Vehicle driving data uploading method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of vehicles, and provides a vehicle driving data uploading method, electronic equipment and a storage medium, and the method comprises the steps: obtaining the vehicle driving data of a target vehicle; predicting a vehicle accident occurrence probability of the target vehicle based on the vehicle driving data and a preset analysis model; and if it is determined that the vehicle accident occurrence probability meets the preset condition, historical driving data of the target vehicle within a preset duration before the current moment is obtained, and the historical driving data is uploaded to a cloud server. According to the method, the situation that vehicle driving data in a period of time before an accident is not uploaded due to failure of a vehicle system due to the vehicle accident can be avoided, and when it is predicted that the target vehicle possibly has the accident or is encountering the accident, the historical driving data in the preset duration before the current moment is uploaded in time; data support can be provided for follow-up accident reason analysis, insurance claim and responsibility judgment, vehicle safety performance improvement and the like, and the accuracy is improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a method for uploading vehicle driving data, an electronic device, and a storage medium. Background Art

[0002] In recent years, with the changes in lifestyle and the development of the automotive industry, there are more and more vehicles on the road, and the probability of vehicle accidents has gradually increased. Among them, the vehicle driving data before and after a vehicle accident (for example, data such as vehicle speed, video in the vehicle recorder, etc.) is an important basis for subsequent accident cause analysis, insurance claims and liability determination, and improvement of vehicle safety performance. However, in related vehicle systems, often due to reasons such as network, when the vehicle system fails due to a vehicle accident, the vehicle driving data for a period of time before the accident has not been uploaded, thus affecting the accuracy of subsequent accident cause analysis, insurance claims and liability determination, and improvement of vehicle safety performance. Summary of the Invention

[0003] In view of this, this application provides a method for uploading vehicle driving data, an electronic device, and a storage medium to solve the problem that in related vehicle systems, the vehicle driving data is not uploaded in time, affecting the accuracy of subsequent accident cause analysis, insurance claims and liability determination, and improvement of vehicle safety performance.

[0004] In a first aspect of an embodiment of this application, a method for uploading vehicle driving data is provided. The method for uploading vehicle driving data includes: obtaining vehicle driving data of a target vehicle; predicting the probability of a vehicle accident occurring to the target vehicle based on the vehicle driving data and a preset analysis model; if it is determined that the probability of the vehicle accident occurring meets a preset condition, obtaining historical driving data of the target vehicle within a preset time period before the current moment, and uploading the historical driving data to a cloud server.

[0005] In some embodiments, the predicting the probability of a vehicle accident occurring to the target vehicle based on the vehicle driving data and a preset analysis model includes: using the preset analysis model to detect the vehicle driving state of the target vehicle to obtain driving state data; predicting the probability of the vehicle accident occurring based on the driving state data and the vehicle driving data.

[0006] In some embodiments, the predicting the probability of the vehicle accident occurring based on the driving state data and the vehicle driving data includes: inputting the driving state data and the vehicle driving data into a preset neural network model; using the preset neural network model to encode the driving state data and the vehicle driving data to obtain a target feature vector; calculating the similarity between the target feature vector and a preset feature vector; taking the preset probability of the preset feature vector corresponding to the similarity greater than a preset threshold as the probability of the vehicle accident occurring.

[0007] In some embodiments, the vehicle driving data uploading method further includes: if the probability of vehicle accident occurrence is within a preset probability range, determining that the probability of vehicle accident occurrence meets the preset condition.

[0008] In some embodiments, the vehicle driving data uploading method further includes: selecting data from the vehicle driving data; predicting the probability of vehicle accident occurrence based on the preset analysis model and the selected data.

[0009] In some embodiments, uploading the historical driving data to the cloud server includes: obtaining the marking time of the historical driving data, sorting a plurality of sub-data in the historical driving data according to the marking time to obtain a data upload sequence; uploading the historical driving data to the cloud server according to the data upload sequence and a preset priority.

