Data processing method, device, electronic device and storage medium

Abstract

The present disclosure provides a data processing method, device, electronic device and storage medium, the method comprising: sending first road condition information containing vehicle information of a target vehicle and used to characterize the road condition at a target moment to a server, so that the server determines the driving risk between each vehicle in the road condition at the target moment based on the first road condition information and second road condition information containing vehicle information of other vehicles; based on the driving risk between each vehicle in the road condition at the target moment, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the target moment; based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the current moment; based on the driving risk between the target vehicle and other vehicles in the road condition at the current moment, giving an early warning to the target vehicle in the road condition at the current moment. The present disclosure can improve the accuracy of vehicle early warning.

Description

Technical Field

[0001] The present disclosure relates to the field of cloud Internet of Things, and in particular to a data processing method, device, electronic device and storage medium. Background Art

[0002] With the in-depth development of Internet technology in the field of transportation, the application of cloud technology to accurately warn vehicles on the road has important application value. In current technology, when warning vehicles, the server is usually required to stream the collected road condition information to the vehicle. In this process, since the road condition information is transmitted in a stream, the road condition information representing the road condition at a specific time will be cut into multiple parts for transmission, which will cause confusion between the road condition information received by the vehicle at different times, making it impossible to accurately determine the driving risk between vehicles, and it is also impossible to accurately warn vehicles. Summary of the invention

[0003] An object of the present disclosure is to provide a data processing method, device, electronic device and storage medium, which can improve the accuracy of vehicle warning.

[0004] According to one aspect of an embodiment of the present disclosure, a data processing method is disclosed, the method comprising:

[0005] Sending first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time to a server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and second road condition information including vehicle information of other vehicles;

[0006] Based on the driving risks between the various vehicles in the road condition at the target time, obtaining the driving risks between the target vehicle and other vehicles in the road condition at the target time;

[0007] Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the current time;

[0008] Based on the driving risk between the target vehicle and other vehicles in the road condition at the current moment, an early warning is issued to the target vehicle in the road condition at the current moment.

[0009] According to one aspect of an embodiment of the present disclosure, a data processing device is disclosed, the device comprising:

[0010] A sending module, configured to send first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time to a server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and second road condition information including vehicle information of other vehicles;

[0011] A first acquisition module is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the target time based on the driving risk between each vehicle in the road condition at the target time;

[0012] A second acquisition module is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the current moment based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment;

[0013] The warning module is configured to warn the target vehicle in the current road condition based on the driving risk between the target vehicle and other vehicles in the current road condition.

[0014] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0015] Sending the identification information of the target vehicle to the server, so that the server determines the driving risk between the target vehicle and other vehicles in the road condition at the target time from the driving risks between the vehicles in the road condition at the target time based on the identification information;

[0016] The driving risk between the target vehicle and other vehicles in the road condition at the target time is obtained from the server.

[0017] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0018] Acquire the driving risk between each vehicle in the road condition at the target time from the server;

[0019] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is obtained from the driving risks between the vehicles in the road condition at the target moment based on the identification information of the target vehicle.

[0020] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0021] Determine the time difference between the target time road condition and the current time road condition;

[0022] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0023] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0024] Acquire a vehicle state change amount of the target vehicle within the time difference;

[0025] Based on the vehicle state change, obtaining the relative driving risk of the target vehicle within the time difference;

[0026] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0027] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0028] Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, the Taylor formula is expanded with a preset order, wherein the zero-order expansion term of the expanded Taylor formula is used to characterize the driving risk between the target vehicle and other vehicles in the road condition at the target time, and the first-order expansion term of the expanded Taylor formula is used to characterize the relative driving risk;

[0029] The relative driving risk is substituted into the expanded Taylor formula, and the obtained value of the expanded Taylor formula is determined as the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0030] According to one aspect of an embodiment of the present disclosure, a data processing method is disclosed, the method comprising:

[0031] Acquire first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time;

[0032] Acquiring second road condition information including vehicle information of other vehicles;

[0033] The driving risks between the vehicles in the road condition at the target time are determined based on the first road condition information and the second road condition information, so that the vehicle end warns the target vehicle based on the driving risks between the vehicles in the road condition at the target time.

