or service interruption caused by traditional passive handover strategies. Furthermore, by integrating comprehensive information about the vehicle's future driving status and the network environment in the area, the method possesses greater foresight and intelligence, improving the stability and continuity of the vehicle's network connection, and significantly supporting Internet of Vehicles applications with high bandwidth and low latency requirements (such as autonomous driving, vehicle-infrastructure collaboration, and high-definition video calling).

[0028] In summary, the method of this embodiment predicts the target route of the vehicle and its second driving status information at the target route based on the first driving state information of the vehicle that has been obtained. On the basis of ensuring the accuracy and reliability of the prediction results, it has good foresight and dynamic adaptability, which helps to improve the overall network connection stability and continuity of the vehicle during driving; by combining the network environment information of each candidate base station in the target route that has been obtained, a suitable target base station is determined in advance, and the network switching is completed when the vehicle enters the target route, effectively avoiding the signal interruption, increased latency or service interruption problems caused by traditional passive switching, significantly enhancing the communication guarantee capability of the vehicle in dynamic scenarios, and improving the continuity and stability of the vehicle network in dynamic scenarios. Overall, the method of this embodiment integrates the comprehensive prediction of the vehicle's driving state and network environment, realizes an intelligent base station switching strategy, has strong adaptability and high continuity, can significantly improve the connection quality and user experience of the vehicle network, and is particularly suitable for vehicle network application scenarios with high network performance requirements.

[0029] Optionally, both the first driving state information and the second driving state information include a position and a driving direction; based on the acquired first driving state information of the vehicle, the next target passing point of the vehicle is predicted, and the second driving state information of the vehicle at the target passing point includes: Based on the acquired current position and driving direction of the vehicle, the position that the vehicle will reach after a preset period is predicted as the next target passing point, and the driving direction of the vehicle at the target passing point is predicted.

[0030] Specifically, based on the acquired position and driving direction of the vehicle, the position that the vehicle will reach after a preset period from the current start is predicted, and this position is used as the next target passing point, and the driving direction of the vehicle at the target passing point is predicted.

[0031] Exemplarily, the preset period includes a time period and a mileage period. When the time period is used, the vehicle's likely destination after a period of time (i.e., the time period) is estimated (or predicted) based on the first driving state information of the current vehicle, such as speed, acceleration, and driving direction, and is used as the target waypoint. When the mileage period is used, the vehicle's likely destination after a certain distance (i.e., the mileage period) is estimated based on the first driving state information of the current vehicle, and is used as the target waypoint.

[0032] In this way, by presetting a data statistical period (i.e., a preset period) and collecting and analyzing data based on the period, rather than conducting continuous and full analysis of all historical driving data or global trajectories, the computational complexity and resource consumption can be effectively reduced, and the prediction efficiency can be improved. At the same time, it is more in line with the real-time requirements of the vehicle during dynamic driving, and helps to achieve a lightweight and highly responsive network switching strategy design. While ensuring the accuracy of the prediction, it also improves the economy and scalability of vehicle resource operation, and is suitable for deployment requirements under a variety of vehicle models and different computing power platforms, thereby improving the applicability of the method of this embodiment.

[0033] Optionally, the network environment information includes beam information about a beam transmitted by an antenna of the candidate base station, and determining the target base station from all candidate base stations based on the second driving state information and the acquired network environment information about the candidate base stations at the target signal coverage en route includes: The target base station is determined from all candidate base stations based on the beam information and the vehicle's driving direction at the target passing point.

[0034] Considering that a vehicle's driving direction creates an angle with the beam direction of the connected base station, the smaller the angle, the better the signal quality, as the vehicle is in the direction of the main lobe of the beam. A larger angle results in more pronounced signal attenuation and reduced communication quality. Therefore, when determining a target base station, the angle between the vehicle's driving direction at the target location and the beam direction of the candidate base station must be considered to maximize the stability and signal quality of the communication link between the vehicle and the base station.

