Network optimization method, vehicle, storage medium and computer program product

Abstract

The present application discloses a network optimization method, a vehicle, a storage medium, and a computer program product, relating to the technical field of vehicles. The network optimization method of the present application is applied to a vehicle, and the vehicle includes an in-vehicle communication module and an in-vehicle computing module. Specifically, it includes: detecting the module state of the in-vehicle computing module, where the module state is a usage state or an idle state; when it is detected that the module state is the usage state, reading the interaction log of the in-vehicle computing module; when it is determined according to the interaction log that there is a network lag phenomenon in the in-vehicle computing module, performing a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy. By adopting the present application, the technical effect of improving the efficiency of network optimization operations is achieved.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a network optimization method, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automotive industry, vehicles have an increasing demand for the real-time performance and stability of network connections. In particular, in-vehicle communication modules, in-vehicle computing modules, etc. highly rely on a reliable network environment with low latency.

[0003] In the related art, a vehicle periodically uploads network status parameters to a base station, and the base station filters all switchable base stations based on the network status parameters to determine a target base station / target cell that best matches the network status parameters of the vehicle, and then sends a handover instruction to the target base station / target cell to complete the network optimization operation of the vehicle.

[0004] However, the base station usually needs to process a large amount of network status data uploaded from the vehicle side, which results in a large delay in the process from collecting network status parameters to specifying a handover strategy, thereby greatly reducing the efficiency of the network optimization operation. Summary of the Invention

[0005] The main purpose of this application is to provide a network optimization method, a vehicle, a storage medium, and a computer program product, aiming to solve the technical problem of low efficiency of network optimization operations in the related art.

[0006] To achieve the above object, this application proposes a network optimization method. The network optimization method is applied to a vehicle, and the vehicle includes an in-vehicle communication module and an in-vehicle computing module. The network optimization method includes:

[0007] Detect the module status of the in-vehicle computing module, where the module status is a usage status or an idle status;

[0008] When it is detected that the module status is the usage status, read the interaction log of the in-vehicle computing module;

[0009] When it is determined according to the interaction log that there is a network lag phenomenon in the in-vehicle computing module, perform a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy.

[0010] In an embodiment, after the step of reading the interaction log of the in-vehicle computing module, the method further includes:

[0011] Determine the number of screen clicks and the screen click time included in the interaction log, and determine the user operation frequency according to the number of screen clicks and the screen click time;

[0012] When it is detected that the user operation frequency reaches the preset click frequency threshold, it is determined that there is a network lag phenomenon in the in-vehicle computing module, and the step of performing network optimization operations according to the target network optimization strategy is executed.

[0013] In one embodiment, the vehicle is communicatively connected to the cloud server. After the step of detecting the module state of the in-vehicle computing module, the method further includes:

[0014] When it is detected that the module state is the usage state, a plurality of first network state parameters corresponding to the in-vehicle computing module are determined;

[0015] A plurality of the first network state parameters are sent to the cloud server, and a network environment detection result sent by the cloud server is received, where the network environment detection result is generated by a lightweight time series prediction model deployed inside the cloud server based on the plurality of first network state parameters;

[0016] A target network optimization strategy is determined according to the network environment detection result, and network optimization operations are performed according to the target network optimization strategy.

[0017] In one embodiment, the step of determining a target network optimization strategy according to the network environment detection result includes:

[0018] When it is detected that the network environment detection result is that the in-vehicle computing module is in a weak network environment, a preset first network optimization strategy is determined as the target network optimization strategy, where the first network optimization strategy is a network optimization strategy for switching the communication cell corresponding to the vehicle;

[0019] When it is detected that the network environment detection result is that the in-vehicle computing module is in a network change environment, a preset second network optimization strategy is determined as the target network optimization strategy, where the second network optimization strategy is a network optimization strategy for switching the network connection mode corresponding to the vehicle.

[0020] In one embodiment, the vehicle is communicatively connected to the target mobile terminal. The step of performing network optimization operations according to the target network optimization strategy includes:

[0021] When it is detected that the target network optimization strategy is the first network optimization strategy, a cell feature database sent by the target mobile terminal is received;

[0022] The cell feature database is queried based on a plurality of first network state parameters to determine a target cell, and the vehicle is made to connect to the target cell through the in-vehicle communication module.

[0023] In one embodiment, the step of performing network optimization operations according to the target network optimization strategy further includes:

[0024] When it is detected that the target network optimization strategy is the second network optimization strategy, determine the preset target network connection mode;

[0025] Switch the current network connection mode corresponding to the vehicle to the target network connection mode through the vehicle-mounted communication module.

[0026] In one embodiment, after the step of detecting the module state of the vehicle-mounted computing module, the method further includes:

[0027] When it is detected that the module state is the idle state, determine multiple second network state parameters corresponding to the vehicle-mounted communication module;

[0028] Determine the preset global network environment judgment rule, and determine the state parameter thresholds corresponding to the multiple second network state parameters included in the global network environment judgment rule;

[0029] Compare the multiple second network state parameters with their corresponding state parameter thresholds to obtain multiple first comparison results, and determine the target network state parameter based on the multiple first comparison results, where the target network state parameter is the second network state parameter that reaches the state parameter threshold;

[0030] When it is detected that the number of the target network state parameters reaches the first quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a weak network environment;

[0031] Determine the first network optimization strategy as the target network optimization strategy, and perform network optimization operations according to the target network optimization strategy.

[0032] In one embodiment, after the step of determining the target network state parameter based on the multiple first comparison results, the method further includes:

[0033] When it is detected that the number of the target network state parameters reaches the second quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a network change environment, where the second quantity threshold is greater than the first quantity threshold;

[0034] Determine the second network optimization strategy as the target network optimization strategy, and perform network optimization operations according to the target network optimization strategy.

[0035] In addition, to achieve the above object, the present application further provides a vehicle, which includes: a vehicle-mounted communication module, a vehicle-mounted computing module, a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the network optimization method as described above.

[0036] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the network optimization method as described above are implemented.

[0037] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the network optimization method as described above are implemented.

[0038] The network optimization method provided by the embodiment of the present application is applied to a vehicle, and the vehicle includes a vehicle-mounted communication module and a vehicle-mounted computing module. By detecting the module state of the vehicle-mounted computing module, where the module state is a usage state or an idle state; in the case where the module state is detected to be the usage state, reading the interaction log of the vehicle-mounted computing module; in the case where it is determined according to the interaction log that there is a network lag phenomenon in the vehicle-mounted computing module, performing a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy.