[0034] According to one aspect of an embodiment of the present disclosure, a data processing device is disclosed, the device comprising:

[0035] A third acquisition module is configured to acquire first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time;

[0036] A fourth acquisition module is configured to acquire second road condition information including vehicle information of other vehicles;

[0037] The determination module is configured to determine the driving risks between each vehicle in the road condition at the target time based on the first road condition information and the second road condition information, so that the vehicle end can warn the target vehicle based on the driving risks between each vehicle in the road condition at the target time.

[0038] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0039] Acquire first time information corresponding to the first road condition information;

[0040] Acquire second time information corresponding to the second road condition information;

[0041] Based on the time difference between the second time information and the first time information, the vehicle information of the other vehicle is corrected to obtain the vehicle information of the other vehicle corresponding to the road condition at the target time;

[0042] Based on the vehicle information of the target vehicle and the vehicle information of the other vehicles corresponding to the road condition at the target time, the driving risk between the vehicles in the road condition at the target time is determined.

[0043] According to one aspect of an embodiment of the present disclosure, a data processing electronic device is disclosed, comprising: a memory storing computer-readable instructions; and a processor reading the computer-readable instructions stored in the memory to execute any one of the methods described in the above claims.

[0044] According to one aspect of an embodiment of the present disclosure, a computer program medium is disclosed, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method described in any one of the above claims.

[0045] In the disclosed embodiment, the server processes the road condition information and preliminarily obtains the driving risk between each vehicle, and then the vehicle side can determine the driving risk between the target vehicle and other vehicles based on the preliminarily obtained driving risk between each vehicle and issue an early warning to the target vehicle. This method avoids the vehicle side receiving the road condition information transmitted in a streaming manner from the server, and also avoids the vehicle side from incorrectly determining the driving risk between the target vehicle and other vehicles due to receiving confusing road condition information, thereby improving the accuracy of vehicle early warning.

[0046] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0047] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0049] Figure 1 A basic system architecture diagram according to an embodiment of the present disclosure is shown.

[0050] Figure 2 A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0051] Figure 3 A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0052] Figure 4 The complete processing flow according to one embodiment of the present disclosure is shown.

[0053] Figure 5 A block diagram of a data processing device according to an embodiment of the present disclosure is shown.

[0054] Figure 6 A block diagram of a data processing device according to an embodiment of the present disclosure is shown.

[0055] Figure 7 A hardware diagram of a data processing electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete and the concepts of the example embodiments will be fully conveyed to those skilled in the art. The accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0057] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0058] Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0059] The disclosed embodiment proposes a data processing method, which relates to the field of intelligent transportation in the field of cloud Internet of Things, and is used to improve the accuracy of vehicle warning.

[0060] Among them, the Internet of Things (IOT) refers to the ubiquitous connection between things and people through various devices and technologies such as information sensors, radio frequency identification technology, global positioning system, infrared sensor, laser scanner, etc., to achieve intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunications network, etc., which allows all ordinary physical objects that can be independently addressed to form an interconnected network.

[0061] Cloud IOT aims to connect the information sensed and instructions received by sensor devices in traditional IoT to the Internet, truly realize networking, and achieve massive data storage and computing through cloud computing technology. Due to the characteristics of IoT, objects are connected to each other and the current operating status of each "object" is perceived in real time. A large amount of data information will be generated in this process. How to aggregate this information and how to filter out useful information from the massive information to support decision-making for subsequent development have become key issues affecting the development of IoT. Physical cloud based on cloud computing and cloud storage technology has therefore become a strong support for IoT technology and applications.

[0062] Figure 1 The basic system architecture of an embodiment of the present disclosure is shown.

[0063] In this embodiment, information is transmitted between the vehicle warning system in the vehicle 10 and the server 20 via a transmission network. The server 20 may be an independent physical server, or a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The vehicle warning system may be integrated into the terminal of the driver of the vehicle, such as a small program of the terminal or a map application of the terminal. Among them, the terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server 20 may be directly or indirectly connected via wired or wireless communication, and this application is not limited thereto. The transmission network may be a 4G network or a 5G network established by a 4G (the 4th generation mobile communication technology, fourth generation mobile communication technology) base station or a 5G (the 5th generation mobile communication technology, fifth generation mobile communication technology) base station.