[0035] Specifically, network environment information includes beam information about the beams emitted by the antennas of candidate base stations. This beam information includes parameters such as the beam center direction (i.e., the main beam direction, or simply the beam direction), beam width, and coverage range. After predicting the target waypoint and the vehicle's travel direction at that target waypoint (i.e., the second driving state information), network environment information for all candidate base stations covering that target waypoint is obtained, specifically the antenna beam information for each base station. Then, combined with the vehicle's travel direction, the degree of match between each candidate base station's beam and the vehicle's travel direction is analyzed, and the target base station for handover is determined from among all candidate base stations based on this match.

[0036] For example, if the vehicle's driving direction in the target transit point is relatively consistent with the main beam direction of a candidate base station, and falls within the effective coverage range of the candidate base station beam (the coverage range can be determined based on parameters such as the location of the target transit point, the location of the candidate base station, the beam pointing angle of the active antenna of the candidate base station and its adjustable angle), then the candidate base station can be given priority as the target base station; if multiple candidate base stations meet this condition, they can be further comprehensively evaluated based on indicators such as the beam strength, signal-to-noise ratio, and network load of each base station to select the optimal target base station.

[0037] In this way, by introducing the matching judgment of beam information and vehicle driving direction, not only the accuracy of selecting the switching target base station is improved, but also the stability of network connection and the consistency of signal reception are further enhanced. It is especially suitable for vehicle network access optimization in complex scenarios such as high-speed driving and rapid direction changes.

[0038] Optionally, the beam information includes a beam direction; and determining the target base station from all candidate base stations according to the beam information and the driving direction of the vehicle at the target passing point includes: From all candidate base stations, the candidate base station corresponding to the target beam direction is selected as the target base station; wherein the target beam direction is the beam direction with the smallest angle with the vehicle's driving direction at the target passing point or the angle is less than a preset angle.

[0039] Specifically, given that a vehicle's travel direction at a target stop may differ, the angle between it and the beam direction of different candidate base stations may vary, and this angle significantly impacts signal reception quality. Generally, the smaller the angle between the vehicle's travel direction and the base station's beam direction, the more likely the vehicle is within the main lobe coverage area of ​​that beam, resulting in better signal quality and network connection stability. Therefore, when selecting a target base station, a comprehensive assessment must be made based on the angle between the vehicle's travel direction at the target stop and the beam direction of each candidate base station.

[0040] For example, based on the vehicle's predicted travel direction at the target stop, the beam direction information of all candidate base stations is traversed, and the angle between the beam direction of each candidate base station at the target stop and the vehicle's travel direction is calculated. The candidate base station corresponding to the beam direction with the smallest angle, or an angle less than a preset threshold angle, is then selected as the target base station for the current scenario. If multiple candidate base stations exist whose angles meet the preset angle condition (i.e., the smallest angle or the angle less than a preset angle), other network indicators (such as base station load and channel quality) can be further combined for sorting and screening to improve selection accuracy and select the target base station with the best network signal. This effectively achieves proactive network access optimization for the vehicle at the target stop, improving the continuity, reliability, and user experience of the in-vehicle network connection.

[0041] Optionally, the network environment information also includes signal quality and signal strength; Selecting the candidate base station corresponding to the target beam direction from all candidate base stations as the target base station includes: From all candidate base stations, multiple candidate base stations corresponding to the target beam direction are selected as candidate base stations; Based on the acquired signal qualities and signal strengths of the candidate base stations, a target base station is determined from all the candidate base stations.

[0042] Considering that different candidate base stations may have different signal coverage effects when their beam directions are similar, when selecting a target base station, it is necessary not only to refer to the angle between the beam direction and the vehicle's travel direction, but also to comprehensively evaluate the signal quality and strength of the corresponding base station to achieve a more refined base station selection strategy. Specifically, when determining the target base station, first, based on the vehicle's predicted travel direction at the target location, several candidate base stations with the smallest angle between the beam direction and the travel direction or less than a preset threshold angle are screened from all candidate base stations to form a set of candidate base stations; then, further signal quality and signal strength information corresponding to these candidate base stations is obtained. Based on this information, according to preset optimization criteria (for example, prioritizing base stations with high signal strength and good signal quality), the optimal base station in the candidate set is screened as the preferred target base station.