[0039] In this embodiment, during the driving process of the vehicle, first, the vehicle-mounted computing module configured for itself is detected to determine whether the module state of the vehicle-mounted computing module is a usage state or an idle state. After that, in the case where the module state of the vehicle-mounted computing module is detected to be the usage state, the vehicle further reads the interaction log included in the vehicle-mounted computing module. Finally, the vehicle identifies whether there is a network lag phenomenon in the vehicle-mounted computing module according to the interaction log, and in the case where it is determined that there is a network lag phenomenon in the vehicle-mounted computing module, determines the preset first network optimization strategy or the second network optimization strategy as the target network execution strategy, and actively performs a network optimization operation based on the target network execution strategy to change the connected base station or the network connection mode.

[0040] Thus, the present application solves the technical problem of low efficiency in network optimization operations in related technologies. That is, when it is detected that the in-vehicle computing module is in use, the present application determines whether there is a network lag phenomenon in the in-vehicle computing module based on the interaction log of the in-vehicle computing module, and when it is detected that there is a network lag phenomenon in the in-vehicle computing module, the network optimization operation is actively executed according to the target network optimization strategy, so that the vehicle terminal can actively send a network optimization request to the base station, eliminating the need for the base station to further screen for a suitable target base station / target cell after collecting network status parameters, thereby reducing the delay in the network optimization operation process and greatly improving the efficiency of the network optimization operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0043] Figure 1 Schematic diagram of an existing network optimization operation scenario related to an embodiment of the network optimization method of the present application;

[0044] Figure 2 Flow chart provided by Embodiment 1 of the network optimization method of the present application;

[0045] Figure 3 Schematic diagram of a multi-terminal interaction scenario related to an embodiment of the network optimization method of the present application;

[0046] Figure 4 Schematic diagram of the module structure of the network optimization device in the embodiment of the present application;

[0047] Figure 5 Schematic diagram of the device structure of the hardware operating environment related to the network optimization method in the embodiment of the present application.

[0048] The implementation, functional features, and advantages of the present application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0050] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the drawings in the specification and the specific embodiments.

[0051] In this embodiment, for the convenience of description, the following takes vehicles internally configured with in-vehicle computing modules and in-vehicle communication modules, or mobile terminals, data storage control terminals, PCs and other terminals connected to the electronic control unit supporting the vehicle as the execution subjects for elaboration.

[0052] It should be noted that the in-vehicle computing module is a device with relatively strong computing power configured in the vehicle, and specifically can be hardware devices such as an intelligent cockpit controller or an intelligent driving controller. In addition, the in-vehicle communication device is a communication device with relatively weak computing power configured in the vehicle, which can switch network connection modes and base stations.

[0053] Based on the above vehicle, the overall concept of the network optimization method of this application is proposed here.

[0054] With the continuous development of the automotive industry, vehicles have higher and higher requirements for the real-time performance and stability of network connections. In particular, in-vehicle communication modules, in-vehicle computing modules, etc. highly rely on a reliable network environment with low latency. In the related art, please refer to Figure 1 , Figure 1 which is a schematic diagram of an existing network optimization operation scenario involved in an embodiment of the network optimization method of this application. As shown in Figure 1 , the vehicle periodically uploads network status parameters to the base station. The base station screens all switchable base stations based on the network status parameters to determine the target base station / target cell that best matches the network status parameters of the vehicle, and then sends a handover instruction to the target base station / target cell to complete the network optimization operation of the vehicle. However, the base station usually needs to process a large amount of network status data uploaded from the vehicle end, which results in a large time delay in the process of the base station collecting network status parameters and specifying a handover strategy, thereby greatly reducing the efficiency of the network optimization operation.

[0055] In view of the above phenomenon, this application provides a network optimization method. The network optimization method is applied to a vehicle, and the vehicle includes an in-vehicle communication module and an in-vehicle computing module. The network optimization method includes: detecting the module status of the in-vehicle computing module, where the module status is a usage status or an idle status; in the case where the module status is detected to be the usage status, reading the interaction log of the in-vehicle computing module; in the case where it is determined according to the interaction log that there is a network lag phenomenon in the in-vehicle computing module, performing a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy.

[0056] Thus, the present application solves the technical problem of low efficiency in network optimization operations in related technologies. That is, when it is detected that the vehicle-mounted computing module is in a usage state, the present application determines whether there is a network lag phenomenon in the vehicle-mounted computing module based on the interaction log of the vehicle-mounted computing module, and when it is detected that there is a network lag phenomenon in the vehicle-mounted computing module, the network optimization operation is actively executed according to the target network optimization strategy, so that the vehicle terminal can actively initiate a network optimization request to the base station, enabling the base station to not need to further screen a suitable target base station / target cell after collecting network status parameters, thereby reducing the delay in the network optimization operation process and greatly improving the efficiency of the network optimization operation.

[0057] Based on the overall concept of the network optimization method of the present application, an embodiment of the present application provides a network optimization method. Referring to Figure 2 , Figure 2 is a schematic flowchart of the first embodiment of the network optimization method of the present application. In this embodiment, the network optimization method is applied to a vehicle, and the vehicle includes a vehicle-mounted communication module and a vehicle-mounted computing module. The network optimization method includes steps S10 to S30:

[0058] Step S10: Detect the module state of the vehicle-mounted computing module, where the module state is a usage state or an idle state;

[0059] It should be noted that the module state is the network resource occupancy state of the vehicle-mounted computing module, including: a usage state or an idle state. When the vehicle-mounted computing module is in the usage state, it indicates that the user starts a network-related application program (such as a music APP, a navigation APP, etc.) through the vehicle-mounted computing module, and the network-related application program is occupying network resources. Similarly, when the vehicle-mounted computing module is in the idle state, it indicates that all network-related application programs are not running, or the running network-related application programs do not actively occupy network resources (such as an APP that is performing a local update in the background).

[0060] In this embodiment, during the driving process of the vehicle, the vehicle-mounted control system integrated in the vehicle first accesses the system process management interface of the vehicle-mounted computing module configured in the vehicle to obtain the application running list of the vehicle-mounted computing module through the system process management interface, and determines the network occupancy parameters corresponding to the running network-related application programs respectively according to the application running list. Then, the vehicle management system determines that the module state of the vehicle-mounted computing module is in the usage state when it detects that the network occupancy parameters of at least one network-related application program reach the preset occupancy parameter threshold. Similarly, when the vehicle management system detects that all network-related application programs are in the closed state / the network occupancy parameters of all network-related application programs do not reach the occupancy parameter threshold, it determines that the module state is in the idle state.

[0061] Exemplarily, for example, when the vehicle is in motion, if the user starts the music APP through the in-vehicle intelligent cockpit controller configured in the intelligent cockpit and controls the music APP to play online music in real time, at this time, the in-vehicle control system integrated in the vehicle accesses the system process management interface in the intelligent cockpit controller to obtain the list of started APPs through the system process management interface, and determines that the in-vehicle APP being started is the music APP according to the list of started APPs. At the same time, the in-vehicle management system determines the network occupancy parameters corresponding to the music APP according to the list of started APPs, and determines that the music APP is in an active state when it detects that the network occupancy parameters reach the preset occupancy parameter threshold. At this time, the in-vehicle management system determines that the module state of the intelligent cockpit controller is in a used state.

