Method for pre-storing network data in vehicle before entering signal blind area and related equipment

Abstract

The invention discloses a method for pre-storing network data in a vehicle before entering a signal blind area and related equipment, and relates to the technical field of vehicle-mounted communication, the method for pre-storing the network data in the vehicle before entering the signal blind area comprises the steps of obtaining a vehicle navigation route, and determining a corresponding base station signal distribution map and navigation route historical signal data; according to the base station signal distribution map and the navigation route historical signal data, each predicted signal blind area in the vehicle navigation route is predicted through a preset blind area prediction model; and if it is monitored that the vehicle is about to enter the predicted signal blind area, obtaining current driving information of the vehicle and predicting duration of the signal blind area, and pre-storing network data in the vehicle through a preset pre-storage rule according to the duration of the signal blind area. Before the vehicle approaches the signal blind area, the signal blind area distribution is predicted according to the vehicle navigation route, and the network data is pre-stored according to the signal blind area distribution, so that the reliability of the network data in the blind area environment is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle communication technologies, and particularly to a method for pre-storing in-vehicle network data before entering a signal blind area and related devices. Background Art

[0002] With the development of vehicle-to-everything (V2X) technology, in-vehicle wireless networks have become the core of intelligent transportation systems, used to achieve data interaction between vehicles and the cloud, infrastructure, and other vehicles. However, existing technologies still have some deficiencies. For example, when a vehicle enters a signal blind area, traditional technologies rely on offline map caches and cannot update dynamic traffic information such as accidents and congestion in real time, which causes navigation systems to be unable to provide accurate route suggestions in these blind areas. In addition, when the network is interrupted, users' applications often need to be manually reconnected, lacking the ability to maintain an "online" state during network outages, resulting in a poor user experience. Finally, existing resource allocation strategies are usually fixed and fail to be flexibly adjusted according to the blind area duration and the specific needs of users, leading to poor reliability of in-vehicle network data in vehicles in the blind area environment.

[0003] Therefore, how to improve the reliability of in-vehicle wireless networks in the blind area environment is an urgent problem to be solved. Summary of the Invention

[0004] The main purpose of this application is to provide a method for pre-storing in-vehicle network data before entering a signal blind area and related devices, aiming to solve the technical problem of poor reliability of in-vehicle wireless networks in the blind area environment.

[0005] To achieve the above object, this application proposes a method for pre-storing in-vehicle network data before entering a signal blind area, and the method includes: Obtain the vehicle navigation route, and determine the corresponding base station signal distribution map and navigation route historical signal data; According to the base station signal distribution map and the navigation route historical signal data, through a preset blind area prediction model, predict each predicted signal blind area in the vehicle navigation route; If it is monitored that the vehicle is about to enter the predicted signal blind area, obtain the current driving information of the vehicle and predict the signal blind area duration, and according to the signal blind area duration, pre-store the in-vehicle network data through a preset pre-storing rule.

[0006] In one embodiment, the method for predicting each predicted signal blind area in the vehicle navigation route according to the base station signal distribution map and the navigation route historical signal data through a preset blind area prediction model includes: Perform time series feature extraction on the navigation historical signal data through a preset long short-term memory neural network model to obtain historical signal time series features; Perform spatial feature extraction on the base station signal distribution map through a preset convolutional neural network model to obtain the current spatial vector features; Obtain real-time weather information and real-time landform information of the vehicle navigation route; Input the real-time weather information, the real-time landform information, the historical signal time series features, and the current spatial vector features into a blind area prediction model to predict each predicted signal blind area on the vehicle navigation route.

[0007] In one embodiment, before the step of inputting the real-time weather information, the real-time landform information, the historical signal time series features, and the current spatial vector features into a blind area prediction model to predict each predicted signal blind area on the vehicle navigation route, it includes: Obtain historical signal time series samples, base station spatial vector samples, and signal blind area distribution labels; Project the historical signal time series samples and the base station spatial vector samples onto the same dimension, and generate a spatio-temporal weighted feature representation according to a preset attention matrix model; Obtain severe weather impact factors and severe landform impact factors; Generate a severe condition weight according to the severe weather impact factors and the severe landform impact factors; Input the spatio-temporal weighted feature representation and the severe condition weight into a signal blind area prediction model to be trained, predict the signal blind area, and obtain a signal blind area prediction result; Compare the signal blind area prediction result and the signal blind area distribution label to obtain a prediction error value; If the prediction error value does not meet the preset threshold, adjust the hyperparameters of the attention matrix model, and return to the step of projecting the historical signal time series samples and the base station spatial vector samples onto the same dimension and generating a spatio-temporal weighted feature representation according to a preset attention matrix model until the prediction error value meets the preset threshold, and output the predicted signal blind area.

[0008] In one embodiment, the step of obtaining the current driving information of the vehicle, predicting the signal blind area duration, and pre-storing the in-vehicle network data according to the signal blind area duration through a preset pre-storage rule includes: Obtain the list of background active applications in the vehicle and the local remaining storage space; Determine the network data pre-storage priority of the background active applications in the vehicle according to a preset application pre-storage priority list and the background active application list; Allocate the multimedia cache data space for each background active application according to the blind area duration, the local remaining storage space, and the network data pre-storage priority; Obtain and pre-store network data into the multimedia cache data space.

[0009] In one embodiment, the step of obtaining and pre-storing network data into the multimedia cache data space includes: Based on the blind area duration, pre-store the real-time traffic event information of the vehicle navigation route; When entering the signal blind area, convert the real-time traffic event information into traffic event markers supported by the offline map; Add the traffic event markers to the offline map; Obtain the acceleration direction, vehicle speed, and driving direction of the vehicle's travel to obtain the blind area vehicle travel information; Combine the vehicle navigation route and the blind area vehicle travel information to update the vehicle position in the offline map in real time; When it is detected that the vehicle position reaches the traffic event marker, execute a preset reminder scheme.

[0010] In one embodiment, after the step of pre-storing the in-vehicle network data according to the signal blind area duration through a preset pre-storage rule, it includes: Determine the active target anti-disconnection applications according to the user-predefined anti-disconnection application list; Send simulated first protocol response data to the target anti-disconnection applications through a pseudo handshake mechanism; Record the protocol request data of the target anti-disconnection applications; When the network signal strength recovers to a preset threshold, upload the protocol request data and obtain the second protocol response data responded by the server to update the first protocol response data.

[0011] In addition, to achieve the above object, the present application also proposes an in-vehicle network data pre-storage device before entering the signal blind area. The in-vehicle network data pre-storage device before entering the signal blind area includes: An acquisition module, configured to acquire a vehicle navigation route and determine a corresponding base station signal distribution map and navigation route historical signal data; A prediction module, configured to predict each predicted signal blind area in the vehicle navigation route through a preset blind area prediction model according to the base station signal distribution map and the navigation route historical signal data; A pre-storage module, which is configured to obtain the current driving information of the vehicle and predict the duration of the signal blind zone if it is detected that the vehicle is about to enter the predicted signal blind zone, and pre-store the in-vehicle network data according to the duration of the signal blind zone through a preset pre-storage rule.