[0043] Optionally, the above-mentioned optimization criteria can be comprehensively evaluated according to weights. For example, by constructing a comprehensive scoring model, a comprehensive score is assigned to each candidate base station, and the one with the highest score is finally selected as the target base station. In this way, while ensuring the comprehensiveness of the target base station selection decision, the reliability of the signal connection and the transmission stability are effectively improved, and the situation of selecting the target base station based solely on the beam direction while ignoring the actual communication quality is avoided. It is especially suitable for dynamic connection optimization needs in complex urban environments or high-speed mobile scenarios.

[0044] Optionally, the driving direction includes a first driving direction and a second driving direction, and the first driving direction is different from the second driving direction; Based on the beam information and the vehicle's direction of travel at the target location, the target base station is determined from all candidate base stations, including: When the predicted driving direction of the vehicle at the target passing point is a first driving direction, determining a first target base station from all candidate base stations based on the beam information and the first driving direction; When the predicted driving direction of the vehicle at the target passing point is the second driving direction, determining the second target base station from all candidate base stations based on the beam information and the second driving direction; When the vehicle enters the target passing point, controlling the vehicle's network connection to switch to the target base station includes: When the vehicle enters the target passing place in the first driving direction, controlling the vehicle's network connection to switch to the first target base station; When the vehicle enters the target passing place in the second driving direction, the network connection of the vehicle is controlled to switch to the second target base station.

[0045] Specifically, considering that the prediction results may not be unique, for example, the predicted driving direction of the vehicle in the second driving status information of the corresponding target passing point may not be unique, but may exist in multiple directions; when the vehicle enters the target passing point in different driving directions, its position, orientation and relative relationship with each candidate base station may be significantly different, which may result in the target base station applicable to a certain driving direction not necessarily being applicable to other driving directions. To this end, in the method of this embodiment, after predicting multiple possible driving directions, such as the first driving direction and the second driving direction, matching and evaluation are performed based on the beam information (such as coverage angle, signal strength, interference situation, etc.) between each possible driving direction and the candidate base station, and the corresponding target base station is determined in advance for each driving direction. That is to say, when determining the target base station from all candidate base stations based on the beam information and the driving direction of the vehicle at the target passing point, if the predicted driving direction of the vehicle at the target passing point is the first driving direction, the first target base station is determined from all candidate base stations based on the corresponding beam information and the first driving direction; if the predicted driving direction of the vehicle at the target passing point is the second driving direction, the second target base station is determined from all candidate base stations based on the corresponding beam information and the second driving direction. Afterwards, when the vehicle actually enters the target passing place, the vehicle's driving status is obtained in real time, the actual driving direction is identified, and according to the preset target base station corresponding to the direction (such as the first target base station corresponding to the first driving direction, the second target base station corresponding to the second driving direction), the vehicle's network connection is controlled to switch to the base station. That is to say, when the vehicle enters the target passing place, if the vehicle actually enters the target passing place in the first driving direction, the vehicle's network connection is controlled to switch to the first target base station; if the vehicle enters the target passing place in the second driving direction, the vehicle's network connection is controlled to switch to the second target base station.

[0046] This approach balances prediction uncertainty with route diversity, providing a base station handover strategy based on multiple alternative routes based on pre-determination. This effectively reduces the risk of connection interruption or signal quality degradation due to prediction bias, improving the foresight, robustness, and flexibility of vehicle network connections. This significantly enhances the robustness and adaptability of vehicle communications in dynamic environments, avoiding handover failures or degradation of communication quality due to base station selection mismatches. Furthermore, through a multi-path preparation mechanism, low-latency, highly reliable network handover can be achieved, making it particularly suitable for applications requiring extremely high communication continuity, such as complex traffic conditions and high-speed mobility, thereby fully guaranteeing vehicle communication continuity and user experience.

[0047] Optionally, step 100 includes: Based on the acquired first driving state information of the vehicle, a preset model is used to predict the vehicle's next target passing point and the vehicle's second driving state information at the target passing point.