[0062] Similarly, in this embodiment and another embodiment, if the in-vehicle control system determines that all system APPs are not running after reading the list of started APPs, it directly determines that the module state of the cockpit controller is in an idle state; or, if the in-vehicle control system reads the list of started APPs and determines that the music APP is running, but the network occupancy parameters of the music APP do not reach the above-mentioned occupancy parameter threshold, the in-vehicle control system determines that the music APP is in an inactive state and determines that the module state of the cockpit controller is in an idle state.

[0063] Step S20: When it is detected that the module state is in a used state, read the interaction log of the in-vehicle computing module;

[0064] It should be noted that the interaction log includes various operation behaviors generated when the user controls the in-vehicle computing module through the human-machine interface. It can be understood that the operation behaviors include direct interaction behaviors such as touch clicks, voice commands, and gesture operations, and indirect interaction behaviors such as sound quality adjustment, play rate switching, and application window scaling.

[0065] In this embodiment, when the in-vehicle control system detects that the module state of the in-vehicle computing module is in a used state, it further accesses the in-vehicle computing module to obtain the interaction log, and reads each direct interaction behavior and / or indirect interaction behavior included in the interaction log, so as to determine whether there is a network lag phenomenon in the process of using the in-vehicle computing module based on each direct interaction behavior and / or indirect interaction behavior.

[0066] Exemplarily, for example, if the vehicle management system determines that the module status of the intelligent cockpit controller is in the usage state, it further accesses the intelligent cockpit controller to obtain the interaction log stored therein. The vehicle management system reads the touch click events included in the interaction log, and determines the number of clicks and click times generated when the user performs interaction operations through the HMI (Human-Machine Interface). Furthermore, when it is recognized that the user continuously clicks the pause button in the music APP based on the number of clicks and click times, it is determined that there is a network lag phenomenon in the intelligent cockpit controller;

[0067] Similarly, in this embodiment and another embodiment, after the vehicle management system obtains the interaction log, it can also read the sound quality adjustment events included in the interaction log, and directly determine that there is a network lag phenomenon in the intelligent cockpit controller when it is determined according to the sound quality adjustment events that the user performs a sound quality downgrade operation in the music APP;

[0068] Similarly, in this embodiment and another embodiment, if the vehicle management system determines according to the interaction log that the user does not continuously click the pause button or perform a sound quality downgrade operation in the music APP after obtaining the interaction log, it can be determined that there is no network lag phenomenon in the intelligent cockpit controller.

[0069] In this way, the vehicle can recognize the interaction behavior triggered by the user on the in-vehicle computing module, and determine whether there is a network lag phenomenon in the in-vehicle computing module according to the interaction behavior.

[0070] In a feasible implementation manner, after the above step S20, the network optimization method of the present application may further include steps S201 to S202:

[0071] Step S201: Determine the number of screen clicks and screen click times included in the interaction log, and determine the user operation frequency according to the number of screen clicks and the screen click times;

[0072] Step S202: When it is detected that the user operation frequency reaches a preset click frequency threshold, determine that there is a network lag phenomenon in the in-vehicle computing module, and execute the step of performing network optimization operations according to the target network optimization strategy.

[0073] In this embodiment, after the vehicle-mounted control system obtains the interaction log, it reads the number of screen clicks and the screen click time included in the interaction log, and determines the user operation frequency generated by the user on the vehicle-mounted computing module within a certain period of time according to the number of screen clicks and the screen click time. Then, the vehicle-mounted control system obtains a preset click frequency threshold, and compares the user operation frequency with the click frequency threshold. When the vehicle-mounted control system detects that the user operation frequency reaches the click frequency threshold, it determines that there is a network lag phenomenon in the vehicle-mounted computing module.

[0074] Exemplarily, for example, after the vehicle-mounted control system obtains the interaction log, it reads the interaction log to determine the number of operations of the user's screen click operation on the HMI when the user controls the intelligent cockpit controller through the HMI, and determines the corresponding operation time for each click operation of the user on the HMI according to the interaction log. Then, the vehicle-mounted control system calculates the user operation frequency of the user's operation on the HMI within a certain period of time according to the number of clicks and the click time. After that, the vehicle-mounted control system obtains a preset click frequency threshold, and compares the user operation frequency with the click frequency threshold. When the vehicle-mounted control system detects that the user operation frequency reaches the click frequency threshold, it determines that the intelligent cockpit controller cannot respond to the user's click operation. At this time, the vehicle-mounted control system determines that there is a network lag phenomenon in the intelligent cockpit controller.

[0075] In addition, in this embodiment and another embodiment, after the vehicle management system obtains the interaction log, it can also read the sound quality adjustment events included in the interaction log, and directly determine that there is a network lag phenomenon in the intelligent cockpit controller when it determines that the user performs a sound quality downgrade operation in the music APP according to the sound quality adjustment events.

[0076] In this way, the vehicle can identify the interaction behavior triggered by the user on the vehicle-mounted computing module, and identify whether there is a network lag phenomenon in the vehicle-mounted computing module according to the interaction behavior.

[0077] Step S30: When it is determined that there is a network lag phenomenon in the vehicle-mounted computing module according to the interaction log, perform a network optimization operation according to the target network optimization strategy, where the target network optimization strategy is the first network optimization strategy or the second network optimization strategy;

[0078] It should be noted that the target network optimization strategy is a network adjustment scheme dynamically selected according to the current network environment, including the first network optimization strategy or the second network optimization strategy. The first network optimization strategy is a network optimization strategy for switching the communication cell corresponding to the vehicle, and the second network optimization strategy is a network optimization strategy for adjusting the network connection mode corresponding to the vehicle.

[0079] In this embodiment, when the vehicle management system determines that there is a network lag phenomenon in the vehicle-mounted computing module based on the interaction log, it determines the preset first network optimization strategy or the second network optimization strategy as the target network optimization strategy, and performs operations such as switching cells / adjusting the network connection mode according to the target network optimization strategy to complete the network optimization operation.

[0080] Exemplarily, for example, when the vehicle management system determines that there is a network lag phenomenon in the music APP running in the intelligent cockpit controller based on the above interaction log, it first determines that the target network optimization strategy for improving the network environment of the intelligent cockpit controller is the preset first network optimization strategy or the second network optimization strategy. Then, the vehicle management system controls the vehicle-mounted communication device according to the target network optimization strategy to make the vehicle-mounted communication device perform operations such as switching cells / switching the network connection mode from 5G mode to 4G mode, so as to optimize the network environment of the intelligent cockpit controller.