[0012] In addition, to achieve the above object, the present application further provides a device for pre-storing in-vehicle network data before entering a signal blind zone, the device including: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the method for pre-storing in-vehicle network data before entering a signal blind zone as described above.

[0013] In addition, to achieve the above object, the present application further provides a storage medium, the storage medium being a computer-readable storage medium, and a computer program being stored on the storage medium, the computer program implementing the steps of the method for pre-storing in-vehicle network data before entering a signal blind zone as described above when executed by a processor.

[0014] In addition, to achieve the above object, the present application further provides a computer program product, the computer program product including a computer program, the computer program implementing the steps of the method for pre-storing in-vehicle network data before entering a signal blind zone as described above when executed by a processor.

[0015] One or more technical solutions proposed by the present application have at least the following technical effects: In the related art, when the in-vehicle navigation system enters a signal blind area, it only relies on the pre-downloaded offline map to provide static route navigation; when the vehicle exits the signal blind area, the navigation application needs to be manually refreshed to reconnect to the network due to network disconnection, and the multimedia player is interrupted due to insufficient cache and needs to wait for the network to recover and then reload, resulting in frequent service interruptions and redundant operations during driving in the blind area environment, and the reliability of the in-vehicle wireless network is poor. In contrast, in this application, by obtaining the vehicle navigation route, the corresponding base station signal distribution map and the navigation route historical signal data are determined; according to the base station signal distribution map and the navigation route historical signal data, through a preset blind area prediction model, each predicted signal blind area in the vehicle navigation route is predicted; if it is monitored that the vehicle is about to enter the predicted signal blind area, the current driving information of the vehicle is obtained and the signal blind area duration is predicted, and according to the signal blind area duration, the in-vehicle network data is pre-stored through a preset pre-storage rule. This application mainly includes three core components: obtaining the vehicle navigation route, predicting the blind area distribution, and pre-storing the in-vehicle wireless network data according to the blind area duration. The acquisition of the vehicle navigation route provides the basic information of the driving path for the entire system; the combination of the base station signal distribution map and the navigation route historical signal data helps to accurately predict the blind area distribution; predicting the duration of the blind area that the vehicle is about to enter; pre-storing the in-vehicle wireless network data required in the blind area according to the preset pre-storage rule to ensure that relevant application services can still be provided when the network signal is poor. The working principle is that before the vehicle approaches the signal blind area, by analyzing the real-time and historical signal data, the length of the blind area is predicted, and the necessary network data is pre-loaded accordingly, so that accurate navigation information and other application services can be immediately provided after entering the blind area, avoiding frequent operations and lagging information updates caused by network interruption, and improving the reliability of network data in the blind area environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or 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 be obtained according to these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart provided for the first embodiment of the method for pre-storing in-vehicle network data before the vehicle enters the signal blind area according to the present application; Figure 2 It is a schematic flowchart provided for the second embodiment of the method for pre-storing in-vehicle network data before the vehicle enters the signal blind area according to the present application; Figure 3 This is a schematic flowchart provided for the third embodiment of the method for pre-storing in-vehicle network data before the vehicle enters a signal blind area in the present application; Figure 4 This is a schematic module structure diagram of the in-vehicle network data pre-storing device before the vehicle enters a signal blind area in the embodiment of the present application; Figure 5 This is a schematic device structure diagram of the hardware operating environment involved in the method for pre-storing in-vehicle network data before the vehicle enters a signal blind area in the embodiment of the present application.

[0019] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0020] 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.

[0021] For a better understanding of the technical solutions of the present application, the following will be described in detail with reference to the accompanying drawings of the specification and the specific implementation manners.

[0022] The main solution of the embodiment of the present application is as follows: Obtain the vehicle navigation route, and determine the corresponding base station signal distribution map and navigation route historical signal data; According to the base station signal distribution map and the navigation route historical signal data, through a preset blind area prediction model, predict each predicted signal blind area in the vehicle navigation route; If it is monitored that the vehicle is about to enter the predicted signal blind area, obtain the current driving information of the vehicle and predict the signal blind area duration, and according to the signal blind area duration, pre-store the in-vehicle network data through a preset pre-storing rule.

[0023] In this embodiment, the present application takes the in-vehicle network data pre-storing device before the vehicle enters a signal blind area as the execution subject. For the convenience of description, hereinafter it will be simply referred to as "device" for specific description.

[0024] In the prior art, when the vehicle enters a signal blind area, the traditional technology relies on offline map caching and cannot update dynamic traffic information such as accidents and congestion in real time, which causes the navigation system to be unable to provide accurate route suggestions in these blind areas. In addition, when the network is interrupted, the user's application often needs to be manually reconnected, lacking the ability to maintain an "online" state when the network is disconnected, resulting in a poor user experience. Finally, the existing resource allocation strategies are usually fixed and cannot be flexibly adjusted according to the blind area duration and the specific needs of users, resulting in poor reliability of the in-vehicle wireless network in the blind area environment.

[0025] This application provides a solution. This application obtains the vehicle navigation route, determines the corresponding base station signal distribution map and navigation route historical signal data; according to the base station signal distribution map and the navigation route historical signal data, through a preset blind area prediction model, predicts each predicted signal blind area in the vehicle navigation route; if it is monitored that the vehicle is about to enter the predicted signal blind area, obtains the current driving information of the vehicle and predicts the duration of the signal blind area, and according to the duration of the signal blind area, prestores the in-vehicle network data through a preset prestorage rule. This application mainly includes three core components: obtaining the vehicle navigation route, predicting the blind area distribution, and prestoring the in-vehicle wireless network data according to the duration of the blind area. The acquisition of the vehicle navigation route provides the basic information of the driving path for the entire system; the combination of the base station signal distribution map and the navigation route historical signal data helps to accurately predict the blind area distribution; predicts the duration of the blind area that the vehicle is about to enter; according to the preset prestorage rule, prestores the in-vehicle wireless network data required in the blind area to ensure that relevant application services can still be provided when the network signal is poor. The working principle is that before the vehicle approaches the signal blind area, it analyzes the real-time and historical signal data to predict the length of the blind area, and accordingly preloads the necessary network data in advance, so that accurate navigation information and other application services can be immediately provided after entering the blind area, avoiding frequent operations and lag in information updates caused by network interruption, and improving the reliability of network data in the blind area environment.

[0026] Based on this, an embodiment of this application provides a method for prestoring in-vehicle network data before entering a signal blind area, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the method for prestoring in-vehicle network data before entering a signal blind area of this application.

[0027] In this embodiment, the method for prestoring in-vehicle network data before entering a signal blind area includes steps S10 to S30: Step S10, obtain the vehicle navigation route, and determine the corresponding base station signal distribution map and navigation route historical signal data; It should be noted that the vehicle navigation route is the driving path of the vehicle from the starting point to the ending point, usually provided by the in-vehicle navigation system, and includes the geographical coordinates and driving directions of the route. The base station signal distribution map is a base station signal strength distribution map stored in the cloud, used to describe the signal coverage of different regions. The navigation route historical signal data is the signal strength data of the vehicle on the navigation route during the historical driving process, used to assist in predicting the blind area.

[0028] It can be understood that, referring to Figure 2 , assuming a car plans to pass through a mountain road section, due to the base station signal not covering the entire mountain road section, there is a signal blind area in the mountain road section.