[0048] Specifically, based on the acquired first driving state information of the vehicle, a pre-trained model for predicting target destinations and driving state information (referred to as a preset model) is used to predict the vehicle's next target destination and the vehicle's second driving state information at that target destination, thereby improving prediction efficiency and accuracy. In some embodiments, the first driving state information includes parameters such as the vehicle's current position, speed, acceleration, and driving direction. The preset model can be a machine learning model based on historical driving data or a physical model based on vehicle dynamics, such as a preset model based on an LSTM (Long Short-Term Memory) neural network.

[0049] For example, while a vehicle is in motion, based on the current first driving state information, a preset model analyzes the first driving state information to predict the location of the vehicle's next target stopover point after a certain period of time (or mileage), as well as the vehicle's second driving state information at the target stopover point. Furthermore, the preset model can also incorporate external information such as traffic flow, road conditions, and weather to optimize the prediction results, thereby more accurately predicting the vehicle's driving state (e.g., driving direction, speed changes, etc.) at the target stopover point. In some embodiments, the preset model can use real-time data acquired by the vehicle's GPS system or onboard sensors as input, combined with historical driving trajectory data, and employ methods such as regression analysis, time series analysis, or deep learning to perform predictions.

[0050] In this way, by using this prediction model, the possible driving status of the vehicle can be understood in advance before it approaches the target route, thereby providing necessary input for subsequent network base station switching, traffic navigation and other systems, and improving the efficiency of intelligent services and network switching during vehicle driving.

[0051] Optionally, the first driving status information also includes navigation information. When predicting the target passing point and the second driving status information of the vehicle at the target passing point, combining the navigation information with the vehicle's dynamic driving status (such as speed, acceleration, driving direction, etc.) can further enhance the ability to predict the vehicle's motion trajectory and improve the accuracy of the prediction results.

[0052] Combine Figure 2 As shown, another embodiment of the present invention provides an in-vehicle network intelligent switching device, comprising: A prediction module, configured to predict the next target passing point of the vehicle and second driving state information of the vehicle at the target passing point based on the acquired first driving state information of the vehicle; a selection module, configured to determine a target base station from all candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target en route location; The control module is used to control the vehicle's network connection to switch to the target base station when the vehicle enters the target passing point.

[0053] The vehicle network intelligent switching device of this embodiment is used to implement the above-mentioned vehicle network intelligent switching method. Its advantages over the existing technology are the same as the advantages of the above-mentioned vehicle network intelligent switching method over the existing technology, and will not be repeated here.

[0054] Combine Figure 3 As shown, another embodiment of the present invention provides a vehicle computer, including a memory 301 and a processor 302; Memory 301, used for storing computer programs; The processor 302 is configured to implement the above-mentioned vehicle network intelligent switching method when executing the computer program.

[0055] In other words, a vehicle computer includes a memory 301 and a processor 302 coupled to the memory 301; the memory 301 is configured to store a computer program; and the processor 302 is configured to perform the following operations when executing the computer program: Predicting the next target passing point of the vehicle and second driving state information of the vehicle at the target passing point based on the acquired first driving state information of the vehicle; Determining a target base station from all candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target en route location; When the vehicle enters the target passing point, the network connection of the vehicle is controlled to switch to the target base station.

[0056] The vehicle computer of this embodiment can be used to implement the above-mentioned vehicle network intelligent switching method. Its advantages over the existing technology are the same as the advantages of the above-mentioned vehicle network intelligent switching method over the existing technology, and will not be repeated here.

[0057] Another embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is read and executed by a processor, the above-mentioned vehicle network intelligent switching method is implemented.

[0058] In other words, a non-volatile computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following operations: Predicting the next target passing point of the vehicle and second driving state information of the vehicle at the target passing point based on the acquired first driving state information of the vehicle; Determining a target base station from all candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target en route location; When the vehicle enters the target passing point, the network connection of the vehicle is controlled to switch to the target base station.

[0059] The technical solution of the embodiments of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (such as a personal computer, server, or network device) or a processor to execute all or part of the steps of the method of the embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0060] The computer-readable storage medium of this embodiment can be used to implement the above-mentioned vehicle network intelligent switching method. Its advantages over the existing technology are the same as the advantages of the above-mentioned vehicle network intelligent switching method over the existing technology, and will not be repeated here.