[0081] In addition, in this embodiment and another embodiment, before the vehicle management system controls the vehicle-mounted communication device to perform operations such as switching cells / switching the network connection mode from 5G mode to 4G mode, the vehicle management system can also first determine the application priorities corresponding to each running APP, and determine the non-critical APPs running according to the application priorities. Then, it compresses the bandwidth corresponding to each non-critical APP according to the preset compression ratio. After that, the vehicle management system re-detects whether there is a lag phenomenon in the vehicle-mounted computing module, and if it determines that there is still a lag phenomenon in the vehicle-mounted computing module, it performs operations such as switching cells / switching the network connection mode from 5G mode to 4G mode according to the target network optimization strategy. It can be understood that in addition to compressing the bandwidth of non-critical APPs, the vehicle management system can also perform other parameter adjustment operations such as adjusting real-time traffic and optimizing TCP operations. The specific content of the parameter adjustment operation is not limited in this application.

[0082] In this way, the vehicle can actively send a network optimization request to the base station, so that the base station does not need to further screen a suitable target cell after collecting the network status parameters, thereby reducing the delay in the network optimization operation process and greatly improving the efficiency of the network optimization operation.

[0083] In a feasible implementation manner, the vehicle is communicatively connected to the target mobile terminal. The step of "performing a network optimization operation according to the target network optimization strategy" in the above step S30 may specifically include steps S301~S302:

[0084] Step S301: When it is detected that the target network optimization strategy is the first network optimization strategy, receive the cell feature database sent by the target mobile terminal;

[0085] Step S302: Query the cell feature database based on multiple first network state parameters to determine a target cell, and instruct the vehicle to connect to the target cell through the vehicle-mounted communication module.

[0086] It should be noted that the cell feature database is a dynamic data set generated by unsupervised contrast learning of a target mobile terminal communicatively connected to the vehicle. The cell feature database includes multi-dimensional vector features (such as signal strength, load rate, geographical location, historical performance, etc.) of multiple cells included in each of multiple base stations. It can be understood that the type and quantity of features included in the cell feature database can be set by those skilled in the art according to actual needs, and this application does not limit this.

[0087] In this embodiment, when the vehicle-mounted control system determines that there is a network lag phenomenon in the vehicle-mounted computing module, if it is determined that the target network optimization strategy to be executed is the above-mentioned first network optimization strategy, the vehicle-mounted control system first receives the cell feature database sent by the target mobile terminal communicatively connected to itself. Then, the vehicle-mounted control system detects the vehicle-mounted computing module to obtain multiple first network state parameters corresponding to the vehicle-mounted computing module, and screens each preset cell included in the cell feature database according to the multiple first network state parameters to determine the cells to be screened that meet the multiple first network state parameters, and determines the target cell that best matches the vehicle-mounted computing module according to the stability scores of each cell to be screened corresponding to them in the cell feature database. The vehicle-mounted control system sends the target cell to the vehicle-mounted communication module, and the vehicle-mounted communication module sends a handover request to the target cell to hand over the cell currently connected by the vehicle to the target cell.

[0088] Exemplarily, for example, please refer to Figure 3 , Figure 3 which is a schematic diagram of a multi-terminal interaction scenario involved in an embodiment of the network optimization method of this application. As Figure 3As shown, when the vehicle-mounted control system determines that there is a network lag in the intelligent cockpit controller, if it is determined that the target network optimization strategy to be executed is the above-mentioned first network optimization strategy, the vehicle-mounted control system first receives the cell feature database sent by the target mobile terminal communicatively connected to the vehicle. After that, the vehicle-mounted control system detects the intelligent cockpit controller to determine multiple first network status parameters included in the intelligent cockpit controller, such as RSSI (Received Signal Strength Indication) and latency. The vehicle-mounted control system then queries the cell feature database based on the multiple first network status parameters, and thus, based on the multiple first network status parameters, determines candidate cells in the cell feature database whose signal strength is greater than the RSSI and whose load rate is less than a certain threshold. The vehicle-mounted control system then sorts the candidate cells according to factors such as their respective stability scores and geographical locations in order of priority to determine the target cell with the highest priority. Finally, the vehicle-mounted control system sends the target cell to the vehicle-mounted communication module Tx, and the vehicle-mounted communication module Tx sends a handover request carrying the vehicle-end identity information to the target cell to switch the connection to the target cell.

[0089] In this way, the vehicle can actively initiate a network optimization request to the base station, so that the base station does not need to further screen for a suitable target cell after collecting network status parameters, thereby reducing the latency in the network optimization operation process and greatly improving the efficiency of the network optimization operation.

[0090] In a feasible implementation manner, the vehicle is communicatively connected to the target mobile terminal, and the step of "performing network optimization operations according to the target network optimization strategy" in step S30 may further include steps S303 to S304:

[0091] Step S303: When it is detected that the target network optimization strategy is the second network optimization strategy, determine the preset target network connection mode;

[0092] Step S304: Switch the current network connection mode corresponding to the vehicle to the target network connection mode through the vehicle-mounted communication module.

[0093] In this embodiment, when the vehicle-mounted control system determines that there is a network lag in the vehicle-mounted computing module, if it is determined that the target network optimization strategy to be executed is the above-mentioned second network optimization strategy, the vehicle-mounted control system first determines the target network connection mode to be switched. After that, the vehicle-mounted control system sends the target network connection mode to the vehicle-mounted communication module, and the vehicle-mounted communication module switches the current network connection mode of the vehicle to the target network connection mode.

[0094] Exemplarily, for example, when the vehicle-mounted control system determines that there is a network lag phenomenon in the intelligent cockpit controller, if it is determined that the target network optimization strategy to be executed is the above-mentioned second network optimization strategy, then first, based on the above-mentioned first network state parameters such as RSSI and latency, it is determined that the 4G low-band system is the target network connection system. After that, the vehicle-mounted control system sends the target network connection system to the vehicle-mounted communication module Tx, and the vehicle-mounted communication module Tx sends a system switching request to the base station, thereby closing the 5G radio frequency module configured in the vehicle, activating the 4G module and connecting it to the specified frequency band, so that the connection system of the vehicle is switched from the 5G mode to the 4G low-band system.

[0095] It can be understood that the specific process of the vehicle-mounted control system determining the target network connection system based on the first network state parameters is prior art, so it will not be elaborated here.