[0010] In some embodiments, the vehicle driving data uploading method further includes: monitoring the upload progress of the historical driving data; if the upload progress does not meet the preset requirements, adjusting the upload speed of the historical driving data.

[0011] In some embodiments, obtaining the vehicle driving data of the target vehicle includes: detecting and recording the self-state data and environmental state data of the target vehicle; using the self-state data and the environmental state data as the vehicle driving data.

[0012] A second aspect of the embodiments of the present application provides a vehicle driving data uploading device, including: an obtaining module, configured to obtain the vehicle driving data of a target vehicle; a predicting module, configured to predict the probability of vehicle accident occurrence of the target vehicle based on the vehicle driving data and a preset analysis model; an uploading module, configured to, if it is determined that the probability of vehicle accident occurrence meets the preset condition, obtain the historical driving data of the target vehicle within a preset time period before the current moment, and upload the historical driving data to the cloud server.

[0013] A third aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, where the processor implements the above vehicle driving data uploading method when executing the computer-readable instructions.

[0014] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, storing computer-readable instructions, where the computer-readable instructions implement the above vehicle driving data uploading method when executed by a processor.

[0015] In a vehicle driving data uploading method provided by an embodiment of the present application, vehicle driving data of a target vehicle is obtained, and based on the vehicle driving data and a preset analysis model, the probability of a vehicle accident occurring to the target vehicle is predicted. Then, it is determined whether the probability of the vehicle accident occurring to the target vehicle meets a preset condition, where the preset condition can be set as a probability range corresponding to a situation where an accident may occur or has already occurred. Thus, when it is determined that the probability of the vehicle accident occurring to the target vehicle meets the preset condition, it can be determined that the target vehicle may have an accident or is encountering an accident. At this time, the electronic device can obtain the historical driving data of the target vehicle within a preset time period before the current moment and upload the historical driving data to the cloud server in a timely manner. Thereby, it reduces the situation where the vehicle driving data for a period of time before an accident has not been uploaded yet after the vehicle system fails due to a vehicle accident, and by uploading the historical driving data within a preset time period before the current moment in a timely manner when it is predicted that the target vehicle may have an accident or is encountering an accident, it can provide data support for subsequent accident cause analysis, insurance claims and liability determination, improvement of vehicle safety performance, etc., and improve the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0017] Figure 1 is an application scenario diagram of the vehicle driving data uploading method provided by the embodiment of the present application.

[0018] Figure 2 is an implementation flowchart of the vehicle driving data uploading method provided by the embodiment of the present application.

[0019] Figure 3 is a schematic structural diagram of the vehicle driving data uploading device provided by the embodiment of the present application.

[0020] Figure 4 is a schematic structural diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] It should be noted that the terms "first" and "second" in the specification, claims and drawings of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0022] In addition, it should be noted that the methods disclosed in the embodiments of the present application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of the claims, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0023] Some embodiments will be described below with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0024] Please refer to Figure 1 As shown, it is an application scenario diagram of the vehicle driving data uploading method provided by the embodiment of the present application. As Figure 1 shown, the electronic device 100 is communicatively connected to the cloud server 200. The electronic device 100 first obtains the vehicle driving data of the target vehicle, then predicts the vehicle accident probability of the target vehicle based on the vehicle driving data and a preset analysis model, and determines whether the vehicle accident probability of the target vehicle meets the preset conditions, so as to determine whether the target vehicle is likely to have an accident. If it is determined that the vehicle accident probability of the target vehicle meets the preset conditions, that is, it is determined that the target vehicle is likely to have an accident or is in the process of experiencing an accident, the electronic device 100 can obtain the historical driving data of the target vehicle within a preset time period at the current moment and immediately upload the historical driving data to the cloud server 200. After receiving the historical driving data, the cloud server 200 can store the historical driving data or upload it to the blockchain to avoid data loss or tampering.