[0061] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A vehicle network intelligent switching method, characterized in that: include: Predicting a next target waypoint of the vehicle and second driving state information of the vehicle at the target waypoint based on the acquired first driving state information of the vehicle; Determining a target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about candidate base stations whose signals cover the target transit point; When the vehicle enters the target passing place, the network connection of the vehicle is controlled to switch to the target base station.

2. The vehicle network intelligent switching method according to claim 1, characterized in that: The first driving state information and the second driving state information both include a position and a driving direction; the prediction of the next target passing point of the vehicle based on the acquired first driving state information of the vehicle, and the second driving state information of the vehicle at the target passing point include: Based on the acquired current position and driving direction of the vehicle, the position to be reached by the vehicle after a preset period is predicted to be the next target passing point, and the driving direction of the vehicle at the target passing point is predicted.

3. The vehicle network intelligent switching method according to claim 1 or 2, characterized in that: The network environment information includes beam information about a beam transmitted by an antenna of the candidate base station, and determining the target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about the candidate base stations whose signals cover the target en route location includes: The target base station is determined from all the candidate base stations based on the beam information and the driving direction of the vehicle at the target passing point.

4. The vehicle network intelligent switching method according to claim 3, characterized in that: The beam information includes a beam direction; and determining the target base station from all the candidate base stations based on the beam information and the driving direction of the vehicle at the target waypoint includes: From all the candidate base stations, the candidate base station corresponding to the target beam direction is selected as the target base station; wherein, the target beam direction is the beam direction with the smallest angle with the driving direction of the vehicle at the target passing point or the angle is less than a preset angle.

5. The vehicle network intelligent switching method according to claim 4, characterized in that: The network environment information also includes signal quality and signal strength; The selecting, from all the candidate base stations, the candidate base station corresponding to the target beam direction as the target base station includes: Selecting, from all the candidate base stations, a plurality of the candidate base stations corresponding to the target beam direction as base stations to be selected; The target base station is determined from all the candidate base stations based on the acquired signal qualities and signal strengths of the candidate base stations.

6. The vehicle network intelligent switching method according to claim 3, characterized in that: The driving direction includes a first driving direction and a second driving direction, the first driving direction is different from the second driving direction; The determining the target base station from all the candidate base stations according to the beam information and the driving direction of the vehicle at the target passing point includes: When the predicted driving direction of the vehicle at the target passing point is the first driving direction, determining a first target base station from all the candidate base stations based on the beam information and the first driving direction; When the predicted driving direction of the vehicle at the target passing point is the second driving direction, determining a second target base station from all the candidate base stations based on the beam information and the second driving direction; When the vehicle enters the target passing place, controlling the network connection of the vehicle to switch to the target base station includes: When the vehicle enters the target waypoint in a first driving direction, controlling the network connection of the vehicle to switch to the first target base station; When the vehicle enters the target passing place in the second driving direction, the network connection of the vehicle is controlled to switch to the second target base station.

7. The vehicle network intelligent switching method according to claim 1, characterized in that: The predicting of the next target waypoint of the vehicle based on the acquired first driving state information of the vehicle and the second driving state information of the vehicle at the target waypoint includes: Based on the first driving state information of the vehicle that has been obtained, a preset model is used to predict the next target route of the vehicle and the second driving state information of the vehicle at the target route.

8. An intelligent switching device for an in-vehicle network, characterized in that: include: A prediction module, configured to predict the next target waypoint of the vehicle and second driving state information of the vehicle at the target waypoint based on the acquired first driving state information of the vehicle; a selection module, configured to determine a target base station from all the candidate base stations based on the second driving state information and the acquired network environment information about the candidate base stations whose signals cover the target transit point; A control module is used to control the network connection of the vehicle to switch to the target base station when the vehicle enters the target passing place.

9. A vehicle computer, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the vehicle network intelligent switching method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is read and executed by a processor, the method for intelligent switching of an in-vehicle network according to any one of claims 1 to 7 is implemented.

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