[0096] In this embodiment, during the driving process of the vehicle, the vehicle-mounted control system integrated in the vehicle first accesses the system process management interface of the vehicle-mounted computing module configured in the vehicle, so as to obtain the application running list of the vehicle-mounted computing module through the system process management interface, and determine the network occupancy parameters corresponding to the running network-related application programs respectively according to the application running list. Then, the vehicle management system further determines that the module state of the vehicle-mounted computing module is in the use state when it detects that the network occupancy parameters of at least one network-related application program reach the preset occupancy parameter threshold based on the obtained application running list and each network occupancy parameter. Similarly, when the vehicle management system detects that all network-related application programs are in the closed state / the network occupancy parameters of all network-related application programs do not reach the occupancy parameter threshold, it determines that the module state is in the idle state. After that, the vehicle-mounted control system accesses the vehicle-mounted computing module to obtain the interaction log, and reads each direct interaction behavior and / or indirect interaction behavior included in the interaction log, so as to judge whether there is a network lag phenomenon in the vehicle-mounted computing module during the use process based on each direct interaction behavior and / or indirect interaction behavior. Finally, when the vehicle management system determines that there is a network lag phenomenon in the vehicle-mounted computing module according to the interaction log, it determines the preset first network optimization strategy or second network optimization strategy as the target network optimization strategy, and performs operations such as switching cells / adjusting the network connection system according to the target network optimization strategy to complete the network optimization operation.

[0097] Thus, the present application solves the technical problem of low efficiency in network optimization operations in the related art, that is, the present application determines whether there is a network lag phenomenon in the in-vehicle computing module based on the interaction log of the in-vehicle computing module when it is detected that the in-vehicle computing module is in use, and actively executes network optimization operations according to the target network optimization strategy when it is detected that there is a network lag phenomenon in the in-vehicle computing module, so that the vehicle terminal can actively send a network optimization request to the base station, enabling the base station to avoid further screening for a suitable target base station / target cell after collecting network status parameters, thereby reducing the delay in the network optimization operation process and greatly improving the efficiency of the network optimization operation.

[0098] Based on the first embodiment of the present application, a second embodiment of the present application is proposed here. In the second embodiment of the present application, for the same or similar content as in the above embodiments, reference can be made to the above introduction and will not be repeated hereinafter. On this basis, the vehicle is communicatively connected to the cloud server. After the above step S10, the network optimization method of the present application may further include steps A10 to A30:

[0099] Step A10: When it is detected that the module status is the use status, determine a plurality of first network status parameters corresponding to the in-vehicle computing module;

[0100] Step A20: Send the plurality of first network status parameters to the cloud server and receive the network environment detection result sent by the cloud server, where the network environment detection result is generated by a lightweight time series prediction model deployed inside the cloud server based on the plurality of first network status parameters;

[0101] Step A30: Determine a target network optimization strategy according to the network environment detection result and execute network optimization operations according to the target network optimization strategy.

[0102] In this embodiment, when the vehicle-mounted control system detects that the module state of the above vehicle-mounted computing module is in the usage state, it further collects a plurality of first network state parameters generated by the vehicle-mounted computing module. After that, the vehicle-mounted control system uploads the collected plurality of first network state parameters to the cloud server communicatively connected to the vehicle. The cloud server generates input features based on the plurality of first network state parameters, and inputs the input features into the lightweight time series prediction model configured in the cloud server. The lightweight time series prediction model generates a hidden state corresponding to the input features. Then, the cloud server determines the network environment where the intelligent cockpit controller is located according to the hidden state output by the lightweight time series prediction model, and generates a network environment detection result based on this network environment. The cloud server sends the network environment detection result to the vehicle-mounted management system. Finally, the vehicle-mounted control system reads the network environment detection result to determine the target network optimization strategy as the above first network optimization strategy or second network optimization strategy, and then controls the vehicle-mounted communication module according to the target network optimization strategy to perform network optimization operations.

[0103] Exemplarily, for example, as Figure 3 shown, when the vehicle-mounted control system detects that the module state of the above vehicle-mounted computing module is in the usage state, it detects the intelligent cockpit controller to obtain a plurality of first network state parameters such as RSSI, latency, packet loss rate, satellite signal strength, and throughput generated by the intelligent cockpit controller. After that, the vehicle-mounted control system accesses the cloud server communicatively connected to the vehicle and uploads the plurality of first network state parameters to the cloud server. At this time, the cloud server generates input features containing the plurality of first network state parameters based on RSSI, latency, packet loss rate, satellite signal strength, and throughput , and then the cloud server inputs the input features

[0104] into the lightweight LSTM time series prediction model configured in itself:

[0104] where, is the input weight matrix for mapping the input features to the hidden space under (RSSI, throughput, latency, packet loss rate, satellite signal strength), the value of is set according to the memory limit (typical values are 64 or 256), is the recurrent weight matrix for modeling the time series state transition relationship to prevent gradient explosion, is the hidden state output at the previous moment, which is used to retain the historical network feature memory to initialize the all-zero vector, is an activation function, specifically the Hard Sigmoid function. It can be understood that compared with the traditional Sigmoid function, the Hard Sigmoid function has the characteristic of lower computational complexity;

[0105] The LSTM time series prediction model thus processes the input through the input weight matrix for the input features to output the corresponding hidden state , and then the cloud server reads the time series features of the network state contained in the hidden state . When it determines that the intelligent cockpit controller is in a weak network environment based on the time series features of the network state, it generates the corresponding network environment detection result. Then the cloud server sends the network environment detection result to the vehicle control system. Finally, if the vehicle control system determines that the intelligent cockpit controller is in a weak network environment according to the network environment detection result, it determines the above first network optimization strategy as the target network optimization strategy and controls the vehicle-mounted communication device Tx to perform a cell switching operation according to the first network optimization strategy;

[0106] Similarly, if the cloud server determines that the intelligent cockpit controller is in a network change environment (such as changing from a normal network environment to a basement network environment, etc.) based on the time series features of the network state, it generates the corresponding network environment detection result. Then the cloud server sends the network environment detection result to the vehicle control system. Finally, if the vehicle control system determines that the intelligent cockpit controller is in a network change environment according to the network environment detection result, it determines the above second network optimization strategy as the target network optimization strategy and controls the vehicle-mounted communication device Tx to perform a connection mode switching operation according to the first network optimization strategy.

[0107] It should be noted that the lightweight LSTM time series prediction model fuses the input features (such as RSSI, delay) at the current moment with the historical hidden features through the recurrent weight matrix and the recurrent weight matrix, thereby forming a dynamic modeling of the network state, making the output hidden state include the mutation mode of network parameters (such as a sudden drop in RSSI in the basement scenario) and periodic fluctuations (such as a periodic increase in the packet loss rate in a weak network scenario), and then accurately identify whether the network environment where the vehicle is located is a weak network environment or a network change environment. It can be understood that there are many training methods for the lightweight LSTM time series prediction model, and this application does not limit it.

[0108] In this way, the vehicle can upload the collected network state data to the cloud server. The cloud server processes the network state parameters through the lightweight time series prediction model to judge the network environment where the vehicle is located. Then the vehicle can screen the network optimization strategy that matches the network environment, further improving the efficiency of network optimization operations.