[0029] The device extracts the driving route of the vehicle from the in-vehicle navigation system, including the starting point, the ending point, the path line, and the key nodes along the way (such as tunnels and mountain roads).

[0030] The device obtains the signal distribution map of the base stations along the driving route from the cloud according to the driving route of the vehicle.

[0031] It can be understood that the device marks the areas with weak signal coverage according to the signal distribution map, which can be used for subsequent signal blind area prediction.

[0032] The device calls the vehicle historical driving data from the cloud or the vehicle historical driving database stored locally. The device extracts the signal strength records when the vehicle passes through the same section or similar sections of the road, which can provide data support for subsequent verification of the position and length of the signal blind area.

[0033] It can be understood that by obtaining the vehicle navigation route, determining the corresponding base station signal distribution map and the historical signal data of the navigation route, it can provide data support for subsequent prediction of signal blind areas and improve the reliability of the prediction results.

[0034] Step S20, according to the base station signal distribution map and the historical signal data of the navigation route, through a preset blind area prediction model, predict each predicted signal blind area in the vehicle navigation route; It should be noted that the blind area prediction model is a prediction model including but not limited to machine learning algorithms, which is used to analyze the base station signal distribution map and the historical signal data of the navigation route and predict the blind area positions on the navigation route.

[0035] It can be understood that assume a car needs to pass through a mountain road section and a tunnel during driving. The signal coverage of the mountain road section and the tunnel is poor, and there are multiple signal blind areas. The in-vehicle network data pre-storage device before entering the signal blind area ensures that the vehicle can still provide reliable services through the following steps.

[0036] The device extracts the driving route information of the vehicle from the in-vehicle navigation system, including the starting point, the ending point, the path line, the key nodes passed through (such as mountain road sections and tunnels), the expected driving speed, etc.

[0037] The device inputs the vehicle planned route information into a preset blind area prediction model. This model can combine the base station signal distribution map and the historical signal data to predict the blind area positions on the navigation route. For example, in the historical records, there have been signal disconnection records in the mountain road section in the mountainside part of the navigation route, then the corresponding signal disconnection points are predicted as candidate signal blind area points, and according to the base station distribution corresponding to the navigation line, the highly credible area of the signal blind area is calculated through the model to prepare for the subsequent preloading step.

[0038] In a feasible implementation manner, the method for predicting each predicted signal blind area in the vehicle navigation route according to the base station signal distribution map and the navigation route historical signal data through a preset blind area prediction model includes: Extract time series features from the navigation historical signal data through a preset long short-term memory neural network model to obtain historical signal time series features; Extract spatial features from the base station signal distribution map through a preset convolutional neural network model to obtain the current spatial vector features; Obtain real-time weather information and the real-time landform information of the vehicle navigation route; Input the real-time weather information, the real-time landform information, the historical signal time series features, and the current spatial vector features into the blind area prediction model to predict each predicted signal blind area on the vehicle navigation route.

[0039] It should be noted that the base station signal distribution map includes, but is not limited to, the base station signal strength distribution map stored in the cloud, which is used to describe the signal coverage of different regions and also contains real-time updated signal strength data.

[0040] The vehicle navigation route historical signal data includes, but is not limited to, the signal strength data recorded during the historical driving of the vehicle, and also contains information such as the position and duration of the signal blind area.

[0041] Exemplarily, a dual-branch network is constructed to extract time and spatial features respectively: Time branch: Use LSTM to process the historical signal sequence to capture time series patterns such as traffic tides and base station loads.

[0042] Spatial branch: Adopt the fusion of CNN and graph neural network (GNN) for the base station distribution and terrain three-dimensional data to model the physical attenuation of signal propagation.

[0043] Cross-attention layer: Use the attention matrix to take the time feature as the query (Query) and the spatial feature as the key-value (Key-Value), and dynamically focus on the most relevant spatial area at the current moment. For example, during heavy rain, focus on the status of nearby base stations.

[0044] Add parallel sub-networks: Weather classification head: Predict the current weather level according to the spatial features and optimize the feature encoding through backpropagation.

[0045] Landform recognition head: Analyze the terrain risk through the elevation map and output the probability of dangerous landforms such as landslides or canyons.

[0046] The loss functions of these two sub-networks are used as auxiliary tasks to enhance the sensitivity of the model to harsh environments.

[0047] It is understandable that the device predicts the distribution of blind spots on the navigation route by combining real-time and historical spatio-temporal data.

[0048] In a feasible implementation manner, before the step of inputting the real-time weather information, the real-time landform information, the historical signal time-series characteristics, and the current spatial vector characteristics into the blind spot prediction model to predict each predicted signal blind spot on the vehicle navigation route, the following steps are included: Obtain historical signal time-series samples, base station spatial vector samples, and signal blind spot distribution labels; Project the historical signal time-series samples and the base station spatial vector samples onto the same dimension, and generate a spatio-temporal weighted feature representation according to a preset attention matrix model; Obtain the adverse weather impact factor and the adverse landform impact factor; Generate an adverse condition weight according to the adverse weather impact factor and the adverse landform impact factor; Input the spatio-temporal weighted feature representation and the adverse condition weight into the signal blind spot prediction model to be trained, predict the signal blind spot, and obtain a signal blind spot prediction result; Compare the signal blind spot prediction result with the signal blind spot distribution label to obtain a prediction error value; If the prediction error value does not meet the preset threshold, adjust the hyperparameters of the attention matrix model, and return to the step of projecting the historical signal time-series samples and the base station spatial vector samples onto the same dimension and generating a spatio-temporal weighted feature representation according to the preset attention matrix model until the prediction error value meets the preset threshold, and output the predicted signal blind spot.

[0049] It should be noted that the historical signal time-series samples refer to the network signal strength data sequences recorded in chronological order during the vehicle's past driving. These samples reflect the signal reception conditions of the vehicle at different time points and different positions. By analyzing the historical signal time-series samples, the law of signal strength change over time can be extracted, providing feature support in the time dimension for predicting future signal blind spots.

[0050] The base station spatial vector samples refer to the base station location information related to the vehicle navigation route, represented in vector form. These vectors usually contain the geographical coordinates of the base station (such as longitude and latitude), the base station coverage range, etc. The base station spatial vector samples are used to describe the distribution of base stations on the vehicle driving path. Through spatial feature extraction, the signal coverage area and potential signal blind spot positions can be clarified.

[0051] The signal blind spot distribution label refers to the location information of signal blind spots on the pre-marked vehicle navigation route. These labels are usually obtained based on historical data or field tests and are used to indicate areas with insufficient signal coverage. The signal blind spot distribution label serves as the "answer" for training data to supervise the learning process and help the model learn how to accurately identify signal blind spots.

[0052] The spatio-temporal weighted feature representation refers to the feature vector obtained by weighted fusion of time series features (historical signal time series features) and spatial features (base station spatial vector features) through an attention mechanism. This representation method can highlight the more important spatio-temporal features in signal blind spot prediction. By generating the spatio-temporal weighted feature representation, the model can better comprehensively consider the influence of time and space factors on signal coverage, thereby improving the accuracy of signal blind spot prediction.