[0064] Specifically, when each vehicle 10 is driving on the road, the server 20 obtains road condition information including vehicle information (e.g., vehicle speed, vehicle acceleration) of the vehicle 10. The server 20 can actively detect and store the road condition information including vehicle information of the vehicle 10. When the server receives a driving risk acquisition request from the vehicle warning system in the target vehicle, the server preliminarily calculates the preliminary driving risk between each vehicle based on the stored road condition information, and sends the preliminary driving risk to the vehicle warning system, and then the vehicle warning system corrects the preliminary driving risk on this basis and warns the target vehicle based on the corrected driving risk.

[0065] The server 20 can also continuously receive road condition information including vehicle information of the vehicle 10 in real time. When the server receives a driving risk acquisition request from the vehicle warning system in the target vehicle, the server calculates the preliminary driving risk between each vehicle based on the currently received road condition information, and sends the preliminary driving risk to the vehicle warning system. The vehicle warning system then corrects the preliminary driving risk on this basis and warns the target vehicle based on the corrected driving risk.

[0066] It should be noted that this embodiment only exemplarily illustrates the basic system architecture used in an embodiment of the present disclosure, and should not limit the functions and scope of use of the present disclosure. In specific applications, the composition and structure of the system architecture can be adjusted according to needs. For example: the composition of the system architecture can also include road test equipment for collecting information (for example: ultrasonic testing equipment and meteorological monitoring equipment installed on the roadside), and the server 20 can obtain road condition information containing vehicle information of the vehicle 10 from the road test equipment, and can also obtain road condition information containing vehicle information of the vehicle 10 from the cooperation between the vehicle warning system and the road test equipment.

[0067] Figure 2 The data processing method of an embodiment of the present disclosure is shown, and the vehicle early warning system located at the vehicle end in the field of intelligent transportation is exemplarily used as the execution subject. The method includes:

[0068] Step S310: sending first road condition information including vehicle information of the target vehicle and used to characterize the road condition at the target time to a server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and second road condition information including vehicle information of other vehicles;

[0069] Step S320, based on the driving risks between the various vehicles in the road condition at the target time, obtaining the driving risks between the target vehicle and other vehicles in the road condition at the target time;

[0070] Step S330, based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the current moment;

[0071] Step S340: Based on the driving risk between the target vehicle and other vehicles in the current road condition, an early warning is issued to the target vehicle in the current road condition.

[0072] In the disclosed embodiment, the server processes the road condition information and preliminarily obtains the driving risk between each vehicle, and then the vehicle side can determine the driving risk between the target vehicle and other vehicles based on the preliminarily obtained driving risk between each vehicle and issue an early warning to the target vehicle. This method avoids the vehicle side receiving the road condition information transmitted in a streaming manner from the server, and also avoids the vehicle side from incorrectly determining the driving risk between the target vehicle and other vehicles due to receiving confusing road condition information, thereby improving the accuracy of vehicle early warning.

[0073] In the disclosed embodiment, vehicle information mainly refers to information related to vehicle driving, including vehicle speed and vehicle acceleration.

[0074] Road condition information mainly refers to information that describes the impact on vehicle driving, including road information and weather information. Specifically, road information may include relevant information such as road friction coefficient and road slope; weather information may include relevant information such as visibility and wind speed.

[0075] Driving risk mainly refers to the risk of an accident that may occur during driving. The driving risk between two vehicles can be measured by the probability of collision or the potential intensity of collision between the two vehicles.

[0076] In the disclosed embodiment, the server obtains road condition information including vehicle information of each vehicle, wherein the road condition information including vehicle information of each vehicle obtained by the server usually does not represent the same road condition.

[0077] For example, the traffic information obtained by the server, which includes the vehicle information of each vehicle, is as follows: "the vehicle information and traffic information of vehicle A at 15:03:00", "the vehicle information and traffic information of vehicle B at 15:03:02", and "the vehicle information and traffic information of vehicle C at 15:02:57".

[0078] In the disclosed embodiment, during the driving of the target vehicle, the vehicle warning system obtains vehicle information of the target vehicle and road condition information used to characterize the road condition at the target time where the target vehicle is located, and then sends first road condition information including the vehicle information and the road condition information to a server. The server then determines the driving risks between each vehicle in the road condition at the target time based on the first road condition information and the second road condition information including the vehicle information of other vehicles.

[0079] For example, the target vehicle is vehicle A. At 15:03:00, the vehicle warning system obtains the vehicle information of vehicle A and the road condition information at that time, and then sends the first road condition information "vehicle information and road condition information of vehicle A at 15:03:00" to the server. Then, based on the first road condition information, the second road condition information "vehicle information and road condition information of vehicle B at 15:03:02" and the second road condition information "vehicle information and road condition information of vehicle C at 15:02:57", the server determines the driving risks between vehicles A, B and C in the road conditions at 15:03:00.