[0109] In a feasible implementation manner, the step of "determining a target network optimization strategy according to the network environment detection result" in step A30 may specifically include steps A301 to A302:

[0110] Step A301: When it is detected that the network environment detection result is that the vehicle-mounted computing module is in a weak network environment, determine a preset first network optimization strategy as the target network optimization strategy, where the first network optimization strategy is a network optimization strategy for switching the communication cell corresponding to the vehicle;

[0111] Step A302: When it is detected that the network environment detection result is that the vehicle-mounted computing module is in a network change environment, determine a preset second network optimization strategy as the target network optimization strategy, where the second network optimization strategy is a network optimization strategy for switching the network connection mode corresponding to the vehicle.

[0112] In this embodiment, after the vehicle-mounted control system receives the network environment detection result sent by the cloud server, if it is determined according to the network environment detection result that the vehicle-mounted computing module is in a weak network environment, the above-mentioned first network optimization strategy is determined as the target network optimization strategy to be executed. Similarly, if the vehicle-mounted control system determines according to the network environment detection result that the vehicle-mounted computing module is in a network change environment, the second network optimization strategy for controlling the vehicle-mounted communication module to switch the network connection mode is determined as the target network optimization strategy.

[0113] Exemplarily, for example, when the cloud server determines that the intelligent cockpit controller is in a weak network environment, it generates a corresponding network environment detection result. The cloud server then sends the network environment detection result to the vehicle-mounted control system. At this time, the vehicle-mounted control system receives the network environment detection result and reads the network environment included in the network environment detection result. The vehicle-mounted control system determines that this network environment is a weak network environment and determines the preset first network optimization strategy for controlling the vehicle-mounted communication device Tx to switch the communication cell as the target network optimization strategy;

[0114] Similarly, when the cloud server determines that the intelligent cockpit controller is in a network change environment, it generates a corresponding network environment detection result. The cloud server then sends the network environment detection result to the vehicle-mounted control system. At this time, the vehicle-mounted control system receives the network environment detection result and reads the network environment included in the network environment detection result. The vehicle-mounted control system determines that this network environment is a network change environment, and then determines the preset second network optimization strategy for controlling the vehicle-mounted communication device Tx to switch the communication mode as the target network optimization strategy.

[0115] In this way, the vehicle can thus screen out the network optimization strategy that matches the network environment, further improving the efficiency of network optimization operations.

[0116] Based on the first embodiment and / or the second embodiment of the present application, the third embodiment of the present application is proposed herein. In the third embodiment of the present application, for the same or similar content as in the above embodiments, reference can be made to the above introduction and will not be elaborated hereinafter. On this basis, after the above step S10, the network optimization method of the present application may further include steps B10 to B50:

[0117] Step B10: When it is detected that the module state is the idle state, determine a plurality of second network state parameters corresponding to the vehicle-mounted communication module;

[0118] Step B20: Determine a preset global network environment judgment rule, and determine state parameter thresholds corresponding to each of the plurality of second network state parameters included in the global network environment judgment rule;

[0119] Step B30: Compare the plurality of second network state parameters with their corresponding state parameter thresholds to obtain a plurality of first comparison results, and determine a target network state parameter based on the plurality of first comparison results, where the target network state parameter is a second network state parameter that reaches the state parameter threshold;

[0120] Step B40: When it is detected that the number of the target network state parameters reaches a first quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a weak network environment;

[0121] Step B50: Determine the first network optimization strategy as the target network optimization strategy, and perform a network optimization operation according to the target network optimization strategy.

[0122] It should be noted that the global network judgment rule is a dynamic threshold setting rule generated by the cloud server based on a large amount of data, and is used to dynamically adjust the parameter determination standard according to different scenarios (such as cities, suburbs, basements). In addition, the first quantity threshold is the minimum number of exceeded parameters for triggering the determination of a weak network environment (for example, if ≥3 parameters exceed the threshold, it is determined that the vehicle-mounted communication module is in a weak network environment), and is used to comprehensively evaluate the network environment.

[0123] In this embodiment, when the vehicle-mounted control system detects that the module state of the above vehicle-mounted computing module is the idle state, it further collects a plurality of second network state parameters generated by the vehicle-mounted communication module. After that, the vehicle-mounted control system obtains the preset global network judgment rule and reads the state parameter thresholds corresponding to the respective second network state parameters included in the global network judgment rule. Then, the vehicle-mounted control system compares the plurality of second network state parameters with their respective corresponding state parameter thresholds to obtain a plurality of first comparison results, and filters the target second network state parameters that reach the state parameter thresholds based on the plurality of first comparison results. Then, the vehicle-mounted control system reads the global network environment judgment rule to obtain the first quantity threshold, and when it detects that the quantity of the target second network state parameters reaches the first quantity threshold, it determines that the network environment where the vehicle-mounted communication module is located is a weak network environment. Finally, the vehicle-mounted control system determines the above first network optimization strategy as the target network optimization strategy to be executed, and controls the vehicle-mounted communication device Tx to perform a cell switching operation according to the first network optimization strategy.

[0124] Exemplarily, for example, when the vehicle-mounted control system detects that the module state of the above vehicle-mounted computing module is the idle state, it detects the vehicle-mounted communication device Tx to obtain a plurality of second network state parameters such as RSSI, delay, and packet loss rate generated by the vehicle-mounted communication device Tx. After that, the vehicle-mounted control system obtains the global network environment judgment rule issued by the cloud server and reads the global network environment judgment rule to determine the network state parameter thresholds corresponding to the respective second network state parameters. Then, the vehicle-mounted control system compares the plurality of second network state parameters with their respective corresponding network state parameter thresholds to obtain a plurality of first comparison results, and determines the target second network state parameters that reach the network state parameter thresholds according to the plurality of first comparison results. Then, the vehicle-mounted control system determines the first quantity threshold through the global network environment judgment rule, and when it detects that the quantity of the target second network state parameters reaches the first quantity threshold (for example, 3 groups), it determines that the vehicle-mounted communication device Tx is in a weak network environment. Finally, the vehicle-mounted control system determines the above first network optimization strategy as the target network optimization strategy, and controls the vehicle-mounted communication device Tx to perform a cell switching operation according to the first network optimization strategy.

[0125] It should be noted that the deep residual network configured in the cloud server can be trained based on a large amount of network status parameters and network environments uploaded by vehicle terminals to obtain an initial network environment judgment rule. Then, the cloud server uses knowledge distillation technology to process the initial network environment judgment rule to generate a lightweight global network environment judgment rule, and then sends the global network environment judgment rule to the vehicle. It can be understood that the global network environment judgment rule generated by processing with knowledge distillation technology has the characteristic of a small volume, which ensures that the bandwidth is not under great pressure during the process of sending the global network environment judgment rule, and further improves the efficiency of network optimization operations.

[0126] In this way, the vehicle can more accurately judge the network environment where the vehicle is located based on the collected network status data, so as to screen out network optimization strategies that match the network environment, and further improve the efficiency of network optimization operations.