[0053] The severe weather impact factor refers to the numerical value that quantitatively describes the degree of influence of severe weather (such as heavy rain, heavy snow, thick fog, etc.) on signal transmission. This factor is usually calculated based on factors such as weather type and intensity. Severe weather affects signal propagation and reception. By introducing the severe weather impact factor, the model can more accurately predict the distribution of signal blind spots under severe weather conditions.

[0054] The complex terrain impact factor refers to the numerical value that quantitatively describes the degree of influence of complex terrain (such as valleys, tunnels, high-rise dense areas, etc.) on signal transmission. This factor is usually calculated based on factors such as terrain type and terrain height difference. Complex terrain can block or weaken signals. By introducing the complex terrain impact factor, the model can more accurately predict the distribution of signal blind spots under complex terrain conditions.

[0055] The severe condition weight refers to the weight value used to weightedly adjust the input features of the signal blind spot prediction model after comprehensively considering the severe weather impact factor and the complex terrain impact factor. This weight is used to highlight the uncertainty of signal coverage under severe conditions. The severe condition weight enables the model to pay more attention to signal changes under severe weather and complex terrain conditions when predicting signal blind spots, thereby improving the robustness of the prediction.

[0056] The prediction error value refers to the degree of difference between the signal blind spot prediction result and the actual signal blind spot distribution label. It is usually measured by calculating a certain distance (such as Euclidean distance, cross-entropy, etc.) between the predicted value and the true value. The prediction error value is used to evaluate the performance of the signal blind spot prediction model. By comparing the prediction error value with a preset threshold, it can be determined whether the model has achieved the expected prediction accuracy.

[0057] The attention matrix model is a neural network model based on the attention mechanism, which is used to perform weighted processing on the input feature data. It dynamically assigns the importance weights of different features by calculating the correlation between the input features. The attention matrix model can automatically learn which features are more important for signal blind spot prediction, thereby generating a more representative time-space weighted feature representation and improving the prediction performance of the model.

[0058] The preset threshold refers to the set value used to determine whether the prediction error value meets the requirements during the signal blind spot prediction process. The threshold is usually determined based on the actual application scenario and the prediction accuracy requirements. The preset threshold is used to control the training process of the signal blind spot prediction model. When the prediction error value does not meet the preset threshold, the model will continue to adjust the parameters until the error value meets the requirements, thereby ensuring the prediction accuracy of the model.

[0059] For example, a multi-source data input system is first constructed to integrate historical vehicle navigation signal time series data, base station three-dimensional topology information and real-time meteorological and geomorphological environmental data. For the time dimension features, a bidirectional memory network is used to extract the time series evolution law of signal strength, and the attention mechanism is combined to focus on key time nodes; for the spatial dimension features, the collaborative coverage relationship between base stations is modeled through a graph neural network, and a lightweight electromagnetic propagation model is constructed by integrating three-dimensional terrain data to calculate the impact of terrain occlusion on signal attenuation. An innovative time-space cross-attention mechanism is designed to enable time features to dynamically guide the focus area of ​​spatial features, and automatically enhance the attention weight of neighboring base stations under severe weather conditions. A harsh environment perception module is introduced, and the fusion ratio of time-space features is dynamically adjusted by parallel training of weather classification and landform recognition auxiliary tasks, so as to improve the signal attenuation prediction sensitivity of terrain mutation areas under heavy rain scenes. A phased training strategy is adopted, first pre-training the basic feature extractor through the signal reconstruction task, then combining the multi-task loss function to jointly optimize the main prediction network and auxiliary classifier, and finally enhancing the model robustness through adversarial sample generation. The model outputs a high-precision blind spot probability distribution map, which triggers graded warnings in combination with real-time positioning information and supports low-latency reasoning under edge computing architecture. This solution innovatively combines physical propagation models with deep learning to achieve reliable prediction and adaptive optimization of navigation blind spots in complex environments.

[0060] It should be noted that the device dynamically optimizes the prediction model by adjusting the weight of severe weather and severe terrain in real time to ensure the accuracy of the prediction results. For example, when driving on rainy days, the device will increase the weight of severe weather to improve the prediction accuracy of signal attenuation. The device continuously corrects the model parameters by comparing the prediction results with the actual labels to ensure that the prediction error is within the allowable range. This high-precision prediction can effectively avoid service interruptions caused by model errors.

[0061] Step S30: If it is detected that the vehicle is about to enter the predicted signal blind area, obtain the current driving information of the vehicle, predict the duration of the signal blind area, and pre-store the in-vehicle network data according to the duration of the signal blind area through a preset pre-storage rule.

[0062] It should be noted that the current driving information of the vehicle refers to the data related to the driving state collected in real time during the driving process of the vehicle, including but not limited to the speed, acceleration, driving direction, geographical location, etc. of the vehicle. By obtaining the current driving information of the vehicle, the time when the vehicle enters the signal blind area and its driving trajectory in the blind area can be predicted more accurately, thereby providing a basis for predicting the duration of the signal blind area.

[0063] The duration of the signal blind area refers to the time length that the vehicle experiences from entering the signal blind area to leaving the signal blind area. This time length is affected by factors such as the driving speed of the vehicle, the driving direction, and the range of the signal blind area. The duration of the signal blind area is a key parameter that determines the in-vehicle network data pre-storage strategy. The amount and type of pre-stored network data need to be reasonably allocated according to the duration of the signal blind area to ensure that the vehicle can normally use the network function in the signal blind area.

[0064] The preset pre-storage rule refers to a set of rules formulated in advance according to the driving state of the vehicle, the characteristics of the signal blind area, and the requirements of in-vehicle network applications for determining the in-vehicle network data pre-storage strategy. The pre-storage rule is used to guide how to select the pre-stored network data before the vehicle enters the signal blind area, including which types of data to pre-store (such as video, audio, text information, etc.), the priority of pre-stored data, the amount of pre-stored data, etc., to ensure that the vehicle can obtain the best network usage experience in the signal blind area.

[0065] The in-vehicle network data refers to the data that various in-vehicle network applications (such as navigation systems, multimedia entertainment systems, vehicle networking applications, etc.) need to obtain from the network during operation. The pre-storage of in-vehicle network data is to ensure that these applications can continue to operate normally after the vehicle enters the signal blind area without being affected by the interruption of the network signal. The pre-stored data can include map data, multimedia content, real-time traffic information, etc.

[0066] The pre-storage strategy refers to a specific in-vehicle network data pre-storage plan formulated according to the preset pre-storage rule, combined with the current driving information of the vehicle and the duration of the signal blind area. This strategy includes the priority of data pre-storage, data volume allocation, data type selection, etc. The formulation of the pre-storage strategy is to reasonably allocate the pre-stored data within the limited local storage space to ensure that the vehicle can preferentially use the most important network data in the signal blind area, thereby improving the availability and user experience of in-vehicle network applications.

[0067] The local remaining storage space refers to the remaining capacity of the storage devices (such as hard disks, solid-state drives, etc.) in the vehicle that can be used to store data in the current state. The local remaining storage space is one of the important bases for formulating the pre-storage strategy. The amount of pre-stored network data needs to be reasonably allocated according to the size of the local remaining storage space to avoid pre-storage failure or inability to pre-store some data due to insufficient storage space.