[0025] In some embodiments, when it is necessary to view the above historical driving data for subsequent accident cause analysis, insurance claims and liability determination, or improving vehicle safety performance, etc., the user can query the historical driving data by remotely logging in to the cloud server 200. Or connect to the cloud server 200 through an FTP / SFTP client tool and access the cloud server 200 through file transfer to implement the query of historical driving data. Or access the cloud server 200 through the corresponding application programming interface (Application Programming Interface, API) and web console.

[0026] In some embodiments of the present application, the communication connection includes but is not limited to a wired communication connection or a wireless communication connection. The electronic device 100 can be any one of devices such as in-vehicle devices, control terminals, servers, etc. The cloud server 200 can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0027] Please refer to Figure 2 as shown, which is a flowchart of the implementation of the vehicle driving data uploading method provided by the embodiment of the present application. Taking the method applied to Figure 1 the electronic device 100 as an example, the following steps are included.

[0028] S11: Obtain the vehicle driving data of the target vehicle.

[0029] In some embodiments, in order to more accurately predict the probability of a vehicle accident occurring to the target vehicle, the vehicle driving data may include diverse driving data that may affect vehicle driving and may cause vehicle accidents. For example, the vehicle driving data may be the speed of the target vehicle, acceleration, surrounding environment data (such as obstacles), vehicle driving position, etc.

[0030] In some embodiments, taking the in-vehicle device in the target vehicle as the electronic device as an example, the target vehicle may collect the speed of the target vehicle through a speed sensor, the rotation angle and rotation direction of the steering wheel when the target vehicle steers through a steering angle sensor, acceleration data through an acceleration sensor, surrounding environment data during vehicle driving through a camera device, vehicle positioning data through a Global Positioning System (GPS), etc., and use the obtained speed, rotation angle and rotation direction of the steering wheel when the target vehicle steers, acceleration data, surrounding environment data during vehicle driving, vehicle positioning data, etc. as the vehicle driving data of the target vehicle.

[0031] In some embodiments of the present application, obtaining the vehicle driving data of the target vehicle includes: detecting and recording the self-state data and environmental state data of the target vehicle; using the self-state data and environmental state data as the vehicle driving data.

[0032] In some embodiments, the electronic device may determine whether the target vehicle may have an accident by detecting the environmental state data of the target vehicle. For example, by detecting the distance between the target vehicle and an obstacle, when the distance between the target vehicle and the obstacle is less than a certain distance, the electronic device may determine that the target vehicle may have an accident or has had an accident (when the distance between the target vehicle and the obstacle is less than or equal to 0). The electronic device may also determine whether the target vehicle may have an accident by detecting whether there is an abnormal change in the self-state data of the target vehicle. For example, by detecting the acceleration change of the target vehicle, when there is a huge change in the acceleration of the target vehicle, it can be determined that the target vehicle may have an accident. In order to improve the prediction accuracy of the occurrence of vehicle accidents, the electronic device may obtain the self-state data and environmental state data of the target vehicle as the vehicle driving data, and predict the probability of vehicle accidents based on the vehicle driving data.

[0033] In some embodiments, the electronic device may obtain environmental state data around the target vehicle through devices such as the camera of the target vehicle body or by establishing a model, such as a distance analysis model. The electronic device may obtain its own state data of the target vehicle through the detection device (such as a sensor) of the target vehicle body.

[0034] S12: Predict the probability of a vehicle accident occurring for the target vehicle based on the vehicle driving data and a preset analysis model.

[0035] In some embodiments, the preset analysis model includes, but is not limited to, an Autonomous Emergency Braking (AEB) system model and a Blind Spot Detection (BSD) system model of the vehicle.