[0127] Based on the embodiments of the present application, the fourth embodiment of the present application is proposed here. In the fourth embodiment of the present application, for the same or similar content as the above embodiments, reference can be made to the above introduction and will not be repeated hereinafter. On this basis, after the above step B30, the network optimization method of the present application may further include steps B60 to B70:

[0128] Step B60: When it is detected that the number of the target network status parameters reaches a second quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a network change environment, where the second quantity threshold is greater than the first quantity threshold;

[0129] Step B70: Determine the second network optimization strategy as the target network optimization strategy, and perform network optimization operations according to the target network optimization strategy.

[0130] It should be noted that the second quantity threshold is the minimum number of exceeded parameters required to determine a network change environment, and its value is greater than the above-mentioned first quantity threshold (for example, if ≥4 parameters exceed the threshold, it is determined that the vehicle-mounted communication module is in a network change environment).

[0131] In this embodiment, after the vehicle-mounted control system screens out the above-mentioned target second network status parameters, it further reads the above-mentioned global network judgment rule to determine the second quantity threshold greater than the above-mentioned first quantity threshold. When the vehicle-mounted control system detects that the number of the target second network status parameters reaches the second quantity threshold, it determines that the network environment where the vehicle-mounted communication module is located is a network change environment. Finally, the vehicle-mounted control system determines the above-mentioned second network optimization strategy as the target network optimization strategy to be executed, and controls the vehicle-mounted communication device Tx to perform a connection mode switching operation according to the second network optimization strategy.

[0132] Exemplarily, for example, after the vehicle control system filters out the above-mentioned target second network status parameters, it further reads the above-mentioned global network judgment rule to determine a second quantity threshold greater than the above-mentioned first quantity threshold (for example, 4 groups). When the vehicle control system detects that the quantity of the target second network status parameters reaches the second quantity threshold, it determines that the network environment where the vehicle communication device Tx is located is a network change environment (for example, the vehicle enters the basement scenario from the normal driving scenario). Finally, the vehicle control system determines the above-mentioned second network optimization strategy as the target network optimization strategy, and controls the vehicle communication device Tx to send a mode switching request to the base station according to the second network optimization strategy, so as to turn off the 5G radio frequency module configured in the vehicle, activate the 4G module and connect it to the specified frequency band, so that the connection mode of the vehicle is switched from the 5G mode to the 4G low-frequency band mode.

[0133] In this way, the vehicle can more accurately judge the network environment where the vehicle is located based on the collected network status data, so as to filter out the network optimization strategy matching the network environment, and further improve the efficiency of network optimization operations.

[0134] This application also provides a network optimization device. Please refer to Figure 4 , the network optimization device is applied to a vehicle, the vehicle includes a vehicle communication module and a vehicle computing module, and the device includes:

[0135] User behavior detection module 10: Detect the module status of the vehicle computing module, where the module status is a usage status or an idle status;

[0136] Lag phenomenon detection module 20: When detecting that the module status is the usage status, read the interaction log of the vehicle computing module;

[0137] Network optimization execution module 30: When it is determined according to the interaction log that there is a network lag phenomenon in the vehicle computing module, perform network optimization operations according to the target network optimization strategy, where the target network optimization strategy is the first network optimization strategy or the second network optimization strategy.

[0138] In a feasible implementation manner, the above-mentioned lag phenomenon detection module 20 is further used for:

[0139] Determine the number of screen clicks and the screen click time included in the interaction log, and determine the user operation frequency according to the number of screen clicks and the screen click time;

[0140] When it is detected that the user operation frequency reaches a preset click frequency threshold, it is determined that there is a network lag phenomenon in the vehicle computing module, and the step of performing network optimization operations according to the target network optimization strategy is executed.

[0141] In a feasible implementation manner, the vehicle is communicatively connected to the cloud server, and the lag phenomenon detection module 20 is further configured to:

[0142] When it is detected that the module state is the usage state, determine a plurality of first network state parameters corresponding to the in-vehicle computing module;

[0143] Send the plurality of first network state parameters to the cloud server, and receive the network environment detection result sent by the cloud server, where the network environment detection result is generated by a lightweight time series prediction model deployed inside the cloud server based on the plurality of first network state parameters;

[0144] Determine a target network optimization strategy according to the network environment detection result, and perform a network optimization operation according to the target network optimization strategy.

[0145] In a feasible implementation manner, the lag phenomenon detection module 20 is further configured to:

[0146] When it is detected that the network environment detection result indicates that the in-vehicle computing module is in a weak network environment, determine a preset first network optimization strategy as the target network optimization strategy, where the first network optimization strategy is a network optimization strategy for switching the communication cell corresponding to the vehicle;

[0147] When it is detected that the network environment detection result indicates that the in-vehicle computing module is in a network change environment, determine a preset second network optimization strategy as the target network optimization strategy, where the second network optimization strategy is a network optimization strategy for switching the network connection mode corresponding to the vehicle.

[0148] In a feasible implementation manner, the vehicle is communicatively connected to the target mobile terminal, and the network optimization execution module 30 is further configured to:

[0149] When it is detected that the target network optimization strategy is the first network optimization strategy, receive the cell feature database sent by the target mobile terminal;

[0150] Query the cell feature database based on the plurality of first network state parameters to determine a target cell, and instruct the vehicle to connect to the target cell through the vehicle-mounted communication module.

[0151] In a feasible implementation manner, the network optimization execution module 30 is further configured to:

[0152] When it is detected that the target network optimization strategy is the second network optimization strategy, determine a preset target network connection mode;

[0153] Switch the current network connection mode corresponding to the vehicle to the target network connection mode through the vehicle-mounted communication module.

[0154] In a feasible implementation manner, the above-mentioned lag phenomenon detection module 20 is further configured to:

[0155] When it is detected that the module state is the idle state, determine a plurality of second network state parameters corresponding to the vehicle-mounted communication module;

[0156] Determine a preset global network environment judgment rule, and determine state parameter thresholds corresponding to each of the plurality of second network state parameters included in the global network environment judgment rule;

[0157] Compare the plurality of second network state parameters with their respective corresponding state parameter thresholds to obtain a plurality of first comparison results, and determine a target network state parameter based on the plurality of first comparison results, where the target network state parameter is a second network state parameter that reaches the state parameter threshold;

[0158] When it is detected that the number of the target network state parameters reaches a first quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a weak network environment;

[0159] Determine a first network optimization strategy as the target network optimization strategy, and perform a network optimization operation according to the target network optimization strategy.

[0160] In a feasible implementation manner, the above-mentioned lag phenomenon detection module 20 is further configured to:

[0161] When it is detected that the number of the target network state parameters reaches a second quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a network change environment, where the second quantity threshold is greater than the first quantity threshold;

[0162] Determine a second network optimization strategy as the target network optimization strategy, and perform a network optimization operation according to the target network optimization strategy.