[0068] The network data pre-storage priority refers to a priority list for sorting different types of network data according to the importance of network applications in the vehicle and user requirements. Data with a higher priority will be pre-stored in the local storage of the vehicle first. The network data pre-storage priority is used to preferentially save the data that has the greatest impact on vehicle driving safety, navigation function or user experience, such as navigation map data, emergency communication data, etc., within the limited storage space and pre-storage time.

[0069] The multimedia cache data space refers to the temporary storage space reserved in the vehicle for storing multimedia data (such as audio, video, pictures, etc.). These spaces are usually used to cache the multimedia content to be played or displayed soon to improve the playback fluency. During the duration of the signal blind area, the multimedia cache data space is used to store the pre-stored multimedia data to ensure that the multimedia applications in the vehicle (such as in-vehicle entertainment systems) can still play or display the content normally when the signal is interrupted.

[0070] In a feasible implementation manner, the steps of obtaining the current driving information of the vehicle, predicting the duration of the signal blind area, and pre-storing the network data in the vehicle according to the duration of the signal blind area through a preset pre-storage rule include: Obtain the list of background active applications in the vehicle and the local remaining storage space; Determine the network data pre-storage priority of the background active applications in the vehicle according to the preset application pre-storage priority list and the background active application list; Allocate the multimedia cache data space for each background active application according to the duration of the blind area, the local remaining storage space, and the network data pre-storage priority; Obtain and pre-store the network data into the multimedia cache data space.

[0071] It should be noted that pre-storage includes, but is not limited to, the process of dynamically adjusting the amount and type of pre-stored data according to real-time prediction results and user needs, and also includes the update and optimization mechanism of cached data.

[0072] The multimedia cache data space includes, but is not limited to, the space allocated to multimedia application data caching in the vehicle storage device, and also includes the dynamically adjusted storage area.

[0073] Exemplarily, assume that a car plans to drive from City A to City B and needs to pass through a mountain road section and a tunnel on the way. The signal coverage of the mountain road and the tunnel is poor, and there are multiple signal blind spots. The in-vehicle network data pre-storage device before entering the signal blind spot ensures that the vehicle can still provide reliable services within the blind spot through the following steps.

[0074] The device detects that the currently active multimedia applications in the vehicle include a navigation system, a music player, and a message notification application. The device obtains the blind spot duration of the vehicle entering the mountain road and the tunnel through a blind spot prediction model, which are 2 minutes and 3 minutes respectively. The device calculates the space available for multimedia data caching based on the blind spot duration and the remaining local storage space. For example, 2 minutes of multimedia data needs to be cached for the mountain road blind spot, and 3 minutes of multimedia data needs to be cached for the tunnel blind spot. During the vehicle's driving process, the device monitors the storage space and network status in real time and dynamically adjusts the pre-stored data. For example, if the storage space is insufficient, the data of critical applications is preferentially retained; if the network status improves, the cached data is updated in advance.

[0075] It can be understood that the device dynamically adjusts the type and quantity of the pre-stored data according to the blind spot duration and user requirements. For example, when the blind spot duration is long, the amount of pre-stored data is increased; when the storage space is insufficient, the data of critical applications is preferentially pre-stored. The device intelligently allocates the cache data space according to the remaining local storage space. For example, if the storage space is limited, the critical data required by the navigation and multimedia applications is preferentially cached to ensure the continuity of services within the blind spot. After the vehicle exits the blind spot, the device automatically uploads the cached protocol request data, obtains the latest data in the server-side response, and updates the local cache to ensure the timeliness of the data. Inside the mountain tunnel, the device ensures that the navigation system can provide accurate route suggestions and the multimedia player can play content smoothly through the pre-stored real-time traffic event information and multimedia data, avoiding service interruptions caused by network outages. The device maintains the "online" status of critical applications through a pseudo handshake mechanism. For example, when the vehicle enters the tunnel, the device simulates protocol response data to ensure that the user's application programs (such as music players or navigation systems) can continue to run without manual refreshing. The device reduces unnecessary data pre-storage through intelligent cache allocation, reduces the occupancy of storage space, and at the same time optimizes the use of network bandwidth to improve the overall system efficiency. Through the dynamic pre-storage strategy and intelligent cache allocation function of the in-vehicle network data pre-storage device before entering the signal blind spot, the vehicle can provide continuous navigation and multimedia services within the signal blind spot, significantly improving the reliability of the in-vehicle wireless network within the blind spot. This technology not only reduces the operation redundancy of users caused by network outages, but also optimizes the overall driving experience, while reducing the consumption of system resources and improving the overall efficiency of the system.

[0076] In a feasible implementation manner, the step of acquiring and pre-storing network data into the multimedia cache data space includes: Based on the blind area duration, pre-store the real-time traffic event information of the vehicle navigation route; When entering the signal blind area, convert the real-time traffic event information into traffic event markers supported by the offline map; Add the traffic event markers to the offline map; Obtain the acceleration direction, vehicle speed, and driving direction of the vehicle's travel to obtain the blind area vehicle travel information; Combine the vehicle navigation route and the blind area vehicle travel information to update the vehicle position in the offline map in real time; When it is detected that the vehicle position reaches the traffic event marker, execute a preset reminder scheme.

[0077] It should be noted that the real-time traffic event information includes but is not limited to dynamic information such as traffic accidents, road construction, and traffic congestion on the current road, and also includes the road conditions under the influence of weather.

[0078] The offline map includes but is not limited to static map data stored in in-vehicle devices, and also includes pre-downloaded geographical information and navigation routes.

[0079] The traffic event markers include but are not limited to markers for identifying the location and type of traffic events, and also include the severity and expected duration of the events. Exemplarily, assume that a car is driving on a highway and there is a traffic congestion in the front section due to an accident. The in-vehicle network data pre-storing device before entering the signal blind area ensures that the vehicle acquires and updates traffic event information before entering the signal blind area so as to still provide accurate navigation services in the blind area through the following steps.

[0080] The device obtains through the blind area prediction model that the duration of the signal blind area that the vehicle is about to enter is 3 minutes.

[0081] The device pre-stores the real-time traffic event information on the vehicle navigation route according to the blind area duration, including the traffic accident and congestion conditions in the front section.

[0082] When the vehicle enters the signal blind area, the device converts the pre-stored real-time traffic event information into traffic event markers supported by the offline map. For example, it converts the traffic accident location into a red marker on the map and the congested section into a yellow marker.

[0083] The device adds these traffic event markers to the offline map to ensure that the navigation system can still display the real-time traffic conditions in the blind area.

[0084] After the vehicle exits the blind spot, the device automatically obtains the latest traffic event information and updates the markings in the offline map to ensure the timeliness of the information.

[0085] It can be understood that the device dynamically pre-stores the real-time traffic event information on the vehicle navigation route according to the blind spot duration, ensuring that key traffic dynamics can still be obtained within the blind spot.

[0086] The device can convert the real-time traffic event information into markings supported by the offline map and seamlessly integrate it into the offline map to provide continuous navigation services.

[0087] After the vehicle exits the blind spot, the device automatically obtains and updates the traffic event information to ensure that the data in the offline map remains up-to-date.