[0080] In one embodiment, based on the driving risks between the vehicles in the road condition at the target time, the driving risks between the target vehicle and other vehicles in the road condition at the target time are obtained, including:

[0081] Sending the identification information of the target vehicle to the server, so that the server determines the driving risk between the target vehicle and other vehicles in the road condition at the target time from the driving risks between the various vehicles in the road condition at the target time based on the identification information;

[0082] The driving risk between the target vehicle and other vehicles in the road condition at the target time is obtained from the server.

[0083] In this embodiment, the server is responsible for determining the driving risk between the target vehicle and other vehicles from the driving risks between various vehicles.

[0084] Specifically, in this embodiment, the vehicle warning system sends the identification information of the target vehicle to the server, and the server determines the driving risk between the target vehicle and other vehicles in the road condition at the target time from the driving risks between each vehicle in the road condition at the target time based on the identification information, and then the vehicle warning system obtains the driving risk between the target vehicle and other vehicles in the road condition at the target time from the server.

[0085] For example, the server has determined the driving risks between vehicles A, B and C in the road conditions at 15:03:00. The vehicle warning system sends the identification information of the target vehicle A to the server, so that the server determines the driving risk between vehicles A and B and the driving risk between vehicles A and C in the road conditions at 15:03:00 based on the identification information of vehicle A. Thus, the vehicle warning system obtains the driving risk between vehicles A and B and the driving risk between vehicles A and C in the road conditions at 15:03:00 from the server.

[0086] The advantage of this embodiment is that the server locates the driving risk between the target vehicle and other vehicles from the driving risks between various vehicles, thereby ensuring the speed of positioning.

[0087] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0088] In one embodiment, based on the driving risks between the vehicles in the road condition at the target time, the driving risks between the target vehicle and other vehicles in the road condition at the target time are obtained, including:

[0089] Obtaining from the server the driving risks between the vehicles in the road conditions at the target time;

[0090] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is obtained from the driving risks between the various vehicles in the road condition at the target moment based on the identification information of the target vehicle.

[0091] In this embodiment, the vehicle warning system is responsible for determining the driving risk between the target vehicle and other vehicles from the driving risks between various vehicles.

[0092] Specifically, in this embodiment, the vehicle warning system obtains the driving risks between each vehicle in the road condition at the target time from the server, and then the vehicle warning system determines the driving risks between the target vehicle and other vehicles in the road condition at the target time from the driving risks between each vehicle in the road condition at the target time based on the identification information of the target vehicle.

[0093] The advantage of this embodiment is that the vehicle warning system locates the driving risk between the target vehicle and other vehicles from the driving risks between various vehicles, thereby reducing the processing burden of the server.

[0094] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0095] In one embodiment, based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the current time includes:

[0096] Determine the time difference between the target time and the current time;

[0097] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0098] In this embodiment, the vehicle warning system determines the time difference between the road condition at the target time and the road condition at the current time, and then corrects the driving risk between the target vehicle and other vehicles in the road condition at the target time based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current time.

[0099] It should be noted that this embodiment is only an exemplary description and should not limit the functions and scope of use of the present disclosure. It is understandable that, according to application needs, in one embodiment, the candidate driving risk interval where the driving risk between the target vehicle and other vehicles in the road condition at the target moment is located can be determined according to the preset driving risk floating interval, and then the driving risk between the target vehicle and other vehicles in the road condition at the current moment can be determined from the candidate driving risk interval.

[0100] In one embodiment, the driving risk between the target vehicle and other vehicles in the road condition at the target time is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current time, including:

[0101] Obtaining a vehicle state change of the target vehicle within the time difference;

[0102] Based on the vehicle state change, obtaining the relative driving risk of the target vehicle within the time difference;

[0103] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0104] In this embodiment, the vehicle warning system obtains the vehicle state change of the target vehicle within the time difference; then obtains the relative driving risk of the target vehicle within the time difference based on the vehicle state change; then corrects the driving risk between the target vehicle and other vehicles in the road condition at the target moment based on the relative driving risk, and obtains the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0105] Among them, it can be understood that as time goes by, the vehicle state (for example, vehicle speed, vehicle acceleration) of the vehicle during driving will usually change to a certain extent; the change in vehicle state will correspondingly bring about a change in driving risk, and the driving risk of this part of the change is the relative driving risk of the target vehicle within the time difference.