[0036] In some embodiments, the AEB system model can monitor obstacles around the vehicle through a microwave radar. When the microwave radar detects an obstacle and there is a collision risk to the vehicle itself, the system will give a warning to the driver and take corresponding braking measures at the same time. The BSD system model can monitor the situation in the blind spot through a radar. When the radar detects a vehicle approaching in the blind spot, the system will give an alarm to the driver and take corresponding braking measures at the same time. Through the AEB system model and the BSD system model, the detection of obstacles or blind spots around the target vehicle can be realized, and thus it can be used to predict the probability of a vehicle accident occurring for the target vehicle.

[0037] In some embodiments, in order to improve the accuracy of predicting the probability of a vehicle accident, the electronic device predicts the probability of a vehicle accident occurring for the target vehicle based on the vehicle driving data and a preset analysis model.

[0038] In some embodiments of the present application, predicting the probability of a vehicle accident occurring for the target vehicle based on the vehicle driving data and a preset analysis model includes: using the preset analysis model to detect the vehicle driving state of the target vehicle to obtain driving state data; predicting the probability of a vehicle accident occurring based on the driving state data and the vehicle driving data.

[0039] In some embodiments, the electronic device may use the preset analysis model to detect the vehicle driving state of the target vehicle. For example, the electronic device uses the AEB system model and the BSD system model to detect whether there are obstacles within a certain distance range from the target vehicle, and uses the detection result as the driving state data of the target vehicle. Based on the driving state data of the target vehicle and the vehicle driving data, the probability of a vehicle accident occurring is predicted.

[0040] In some embodiments, the electronic device may use a neural network model to take driving state data and vehicle driving data as input data of the model to predict the probability of a vehicle accident occurring for the target vehicle. Alternatively, the driving state data and vehicle driving data may be analyzed by analysis methods such as regression analysis, average analysis, cross-analysis, comprehensive evaluation analysis, etc. to predict the probability of a vehicle accident occurring for the target vehicle. The present application does not limit the prediction method for the probability of a vehicle accident occurring.

[0041] In some embodiments of the present application, predicting the probability of a vehicle accident occurring based on driving state data and vehicle driving data includes: inputting the driving state data and vehicle driving data into a preset neural network model; using the preset neural network model to encode the driving state data and vehicle driving data to obtain a target feature vector; calculating the similarity between the target feature vector and a preset feature vector; and taking the preset probability of the preset feature vector corresponding to the similarity greater than a preset threshold as the probability of a vehicle accident occurring.

[0042] In some embodiments, if a neural network model is used to predict the probability of a vehicle accident occurring based on the driving state data and vehicle driving data of the target vehicle, the electronic device may pre-obtain a large number of driving state data samples and vehicle driving data samples as training data, and perform unsupervised model training on the neural network model until the neural network model can be used to judge or identify the preset probability corresponding to the input training data, so as to obtain a preset neural network model. As an example, the preset probability may be a probability range. For example, the probability range [0, 0.1) indicates that a vehicle accident will not occur; the probability range [0.1, 0.8) indicates that a vehicle accident may occur; the probability range [0.8, 1) indicates that a vehicle accident is about to occur; and a probability of 1 indicates that a vehicle accident is occurring.

[0043] In some embodiments, if a neural network model is used to predict the probability of a vehicle accident occurring based on the driving state data and vehicle driving data of the target vehicle, the electronic device may pre-obtain multiple groups of driving state data samples and vehicle driving data samples as training data, and label the corresponding probability of a vehicle accident occurring for each group of training data. Then, the electronic device uses the labeled training data to perform supervised model training to obtain a preset neural network model.

[0044] In some embodiments, the electronic device may also divide the labeled training data samples into a training data set and a test data set. After using the training data set for model training, the test data set may be used to evaluate the trained model, calculate the accuracy, precision, recall rate, etc. of the model, and optimize the model, so as to obtain a preset neural network model.