[0088] Within the signal blind spot, the device ensures that the navigation system can provide accurate route suggestions through pre-stored and converted traffic event markings, avoiding information lag caused by network interruption.

[0089] The device ensures that users can also obtain the latest road conditions within the blind spot by real-time updating traffic event information, reducing driving delays caused by traffic events.

[0090] By pre-storing and displaying traffic event markings in advance, the device helps drivers understand the road conditions in advance, make safer driving decisions, and reduce the accident risk.

[0091] Through the dynamic pre-storing and intelligent conversion functions of the in-vehicle network data pre-storing device before entering the signal blind spot, the vehicle can provide continuous and accurate navigation services within the signal blind spot, significantly improving the reliability of the in-vehicle navigation system within the blind spot. This technology not only optimizes the user experience but also reduces the driving risk and ensures driving safety.

[0092] In a feasible implementation manner, after the step of pre-storing the in-vehicle network data according to the signal blind spot duration through a preset pre-storing rule, the following steps are included: Determine the active target anti-disconnection applications according to the anti-disconnection application list preset by the user; Send simulated first protocol response data to the target anti-disconnection applications through a pseudo-handshake mechanism; Record the protocol request data of the target anti-disconnection applications; When the network signal strength resumes to the preset threshold, upload the protocol request data and obtain the second protocol response data responded by the server to update the first protocol response data.

[0093] It should be noted that the pseudo-handshake mechanism is a mechanism that simulates the handshake signal in the data transmission process and is used to maintain the "online" state of the application, including but not limited to avoiding application disconnection caused by network interruption by simulating protocol response data.

[0094] The target anti-disconnection applications include, but are not limited to, in-vehicle applications with high requirements for network connection, such as navigation systems, multimedia players, and communication applications, which need to maintain an "online" state in signal blind spots.

[0095] The protocol response data includes, but is not limited to, the response data generated according to the communication protocol, which is used to maintain the normal operation of the application. The first protocol response data is simulated and used to maintain the "online" state of the application; the second protocol response data is real and used to update the simulated data.

[0096] Exemplarily, assume that a car is about to enter a signal blind spot. The in-vehicle network data pre-storage device before entering the signal blind spot ensures that the vehicle can still provide reliable services in the blind spot through the following steps.

[0097] The device obtains through the blind spot prediction model that the duration of the signal blind spot the vehicle is about to enter is 3 minutes. The device detects that the currently active target anti-disconnection applications in the vehicle include a navigation system, a music player, and a message notification application.

[0098] The device sends the simulated first protocol response data to the target anti-disconnection applications through the pseudo handshake mechanism to maintain the "online" state of the applications. For example, it sends simulated location update data to the navigation system and simulated playback state data to the music player.

[0099] The device records the protocol request data generated by the target anti-disconnection applications in the blind spot. For example, it records the real-time traffic information requested by the navigation system and the data of the next song requested by the music player.

[0100] When the vehicle drives out of the signal blind spot and the network signal strength resumes to the preset threshold, the device automatically uploads the recorded protocol request data, obtains the second protocol response data responded by the server, and updates the previously simulated first protocol response data. For example, it uploads the real-time traffic information requested by the navigation system, obtains the latest response from the server, and updates the traffic data of the navigation system.

[0101] It can be understood that the device sends the simulated first protocol response data to the target anti-disconnection applications through the pseudo handshake mechanism to ensure that the applications remain "online" in the blind spot and avoid application disconnection caused by network interruption.

[0102] The device records the protocol request data of the application in the blind spot and automatically uploads it after the network resumes, ensuring the integrity and timeliness of the data.

[0103] After the vehicle drives out of the blind spot, the device automatically obtains and updates the protocol response data to ensure the accuracy and currency of the application data.

[0104] In the signal blind area, the device ensures that users can continue to use key applications such as navigation and music playback by maintaining the "online" status of the applications, avoiding service interruptions caused by network outages.

[0105] By maintaining the "online" status of the navigation system, the device helps drivers obtain accurate navigation information in the blind area, make safer driving decisions, and reduce the risk of accidents.

[0106] The device reduces data loss caused by network outages by recording and uploading protocol request data, improving the overall efficiency and reliability of the system.

[0107] Through the pseudo handshake mechanism and automatic update function of the in-vehicle network data pre-storage device before entering the signal blind area, the vehicle can provide continuous and reliable services in the signal blind area, significantly improving the stability of in-vehicle applications and the user experience in the blind area. This technology not only optimizes the driving experience but also reduces the driving risk and ensures driving safety. This embodiment provides a method for pre-storing in-vehicle network data before entering the signal blind area. This application first improves the reliability of the in-vehicle network in the blind area environment: By downloading in advance the in-vehicle wireless network data required for potential blind areas in the path, it is ensured that in the environment with poor network signals or in the blind area, the in-vehicle navigation system and multimedia player can still provide timely and accurate services without frequent operations and waiting for the network to recover, thus reducing service interruptions and improving the user experience. By combining the vehicle navigation route, base station signal distribution map, and historical signal data, the duration of the signal blind area that the vehicle is about to enter is predicted, enabling the system to more accurately predict the location and duration of the blind area, make data preparations and resource allocations in advance, and enhance the flexibility and response ability of the in-vehicle network. For the vehicle planned route information, by analyzing real-time and historical signal data in combination with the blind area prediction model, the resource allocation can be flexibly adjusted, and the required network data can be pre-stored to ensure the continuous availability of the in-vehicle wireless network in the blind area.

[0108] Through the above technical effects, this application can provide a service experience without feeling the blind area around the signal blind area, reduce the interference with the normal use of the vehicle navigation system and multimedia player, and improve the overall reliability of the in-vehicle network system and the user experience.

[0109] Exemplarily, to help understand the implementation process of the method for pre-storing in-vehicle network data before entering the signal blind area obtained by combining this embodiment with the above Embodiment 1, please refer to Figure 3 , Figure 3 A brief flow schematic diagram of the method for pre-storing in-vehicle network data before entering the signal blind area is provided. Specifically: The device extracts the driving route information of the vehicle from the in-vehicle navigation system, including the starting point (City A), the ending point (City B), the key nodes along the way (such as mountainous sections and tunnels), the expected driving speed, etc. At the same time, the device obtains the signal distribution map of the base stations along the route from the cloud, and combines the historical signal data to generate the vehicle planned route information.

[0110] The device inputs the vehicle planned route information into a preset blind area prediction model, and combines the current vehicle speed and the base station signal strength heat map to predict the duration of the vehicle entering the signal blind area. For example, it is predicted that the vehicle will enter the signal blind area on the mountain road and in the tunnel, and the blind area durations are 2 minutes and 3 minutes respectively.

[0111] The device dynamically pre-stores the required in-vehicle wireless network data according to the blind area duration and the preset pre-storage rules. The specific steps are as follows: Determine the multimedia applications that can be pre-stored: It is detected that the currently active multimedia applications of the vehicle include the navigation system, the music player, and the message notification application.

[0112] Allocate the multimedia cache data space: Calculate the space available for multimedia data caching according to the blind area duration and the local remaining storage space. For example, the mountain road blind area needs to cache 2 minutes of multimedia data, and the tunnel blind area needs to cache 3 minutes of multimedia data.