[0106] For example: the target time road condition is the road condition at 15:03:00; the current time road condition is the road condition at 15:03:05; the target vehicle is vehicle A. The vehicle warning system obtains the vehicle speed change and the vehicle acceleration change of vehicle A during the period from 15:03:00 to 15:03:05, and the vehicle warning system determines the relative driving risk of vehicle A during the period from 15:03:00 to 15:03:05 based on the vehicle speed change and the vehicle acceleration change, and then corrects the driving risk between vehicle A and other vehicles in the road condition at 15:03:00 based on the relative driving risk, thereby obtaining the driving risk between vehicle A and other vehicles in the road condition at 15:03:05.

[0107] It should be noted that this embodiment is only an exemplary description and should not limit the functions and scope of use of the present disclosure. It is understandable that, according to application needs, in one embodiment, the driving risk between the target vehicle and other vehicles in the road condition at the target time can be directly corrected linearly in proportion to the time difference based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current time.

[0108] In one embodiment, based on the vehicle state change, the relative driving risk of the target vehicle within the time difference is obtained, including: taking the vehicle state change and the road condition at the target time as inputs of a preset driving risk model, and determining the relative driving risk based on the output of the driving risk model.

[0109] In this embodiment, the vehicle warning system uses the vehicle state change and the road condition at the target time as inputs to a preset driving risk model, and then determines the driving risk corresponding to the vehicle state change based on the output of the driving risk model, that is, the relative driving risk of the target vehicle within the time difference.

[0110] Among them, the driving risk model can evaluate the corresponding driving risk of the vehicle state change in the road condition at the target time based on the gravitational field model (for example: based on the gravitational field theory, evaluate the driving risk of the target vehicle in the road condition at the target time), so as to output the relative driving risk; the vehicle state change in the road condition at the target time can be evaluated based on the Doppler effect model (for example: regard the vehicle in the road condition as a moving wave particle, regard the vehicle collision as the encounter of wave particles, and then evaluate the driving risk of the vehicle in the road condition at the target time based on the Doppler effect), so as to output the relative driving risk; the vehicle state change in the road condition at the target time can also be evaluated based on the driver's driving behavior portrait (for example: based on the driver's driving behavior habits, evaluate the driving risk of the vehicle in the road condition at the target time), so as to output the relative driving risk.

[0111] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0112] In one embodiment, the driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment, including:

[0113] Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, the Taylor formula is expanded with a preset order, wherein the zero-order expansion term of the expanded Taylor formula is used to characterize the driving risk between the target vehicle and other vehicles in the road condition at the target time, and the first-order expansion term of the expanded Taylor formula is used to characterize the relative driving risk;

[0114] The relative driving risk is substituted into the expanded Taylor formula, and the obtained value of the expanded Taylor formula is determined as the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0115] In this embodiment, the vehicle warning system is based on relative driving risk and corrects the driving risk between the target vehicle and other vehicles in the road condition at the target time multiple times according to the multi-order expanded Taylor formula.

[0116] Specifically, the preset order is recorded as n, the driving risk between the target vehicle and other vehicles in the road condition at the target moment is recorded as f(T1), the time difference between the road condition at the target moment and the road condition at the current moment is recorded as t1, and the driving risk between the target vehicle and other vehicles in the road condition at the current moment is recorded as f(T1+t1).

[0117] According to Taylor's theorem, Among them, f′(T1)*t1 is the relative driving risk of the target vehicle within the time difference between the road condition at the target time and the road condition at the current time.

[0118] The advantage of this embodiment is that the driving risk between the target vehicle and other vehicles in the road condition at the target time is corrected multiple times based on the expanded Taylor formula, thereby improving the accuracy of the correction.

[0119] It should be noted that this embodiment is only an exemplary description and should not limit the functions and scope of use of the present disclosure. It is understandable that in one embodiment, the relative driving risk can also be directly superimposed on the driving risk between the target vehicle and other vehicles in the road conditions at the target time, so as to obtain the driving risk between the target vehicle and other vehicles in the road conditions at the current time.