[0045] In some embodiments, in the process of obtaining multiple groups of driving state data samples and vehicle driving data samples as training data and annotating the corresponding vehicle accident occurrence probabilities for each group of training data, the electronic device can obtain a large number of vehicles with no accidents and vehicles with accidents through web crawler technology or the like, and use the driving state data samples and vehicle driving data samples before and after the accident as training data, and annotate each group of training data. For example, the corresponding vehicle accident occurrence probability at the time of the accident can be annotated as 1, and the corresponding vehicle accident occurrence probability of the training data within a preset time period before and after the accident can be annotated as [0.8, 1), etc.

[0046] In some embodiments, in the process of using a preset neural network model to predict the vehicle accident occurrence probability of a target vehicle, the electronic device can use the driving state data and vehicle driving data of the target vehicle as model inputs, use the preset neural network model to encode the driving state data and vehicle driving data to obtain a target feature vector, calculate the similarity between the target feature vector and a preset feature vector, and determine the vehicle accident occurrence probability of the target vehicle based on the similarity. For example, the preset probability of the preset feature vector corresponding to the similarity greater than a preset threshold is used as the vehicle accident occurrence probability.

[0047] In some embodiments, the neural network model includes, but is not limited to, a recurrent neural network model and a convolutional neural network model.

[0048] In some embodiments of the present application, the electronic device can select data from the vehicle driving data; and predict the vehicle accident occurrence probability based on a preset analysis model and the selected data.

[0049] In some embodiments, if there is some data in the vehicle driving data that has little impact on the occurrence of vehicle accidents, and the electronic device still predicts the vehicle accident occurrence probability based on the preset analysis model and the vehicle driving data, it may affect the prediction accuracy of the vehicle accident occurrence probability. Based on this, the electronic device can select some data from the obtained vehicle driving data that has a greater impact on the occurrence of vehicle-road accidents, and predict the vehicle accident occurrence probability based on the preset analysis model and the selected data to improve the prediction accuracy of the vehicle accident occurrence probability.

[0050] S13: If it is determined that the vehicle accident occurrence probability meets a preset condition, obtain the historical driving data of the target vehicle within a preset time period before the current moment, and upload the historical driving data to the cloud server.

[0051] In some embodiments, the historical driving data of the target vehicle includes, but is not limited to, video data and vehicle driving data of the target vehicle within a preset time period. The satisfaction of the preset condition for the vehicle accident occurrence probability includes, but is not limited to, the vehicle accident occurrence probability being within a preset probability range or falling within a preset probability range. Among them, the preset probability range can be custom-set. For example, the preset probability range can be set to [0.8, 1). In the embodiments of the present application, the preset probability range refers to the probability range corresponding to the scenarios where the vehicle may have an accident, is about to have an accident, or has already had an accident.

[0052] In some embodiments, the current moment refers to the time point when the electronic device determines that the vehicle accident occurrence probability satisfies the preset condition. The preset time period can be custom-set. For example, the preset time period can be set to 5 minutes, 10 minutes, etc.

[0053] In some embodiments, when the electronic device determines that the vehicle accident occurrence probability satisfies the preset condition, it can be determined that the vehicle may have an accident, is about to have an accident, or has already had an accident. At this time, the electronic device can obtain the historical driving data of the target vehicle within the preset time period before the current moment and upload the historical driving data to the cloud server, so as to reduce the situation that the vehicle driving data for a period of time before the accident has not been uploaded after the vehicle system fails due to a vehicle accident.

[0054] In some embodiments, the electronic device repeatedly executes steps S11 to S13. That is, after first determining that the vehicle accident occurrence probability satisfies the preset condition, obtaining the historical driving data of the target vehicle within the preset time period before the current moment, and uploading the historical driving data to the cloud server, the electronic device will continue to obtain the vehicle driving data of the target vehicle, predict the vehicle accident occurrence probability of the target vehicle, and when determining that the vehicle accident occurrence probability satisfies the preset condition, obtain the historical driving data of the target vehicle within the preset time period before the current moment and upload the historical driving data to the cloud server until the vehicle system of the target vehicle fails and cannot upload data.