[0163] The network optimization device provided by the present application adopts the network optimization method in the above-mentioned embodiment, and can solve the technical problem of low efficiency of network optimization operations in the related art. Compared with the prior art, the beneficial effects of the network optimization device provided by the present application are the same as those of the network optimization method provided by the above-mentioned embodiment, and other technical features in the network optimization device are the same as those disclosed in the method of the above-mentioned embodiment, and will not be elaborated here.

[0164] The present application provides a vehicle, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the network optimization method in the first embodiment above.

[0165] Reference is made below to Figure 5 , which shows a schematic structural diagram of a vehicle suitable for implementing the embodiments of the present application. The vehicle in the embodiments of the present application may include, but is not limited to, a vehicle internally configured with an in-vehicle computing module and an in-vehicle communication module, or a mobile terminal, a data storage control terminal, a PC, etc. connected to an electronic control unit supporting the vehicle. Figure 5 The vehicle shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0166] As Figure 5 shown, the vehicle may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for vehicle operation are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the vehicle to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a vehicle with various systems, it should be understood that it is not required to implement or include all the systems shown. More or fewer systems may be implemented or included alternatively.

[0167] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0168] The vehicle provided by the present application adopts the network optimization method in the above-mentioned embodiment, and can solve the technical problem of low efficiency of network optimization operations in the related art. Compared with the prior art, the beneficial effects of the vehicle provided by the present application are the same as those of the network optimization method provided by the above-mentioned embodiment, and other technical features in the vehicle are the same as those disclosed in the method of the previous embodiment, which will not be elaborated here.

[0169] It should be understood that each part disclosed in the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0170] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0171] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the network optimization method in the above-mentioned embodiment.

[0172] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with 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 of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0173] The above computer-readable storage medium can be included in a vehicle; it can also exist separately and not be assembled into the vehicle.

[0174] The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by a vehicle, the vehicle is caused to: detect the module state of the in-vehicle computing module, where the module state is a usage state or an idle state; in the case where it is detected that the module state is the usage state, read the interaction log of the in-vehicle computing module; in the case where it is determined according to the interaction log that there is a network lag phenomenon in the in-vehicle computing module, perform a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy.

[0175] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0177] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0178] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned network optimization method, and can solve the technical problem of low efficiency of network optimization operations in related technologies. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the network optimization method provided in the above embodiments, and will not be elaborated here.

[0179] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the network optimization method as described above.

[0180] The computer program product provided by the present application can solve the technical problem of low efficiency of network optimization operations in the related art. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the network optimization method provided by the above embodiments, and will not be elaborated here.

[0181] The foregoing are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A network optimization method, characterized in that, The described network optimization method is applied to a vehicle, which includes an in-vehicle communication module and an in-vehicle computing module. The vehicle is communicatively connected to a cloud server. The network optimization method includes: Detect the module status of the in-vehicle computing module, where the module status is a usage status or an idle status; When it is detected that the module status is the usage status, read the interaction log of the in-vehicle computing module; When it is determined according to the interaction log that there is a network lag phenomenon in the in-vehicle computing module, perform a network optimization operation according to a target network optimization strategy, where the target network optimization strategy is a first network optimization strategy or a second network optimization strategy; After detecting the module status of the in-vehicle computing module, the method further includes: When it is detected that the module status is the usage status, determine a plurality of first network status parameters corresponding to the in-vehicle computing module; Send the plurality of first network status parameters to the cloud server and receive the network environment detection result sent by the cloud server, where the network environment detection result is generated by a lightweight time series prediction model deployed inside the cloud server based on the plurality of first network status parameters; Determine a target network optimization strategy according to the network environment detection result and perform a network optimization operation according to the target network optimization strategy.

2. The network optimization method according to claim 1, wherein After the step of reading the interaction log of the in-vehicle computing module, the method further includes: Determine the number of screen clicks and the screen click time included in the interaction log, and determine the user operation frequency according to the number of screen clicks and the screen click time; When it is detected that the user operation frequency reaches a preset click frequency threshold, determine that there is a network lag phenomenon in the in-vehicle computing module and perform the step of performing a network optimization operation according to a target network optimization strategy.

3. The network optimization method according to claim 1, wherein The step of determining a target network optimization strategy according to the network environment detection result includes: When it is detected that the network environment detection result is that the in-vehicle computing module is in a weak network environment, determine a preset first network optimization strategy as the target network optimization strategy, where the first network optimization strategy is a network optimization strategy for switching the communication cell corresponding to the vehicle; When it is detected that the network environment detection result is that the in-vehicle computing module is in a network change environment, determine a preset second network optimization strategy as the target network optimization strategy, where the second network optimization strategy is a network optimization strategy for switching the network connection mode corresponding to the vehicle.

4. The network optimization method according to claim 1, wherein The vehicle is communicatively connected to a target mobile terminal. The step of performing a network optimization operation according to a target network optimization strategy includes: When it is detected that the target network optimization strategy is the first network optimization strategy, receive the cell feature database sent by the target mobile terminal; Query the cell feature database based on the plurality of first network status parameters to determine a target cell, and instruct the vehicle to connect to the target cell through the in-vehicle communication module.

5. The network optimization method according to claim 4, characterized in that, The step of performing a network optimization operation according to a target network optimization strategy further includes: When it is detected that the target network optimization policy is the second network optimization policy, determine the preset target network connection mode; Switch the current network connection mode corresponding to the vehicle to the target network connection mode through the vehicle-mounted communication module.

6. The network optimization method according to any one of claims 1 to 5, characterized in that, After the step of detecting the module state of the vehicle-mounted computing module, the method further includes: When it is detected that the module state is the idle state, determine a plurality of second network state parameters corresponding to the vehicle-mounted communication module; Determine the preset global network environment judgment rule, and determine the state parameter thresholds corresponding to each of the plurality of second network state parameters included in the global network environment judgment rule; Compare the plurality of second network state parameters with their respective corresponding state parameter thresholds to obtain a plurality of first comparison results, and determine the target network state parameter based on the plurality of first comparison results, where the target network state parameter is the second network state parameter that reaches the state parameter threshold; When it is detected that the number of the target network state parameters reaches the first quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a weak network environment; Determine the first network optimization policy as the target network optimization policy, and perform network optimization operations according to the target network optimization policy.

7. The network optimization method according to claim 6, wherein After the step of determining the target network state parameter based on the plurality of first comparison results, the method further includes: When it is detected that the number of the target network state parameters reaches the second quantity threshold, determine that the network environment corresponding to the vehicle-mounted communication module is a network change environment, where the second quantity threshold is greater than the first quantity threshold; Determine the second network optimization policy as the target network optimization policy, and perform network optimization operations according to the target network optimization policy.

8. A vehicle, characterized in that, The vehicle includes: a vehicle-mounted communication module, a vehicle-mounted computing module, a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the network optimization method according to any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the network optimization method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by the processor, the steps of the network optimization method according to any one of claims 1 to 7 are implemented.

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