[0120] In the disclosed embodiment, the vehicle warning system at the vehicle end obtains the driving risk between the target vehicle and other vehicles in the current road condition, and then issues a warning to the target vehicle in the current road condition based on the risk. In this process, the vehicle warning system does not need to receive the road condition information transmitted in a streaming manner from the server, thereby avoiding possible damage during the transmission of the road condition information to the vehicle warning system, thereby improving the accuracy of the warning.

[0121] The following is a brief description of the main implementation process of the server-side processing of traffic information.

[0122] Figure 3 The data processing method of an embodiment of the present disclosure is shown, and exemplarily a server cooperating with a vehicle warning system on a vehicle side in the field of intelligent transportation is used as an execution subject. The method includes:

[0123] Step S410, obtaining first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time;

[0124] Step S420, obtaining second road condition information including vehicle information of other vehicles;

[0125] Step S430: determine the driving risks between each vehicle in the road condition at the target time based on the first road condition information and the second road condition information, so that the vehicle-side warns the target vehicle based on the driving risks between each vehicle in the road condition at the target time.

[0126] In the disclosed embodiment, the server is mainly used to obtain road condition information including vehicle information of each vehicle, and determine the driving risk between each vehicle in the road condition at the target time based on the obtained road condition information. The server can determine the driving risk between each vehicle in the road condition at the target time based on the obtained road condition information based on a preset driving risk model (for example: a driving risk model based on a gravitational field model; a driving risk model based on a Doppler effect model; a driving risk model based on a driver's driving behavior profile).

[0127] In one embodiment, determining the driving risk between the vehicles in the road condition at the target time based on the first road condition information and the second road condition information includes:

[0128] Acquire first time information corresponding to the first road condition information;

[0129] Acquire second time information corresponding to the second road condition information;

[0130] Based on the time difference between the second time information and the first time information, the vehicle information of the other vehicle is corrected to obtain the vehicle information of the other vehicle corresponding to the road condition at the target time;

[0131] Based on the vehicle information of the target vehicle and the vehicle information of the other vehicles corresponding to the road condition at the target time, the driving risk between the vehicles in the road condition at the target time is determined.

[0132] In this embodiment, the server unifies the vehicle information respectively contained in each road condition information in time, unifies it into the road condition at the target time, and then determines the driving risk between each vehicle in the road condition at the target time on this basis.

[0133] Specifically, the server uses the first time information of the first road condition information used to characterize the road condition at the target moment as a reference to determine the time difference between the second time information of the second road condition information of other vehicles and the first time information, and then corrects the vehicle information of other vehicles based on the time difference to obtain the vehicle information of other vehicles in the road condition at the target moment, and then determines the driving risks between each vehicle in the road condition at the target moment on this basis.

[0134] For example, the traffic information obtained by the server, which includes the vehicle information of each vehicle, is as follows: "the vehicle information and traffic information of vehicle A at 15:03:00", "the vehicle information and traffic information of vehicle B at 15:03:02", and "the vehicle information and traffic information of vehicle C at 15:02:57".

[0135] The server corrects the vehicle information of vehicle B based on the time difference from 15:03:00 to 15:03:02, and obtains the vehicle information of vehicle B in the road condition at 15:03:00; the server corrects the vehicle information of vehicle C based on the time difference from 15:02:57 to 15:03:00, and obtains the vehicle information of vehicle B in the road condition at 15:03:00. Based on this, the server determines the driving risks among vehicles A, B, and C in the road condition at 15:03:00.

[0136] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0137] It should be noted that, in one embodiment, when the road condition information of the vehicle side is also transmitted to the server in a streaming manner, the server may not specifically determine which vehicle the vehicle information respectively contained in each road condition information belongs to when determining the driving risk between each vehicle. In this case, when the server receives the driving risk acquisition request sent by the vehicle side, it uses the response time of receiving the acquisition request as a reference to unify the vehicle information respectively contained in each road condition information into the road condition at the response time. The road condition at the response time is usually offset from the road condition at the target time. In this case, after determining the driving risk between each vehicle in the road condition at the response time, the server can further perform delay compensation to determine the driving risk between each vehicle in the road condition at the target time.

[0138] For example: the road condition information on the vehicle side is transmitted to the server in a streaming manner, and the server receives the following information in sequence: at 15:02:57, vehicle C sent "the vehicle information and road condition information of vehicle C at 15:02:57"; at 15:03:00, vehicle A sent "the vehicle information and road condition information of vehicle A at 15:03:00"; at 15:03:02, vehicle B sent "the vehicle information and road condition information of vehicle B at 15:03:02"; at 15:03:04, vehicle A sent "a request to obtain the driving risks between each vehicle".