[0055] In some embodiments, if it is determined that the vehicle accident occurrence probability satisfies the preset condition, the electronic device can also obtain the driving data of the target vehicle within a certain time period after the current moment and upload the obtained driving data to the cloud server.

[0056] In some embodiments of the present application, uploading the historical driving data to the cloud server includes: obtaining the marked time of the historical driving data, sorting a plurality of sub-data in the historical driving data according to the marked time to obtain a data upload sequence; and uploading the historical driving data to the cloud server according to the data upload sequence and a preset priority.

[0057] In some embodiments, when the electronic device acquires the driving data of the target vehicle, it can mark the time when the driving data is acquired. Under normal circumstances, the upload priority of the driving data is arranged according to the marked time of the driving data, and the driving data is uploaded to the cloud server in sequence. For example, the electronic device can set that the earlier the marked time of the driving data, the higher the priority and the more priority it has for uploading.

[0058] In some embodiments, the preset priority can be the highest priority.

[0059] In some embodiments, when it is determined that the probability of a vehicle accident occurring meets the preset conditions, it is determined that the target vehicle may have an accident or is in the process of having an accident. At this time, to avoid the loss of important driving data before and after the vehicle accident and to completely save the important driving data before and after the vehicle accident, the electronic device can set the priority of the historical driving data of the target vehicle within a preset duration before the current moment to the highest priority and give priority to uploading the historical driving data. At the same time, due to the possible multiple sub-data in the historical driving data, for the upload order of the multiple sub-data, the electronic device can sort the multiple sub-data in the historical driving data according to the marked time corresponding to each sub-data to obtain a data upload sequence. For example, the marked times of the multiple sub-data in the historical driving data are 9:00 am, 9:05 am, and 9:10 am in sequence. If the multiple sub-data are sorted in ascending order of the marked time, the data upload sequence is the sub-data corresponding to 9:00 am, the sub-data corresponding to 9:05 am, and the sub-data corresponding to 9:10 am. When the electronic device gives priority to uploading the historical driving data, it uploads the sub-data corresponding to 9:00 am, the sub-data corresponding to 9:05 am, and the sub-data corresponding to 9:10 am in sequence according to the data upload sequence. In this way, the electronic device can acquire and save as much complete driving data as possible before and after the accident before the vehicle system of the target vehicle fails, which can provide data support for subsequent accident cause analysis, insurance claims and liability determination, improvement of vehicle safety performance, etc., and improve the accuracy of accident cause analysis, insurance claims and liability determination, improvement of vehicle safety performance, etc.

[0060] In some embodiments of the present application, the electronic device can monitor the upload progress of the historical driving data; if the upload progress does not meet the preset requirements, adjust the upload speed of the historical driving data.

[0061] In some embodiments, during the process of uploading the historical driving data to the cloud server, the electronic device can monitor the upload progress of the historical driving data in real time to ensure the integrity of the historical driving data and avoid situations such as data loss or network congestion or deadlock caused by too slow upload progress.

[0062] In some embodiments, the preset requirements include, but are not limited to, that the upload progress is within the normal upload progress range. Among them, the normal upload progress range can be custom - set. When the upload progress does not meet the preset requirements, the electronic device can determine that the upload progress of the historical driving data is abnormal. At this time, the electronic device can adjust the upload progress of the historical driving data so that the upload progress meets the normal upload progress range. For example, if it is determined that the upload progress of the historical driving data is too slow relative to the normal upload progress range, the electronic device can increase the upload progress of the historical driving data; if it is determined that the upload progress of the historical driving data is too fast relative to the normal upload progress range, the electronic device can decrease the upload progress of the historical driving data.