[0113] Pre-store the network data of the multimedia applications: Pre-store the data required by the navigation system, the music player, and the message notification application into the multimedia cache data space.

[0114] The device pre-stores the real-time traffic event information on the vehicle navigation route, including traffic accidents and congestion conditions on the front section. When the vehicle enters the signal blind area, these real-time traffic event information are converted into marks supported by the offline map and added to the offline map.

[0115] The device sends simulated first protocol response data to the target anti-disconnection applications (such as the navigation system, the music player) through a pseudo handshake mechanism to maintain the "online" state of the applications. At the same time, record the protocol request data generated by the applications in the blind area.

[0116] When the vehicle exits the signal blind area and the network signal strength resumes to the preset threshold, the device automatically uploads the recorded protocol request data, obtains the second protocol response data responded by the server, and updates the previously simulated first protocol response data.

[0117] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for pre-storing the in-vehicle network data before the vehicle enters the signal blind area. Based on this technical concept, more simple transformations in various forms are within the protection scope of this application.

[0118] The present application also provides a vehicle in-network data pre-storage device before entering a signal blind area. Please refer to Figure 4 , the vehicle in-network data pre-storage device before entering a signal blind area includes: An acquisition module 10, configured to acquire a vehicle navigation route, and determine a corresponding base station signal distribution map and navigation route historical signal data; A prediction module 20, configured to predict each predicted signal blind area in the vehicle navigation route through a preset blind area prediction model according to the base station signal distribution map and the navigation route historical signal data; A pre-storage module 30, configured to, if it is monitored that the vehicle is about to enter the predicted signal blind area, acquire the current driving information of the vehicle and predict the signal blind area duration, and pre-store the vehicle in-network data according to the signal blind area duration through a preset pre-storage rule.

[0119] And / or, the prediction module 20 includes: A first extraction module, configured to perform time series feature extraction on the navigation historical signal data through a preset long short-term memory neural network model to obtain historical signal time series features; A second extraction module, configured to perform spatial feature extraction on the base station signal distribution map through a preset convolutional neural network model to obtain a current spatial vector feature; A first acquisition module, configured to acquire real-time weather information and real-time geomorphic information of the vehicle navigation route; A first prediction module, configured to input the real-time weather information, the real-time geomorphic information, the historical signal time series features, and the current spatial vector features into the blind area prediction model to predict each predicted signal blind area on the vehicle navigation route.

[0120] And / or, the vehicle in-network data pre-storage device before entering a signal blind area includes: A second acquisition module, configured to acquire historical signal time series samples, base station spatial vector samples, and signal blind area distribution labels; A first projection module, configured to project the historical signal time series samples and the base station spatial vector samples to the same dimension, and generate a spatio-temporal weighted feature representation according to a preset attention matrix model; A third acquisition module, configured to acquire severe weather impact factors and severe geomorphic impact factors; A first generation module, configured to generate a severe condition weight according to the severe weather impact factors and the severe geomorphic impact factors; A second prediction module, configured to input the spatio-temporal weighted feature representation and the severe condition weight into a signal blind area prediction model to be trained to predict the signal blind area and obtain a signal blind area prediction result; The first comparison module is used to compare the predicted signal blind area result and the signal blind area distribution label to obtain a prediction error value; The first loop module is used to, if the prediction error value does not meet the preset threshold, adjust the hyperparameters of the attention matrix model, return to the step of projecting the historical signal time series samples and the base station space vector samples into the same dimension, and generate a spatio-temporal weighted feature representation according to the preset attention matrix model until the prediction error value meets the preset threshold, and output the predicted signal blind area.

[0121] And / or, the pre-storage module 30 includes: The fourth acquisition module is used to acquire the list of active background applications in the vehicle and the local remaining storage space; The first determination module is used to determine the network data pre-storage priority of the active background applications in the vehicle according to the preset application pre-storage priority list and the background active application list; The first allocation module is used to allocate the multimedia cache data space for each background active application according to the blind area duration, the local remaining storage space and the network data pre-storage priority; The first pre-storage module is used to acquire and pre-store network data into the multimedia cache data space.

[0122] And / or, the first pre-storage module includes: The second pre-storage module is used to pre-store the real-time traffic event information of the vehicle navigation route based on the blind area duration; The first conversion module is used to convert the real-time traffic event information into a traffic event marker supported by the offline map when entering the signal blind area; The first addition module is used to add the traffic event marker to the offline map; The fifth acquisition module is used to acquire the acceleration direction, vehicle speed and driving direction of the vehicle to obtain the driving information of the vehicle in the blind area; The first update module is used to update the vehicle position in the offline map in real time by combining the vehicle navigation route and the driving information of the vehicle in the blind area; The first execution module is used to execute a preset reminder scheme when it is detected that the vehicle position reaches the traffic event marker.

[0123] And / or, the in-vehicle network data pre-storage device before entering the signal blind area includes: The second determination module is used to determine the active target anti-disconnection applications according to the user-preset anti-disconnection application list; The first sending module is used to send simulated first protocol response data to the target anti-disconnection applications through a pseudo handshake mechanism; The first recording module is used to record the protocol request data of the target anti-disconnection application; The sixth obtaining module is used to upload the protocol request data and obtain the second protocol response data responded by the server side to update the first protocol response data when the network signal strength recovers to a preset threshold.

[0124] The in-vehicle network data pre-storage device before entering the signal blind area provided by the present application adopts the in-vehicle network data pre-storage method before entering the signal blind area in the above embodiment, and can solve the technical problem of poor reliability of the in-vehicle wireless network in the blind area environment. Compared with the prior art, the beneficial effects of the in-vehicle network data pre-storage device before entering the signal blind area provided by the present application are the same as those of the in-vehicle network data pre-storage method before entering the signal blind area provided by the above embodiment, and other technical features in the in-vehicle network data pre-storage device before entering the signal blind area are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0125] The present application provides an in-vehicle network data pre-storage device before entering the signal blind area. The in-vehicle network data pre-storage device before entering the signal blind area 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 so that the at least one processor can execute the in-vehicle network data pre-storage method in the first embodiment above.

[0126] Next, refer to Figure 5 , which shows a schematic structural diagram of an in-vehicle network data pre-storage device suitable for implementing the embodiments of the present application. The in-vehicle network data pre-storage device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PMPs (Portable Media Player), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The in-vehicle network data pre-storage device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0127] As Figure 5As shown, the in-vehicle network data pre-storage device before entering the signal blind area 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 (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the in-vehicle network data pre-storage device before entering the signal blind area are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O 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 can allow the in-vehicle network data pre-storage device before entering the signal blind area to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an in-vehicle network data pre-storage device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or had alternatively.

[0128] Specifically, 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 the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0129] The in-vehicle network data pre-storage device before entering the signal blind area provided by the present application adopts the method for pre-storing in-vehicle network data before entering the signal blind area in the above-mentioned embodiment, and can solve the technical problem of poor reliability of the in-vehicle wireless network in the blind area environment. Compared with the prior art, the beneficial effects of the in-vehicle network data pre-storage device before entering the signal blind area provided by the present application are the same as those of the method for pre-storing in-vehicle network data before entering the signal blind area provided by the above-mentioned embodiment, and other technical features in the in-vehicle network data pre-storage device before entering the signal blind area are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0130] 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.