[0063] In a method for uploading vehicle driving data provided by an embodiment of the present application, by obtaining the vehicle driving data of a target vehicle and based on the vehicle driving data and a preset analysis model, the probability of a vehicle accident occurring to the target vehicle is predicted. Then, it is determined whether the probability of the vehicle accident occurring to the target vehicle meets the preset conditions, where the preset conditions can be set as the probability range corresponding to the situation where an accident may occur or has already occurred. Thus, when it is determined that the probability of the vehicle accident occurring to the target vehicle meets the preset conditions, it can be determined that the target vehicle may have an accident or is encountering an accident. At this time, the electronic device can obtain the historical driving data of the target vehicle within a preset time period before the current moment and upload the historical driving data to the cloud server in a timely manner. Thus, it reduces the situation where the vehicle driving data for a period of time before the accident has not been uploaded yet after the vehicle system fails due to a vehicle accident, and by uploading the historical driving data within a preset time period before the current moment in a timely manner when it is predicted that the target vehicle may have an accident or is encountering an accident, it can provide data support for subsequent accident - cause analysis, insurance claims and liability determination, and improving vehicle safety performance, and improve the accuracy.

[0064] It should be understood that the magnitudes of the sequence numbers of the steps in the above - mentioned embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0065] In an embodiment of the present application, a vehicle driving data uploading device 300 is provided. The functions that the vehicle driving data uploading device 300 can achieve correspond one - to - one with the vehicle driving data uploading method in the above - mentioned embodiment. As Figure 3As shown in the figure, the vehicle driving data uploading device 300 includes an acquisition module 301, a prediction module 302, and an uploading module 303. The detailed descriptions of each functional module are as follows: The acquisition module 301 is used to acquire the vehicle driving data of the target vehicle; the prediction module 302 is used to predict the probability of a vehicle accident occurring for the target vehicle based on the vehicle driving data and a preset analysis model; the uploading module 303 is used to, if it is determined that the probability of a vehicle accident occurring meets the preset conditions, acquire the historical driving data of the target vehicle within a preset time period before the current moment, and upload the historical driving data to the cloud server.

[0066] For the specific limitations of the vehicle driving data uploading device 300, reference can be made to the limitations on the vehicle driving data uploading method in the foregoing text, which will not be elaborated here. Each module in the above-mentioned vehicle driving data uploading device 300 can be implemented in whole or in part by software, hardware, and their combinations. The above-mentioned modules can be embedded in the processor in the electronic device in the form of hardware or be independent of the processor, or can be stored in the memory in the electronic device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0067] Please refer to Figure 4 As shown in the figure, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 100 includes, but is not limited to, any one of devices such as mobile phones, tablet computers, laptop computers, vehicle-mounted devices, and smart wearable devices. The network where the electronic device 100 is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0068] As Figure 4 As shown in the figure, the electronic device 100 includes a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104, and a bus 105. The processor 103 is respectively coupled to the communication module 101, the memory 102, and the I / O interface 104 through the bus 105.

[0069] The communication module 101 can be a wireless communication module or a mobile communication module. The wireless communication module can provide wireless communication solutions applied to the electronic device 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc. The mobile communication module can provide wireless communication solutions applied to the electronic device 100, including 2G / 3G / 4G / 5G, etc.

[0070] The memory 102 can include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory can be directly read and written by the processor 103, can be used to store the operating system or executable programs (such as machine instructions) of other running programs, and can also be used to store user and application data, etc. The random access memory can include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM, for example, the fifth generation of DDR SDRAM is generally called DDR5 SDRAM), etc.

[0071] The non-volatile memory can also store executable programs and store user and application data, etc., and can be pre-loaded into the random access memory for direct reading and writing by the processor 103. The non-volatile memory can include disk storage devices, flash memory.

[0072] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processor 103, a vehicle driving data uploading method executable on the electronic device 100 can be implemented.

[0073] In other embodiments, the electronic device 100 further includes an external memory interface for connecting to an external memory to implement the expansion of the storage capacity of the electronic device 100.

[0074] The processor 103 may include one or more processing units. For example, the processor 103 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0075] The processor 103 provides computing and control capabilities. For example, the processor 103 is used to execute the computer program stored in the memory 102 to implement the above-mentioned vehicle driving data uploading method.

[0076] The I / O interface 104 is used to provide a channel for user input or output. For example, the I / O interface 104 can be used to connect various input and output devices, such as a mouse, a keyboard, a touch device, a display screen, etc., so that the user can input information or visualize information.

[0077] The bus 105 is at least used to provide a communication channel between the communication module 101, the memory 102, the processor 103, and the I / O interface 104 in the electronic device 100.

[0078] It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0079] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and the method implemented when the program instructions are executed may refer to the vehicle driving data uploading method in the above various embodiments of the present application.

[0080] Among them, the computer-readable storage medium may be the internal memory of the electronic device described in the above embodiment, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0081] Furthermore, the computer-readable storage medium may mainly include a storage program area and a storage data area. Among them, the storage program area may store an operating system, application programs required for at least one function, etc.; the storage data area may store data created according to the use of the electronic device.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for uploading vehicle driving data, It is characterized in that The vehicle driving data uploading method comprises: Obtain vehicle driving data of the target vehicle; Predicting the probability of a vehicle accident occurring for the target vehicle based on the vehicle driving data and a preset analysis model; If it is determined that the probability of the vehicle accident meeting a preset condition, the historical driving data of the target vehicle within a preset time period before the current moment is obtained, and the historical driving data is uploaded to a cloud server.

2. The vehicle driving data uploading method according to claim 1, It is characterized in that The predicting the probability of a vehicle accident of the target vehicle based on the vehicle driving data and a preset analysis model includes: Detecting the vehicle driving state of the target vehicle using a preset analysis model to obtain driving state data; Based on the driving status data and the vehicle driving data, the probability of the vehicle accident occurring is predicted.

3. The vehicle driving data uploading method as claimed in claim 2, It is characterized in that The predicting the probability of the vehicle accident based on the driving state data and the vehicle driving data includes: Inputting the driving state data and the vehicle driving data into a preset neural network model; Using the preset neural network model, the driving state data and the vehicle driving data are encoded to obtain a target feature vector; Calculating the similarity between the target feature vector and the preset feature vector; The preset probability of the preset feature vector corresponding to the similarity greater than the preset threshold is used as the probability of the vehicle accident occurring.

4. The vehicle driving data uploading method as claimed in claim 3, It is characterized in that The method further comprises: If the probability of the vehicle accident occurring is within a preset probability range, it is determined that the probability of the vehicle accident occurring satisfies the preset condition.

5. The vehicle driving data uploading method according to claim 1, It is characterized in that The method further comprises: selecting data from the vehicle driving data; Based on the preset analysis model and the selected data, the probability of the vehicle accident occurring is predicted.

6. The vehicle driving data uploading method according to claim 1, It is characterized in that The uploading of the historical driving data to the cloud server includes: Acquire the marking time of the historical driving data, and sort the multiple sub-data in the historical driving data according to the marking time to obtain a data upload sequence; The historical driving data is uploaded to the cloud server according to the data upload sequence and preset priority.

7. The vehicle driving data uploading method according to claim 1, It is characterized in that The method further comprises: Monitoring the upload progress of the historical driving data; If the upload progress does not meet the preset requirements, the upload speed of the historical driving data is adjusted.

8. The vehicle driving data uploading method according to claim 1, It is characterized in that The step of obtaining the vehicle driving data of the target vehicle includes: Detecting and recording the target vehicle's own state data and environmental state data; The self-state data and the environmental state data are used as the vehicle driving data.

9. An electronic device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, It is characterized in that When the computer-readable instructions are executed by a processor, the vehicle driving data uploading method as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the vehicle driving data uploading method according to any one of claims 1 to 8 is implemented.

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