[0131] As mentioned above, only the specific implementation manners of the present application are described, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all 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.

[0132] 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 method for pre-storing in-vehicle network data before entering the signal blind area in the above-mentioned embodiment.

[0133] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, 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 may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0134] The above computer-readable storage medium may be included in the in-vehicle network data pre-storage device before entering the signal blind area; or it may exist independently without being assembled into the in-vehicle network data pre-storage device before entering the signal blind area.

[0135] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the in-vehicle network data pre-storage device before entering the signal blind area, the in-vehicle network data pre-storage device before entering the signal blind area is caused to: obtain the vehicle navigation route, determine the corresponding base station signal distribution map and navigation route historical signal data; according to the base station signal distribution map and the navigation route historical signal data, through a preset blind area prediction model, predict each predicted signal blind area in the vehicle navigation route; if it is monitored that the vehicle is about to enter the predicted signal blind area, obtain the current driving information of the vehicle and predict the signal blind area duration, and according to the signal blind area duration, pre-store the in-vehicle network data through a preset pre-storage rule.

[0136] 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 kind 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 an Internet service provider using the Internet).

[0137] 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 code that 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 that marked in the accompanying drawings. For example, two consecutive blocks shown 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.

[0138] 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 on the unit itself in some cases.

[0139] The readable storage medium provided in 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 method for pre-storing in-vehicle network data before entering the signal blind area, and can solve the technical problem of poor reliability of the in-vehicle wireless network in the blind area environment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for pre-storing in-vehicle network data before entering the signal blind area provided in the above embodiments, and will not be elaborated here.

[0140] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the method for pre-storing in-vehicle network data before entering a signal blind area as described above.

[0141] The computer program product provided by the present application can solve the technical problem of poor reliability of in-vehicle wireless networks in blind area environments. 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 method for pre-storing in-vehicle network data before entering a signal blind area provided in the above embodiments, and will not be elaborated here.

[0142] The above 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 method for pre-storing network data in a vehicle before entering a signal blind area, characterized in that: The method includes: Obtain the vehicle navigation route, determine the corresponding base station signal distribution map and navigation route historical signal data; According to the base station signal distribution map and the navigation route historical signal data, each predicted signal blind spot in the vehicle navigation route is predicted by a preset blind spot prediction model; If it is detected that a vehicle is about to enter the predicted signal blind spot, the current driving information of the vehicle is obtained and the duration of the signal blind spot is predicted, and based on the duration of the signal blind spot, the network data in the vehicle is pre-stored according to preset pre-stored rules.

2. The method according to claim 1, characterized in that The method for predicting each predicted signal blind spot in the vehicle navigation route according to the base station signal distribution map and the navigation route historical signal data by using a preset blind spot prediction model comprises: By using a preset long short-term memory neural network model, time series feature extraction is performed on the navigation history signal data to obtain the time series feature of the history signal; By using a preset convolutional neural network model, spatial features are extracted from the base station signal distribution map to obtain current spatial vector features; Obtaining real-time weather information and real-time topographic information of the vehicle navigation route; The real-time weather information, the real-time topographic information, the historical signal time series characteristics and the current space vector characteristics are input into a blind spot prediction model to predict each predicted signal blind spot on the vehicle navigation route.

3. The method according to claim 2, characterized in that The step of inputting the real-time weather information, the real-time topographic information, the historical signal time series characteristics and the current space vector characteristics into the blind spot prediction model to predict each predicted signal blind spot on the vehicle navigation route includes: Obtain historical signal time series samples, base station space vector samples and signal blind area distribution labels; Projecting the historical signal time series samples and the base station space vector samples to the same dimension, and generating a time-space weighted feature representation according to a preset attention matrix model; Obtain the influencing factors of severe weather and severe landforms; Generating a severe condition weight according to the severe weather impact factor and the severe landform impact factor; Inputting the time-space weighted feature representation and the severe condition weight into the signal blind spot prediction model to be trained, predicting the signal blind spot, and obtaining a signal blind spot prediction result; Comparing the signal blind area prediction result and the signal blind area distribution label to obtain a prediction error value; If the prediction error value does not meet the preset threshold, the hyperparameters of the attention matrix model are adjusted, and the step of projecting the historical signal time series samples and the base station space vector samples to the same dimension is returned to generate a time-space weighted feature representation according to the preset attention matrix model until the prediction error value meets the preset threshold and the prediction signal blind spot is output.

4. The method according to claim 1, characterized in that The steps of obtaining the current driving information of the vehicle and predicting the duration of the signal blind area, and pre-storing the network data in the vehicle according to the duration of the signal blind area by using a preset pre-storage rule include: Get the list of active background applications in the vehicle and the remaining local storage space; Determining the network data pre-storage priority of background active applications in the vehicle according to the preset application pre-storage priority list and the background active application list; Allocate multimedia cache data space for each background active application according to the blind area duration, the local remaining storage space and the network data pre-storage priority; Acquire and pre-store network data into the multimedia cache data space.

5. The method according to claim 4, characterized in that The step of acquiring and pre-storing network data in the multimedia cache data space comprises: Pre-storing real-time traffic event information of the vehicle navigation route based on the blind spot duration; When entering a signal blind area, converting the real-time traffic event information into a traffic event mark supported by an offline map; adding the traffic event mark to the offline map; Obtain the acceleration direction, vehicle speed and driving direction of the vehicle, and obtain the driving information of the vehicle in the blind spot; In combination with the vehicle navigation route and the blind spot vehicle driving information, the vehicle position is updated in real time in the offline map; When it is detected that the vehicle position reaches the traffic event mark, a preset reminder scheme is executed.

6. The method according to claim 1, characterized in that The step of pre-storing the network data in the vehicle according to the duration of the signal blind zone by using a preset pre-storage rule comprises: According to the user's preset anti-disconnection application list, determine the active target anti-disconnection application; Sending simulated first protocol response data to the target anti-disconnection application through a pseudo handshake mechanism; Recording protocol request data of the target anti-disconnection application; When the network signal strength recovers to a preset threshold, the protocol request data is uploaded, and the second protocol response data responded by the server is obtained to update the first protocol response data.

7. A network data pre-storage device in a vehicle before entering a signal blind area, characterized in that: The device comprises: An acquisition module is used to acquire the vehicle navigation route and determine the corresponding base station signal distribution map and navigation route historical signal data; A prediction module, used to predict each predicted signal blind spot in the navigation route of the vehicle according to the base station signal distribution map and the historical signal data of the navigation route through a preset blind spot prediction model; The pre-storage module is used to obtain the vehicle's current driving information and predict the duration of the signal blind spot if it is detected that the vehicle is about to enter the predicted signal blind spot, and pre-store the network data in the vehicle according to the preset pre-storage rules based on the duration of the signal blind spot.

8. A network data pre-storage device in a vehicle before entering a signal blind area, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for pre-storing network data in a vehicle before entering a signal blind spot as described in any one of claims 1 to 6.

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. When the computer program is executed by a processor, the steps of the method for pre-storing network data in a vehicle before entering a signal blind spot are implemented as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for pre-storing network data in a vehicle before entering a signal blind spot according to any one of claims 1 to 6 are implemented.

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