[0139] After receiving the acquisition request, the server does not specifically locate which road condition information contains the vehicle information of vehicle A, but directly determines the driving risk between the three vehicles in the road condition at 15:03:04 based on the received road condition information. Then the server locates which road condition information contains the vehicle information of vehicle A, determines that the time information corresponding to the road condition at the target time is 15:03:00, and on this basis determines the driving risk between the three vehicles in the road condition at 15:03:00.

[0140] The advantage of this embodiment is that, since the road condition information on the vehicle side is transmitted to the server in a streaming manner, the server will continuously receive new information; if the server needs to locate the vehicle information contained in each road condition information when determining the driving risk between each vehicle, the delay caused by this positioning process may cause the information currently received by the server to be overwritten by the information received later, resulting in the inability to accurately determine the driving risk between each vehicle. Therefore, this method ensures the accuracy of the server in determining the driving risk between each vehicle.

[0141] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0142] Figure 4 The diagram shows a brief and complete flow of data processing according to an embodiment of the present disclosure.

[0143] In this embodiment, the road test equipment monitors each vehicle traveling on the road and sends road condition information including vehicle information to the server.

[0144] After receiving the driving risk acquisition request sent by the target vehicle, the server preliminarily calculates the preliminary driving risk between any two vehicles based on the received road condition information; the server receives the identification information of the target vehicle sent by the target vehicle, and then determines the preliminary driving risk between the target vehicle and other vehicles from the preliminary driving risk between any two vehicles.

[0145] The server sends the timestamp corresponding to each road condition information and the preliminary driving risk between the target vehicle and other vehicles to the target vehicle.

[0146] The target vehicle corrects the initial driving risk between the target vehicle and other vehicles according to the timestamps corresponding to each road condition information, obtains the corrected driving risk between the target vehicle and other vehicles, and then issues an early warning to the target vehicle on this basis.

[0147] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0148] Table 1 below shows the effect comparison between an embodiment of the present disclosure and the prior art in terms of missed alarm rate and false alarm rate of early warning for moving vehicles in the field of intelligent transportation.

[0149] Among them, missed alarm refers to the situation that when the actual driving risk reaches the preset threshold during vehicle driving, an alarm should be issued but is not issued; false alarm refers to the situation that when the actual driving risk is less than the preset threshold during vehicle driving, an alarm is issued when it should not be issued.

[0150] Experimental sequence Existing technology false negative rate The false negative rate of this embodiment False alarm rate of existing technology The false alarm rate of this embodiment First experiment 29％ 9％ 18％ 8％ Second experiment 26％ 6％ 16％ 7％ The third experiment 23％ 6％ 16％ 5％ The fourth experiment 28％ 8％ 18％ 6％ The fifth experiment 27％ 7％ 17％ 5％ The sixth experiment 22％ 8％ 17％ 6％ The seventh experiment 29％ 9％ 19％ 7％ The eighth experiment 26％ 6％ 16％ 8％ The ninth experiment 29％ 9％ 18％ 7％ The tenth experiment 26％ 6％ 17％ 7％

[0151] Table 1

[0152] It can be seen from the data shown in this embodiment that, compared with the prior art, the method provided by the embodiment of the present disclosure can significantly reduce the missed alarm rate and false alarm rate in vehicle driving risk warning, and improve the accuracy of vehicle warning.

[0153] It should be noted that this embodiment is only an exemplary description and should not limit the function and scope of use of the present disclosure.

[0154] Figure 5 A data processing device according to an embodiment of the present disclosure is shown, the device comprising:

[0155] The sending module 510 is configured to send first road condition information including vehicle information of the target vehicle and used to characterize the road condition at the target time to the server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and the second road condition information including vehicle information of other vehicles;

[0156] A first acquisition module 520 is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the target time based on the driving risk between each vehicle in the road condition at the target time;

[0157] The second acquisition module 530 is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the current moment based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment;

[0158] The warning module 540 is configured to issue a warning to the target vehicle in the current road condition based on the driving risk between the target vehicle and other vehicles in the current road condition.

[0159] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0160] Sending the identification information of the target vehicle to the server, so that the server determines the driving risk between the target vehicle and other vehicles in the road condition at the target time from the driving risks between the vehicles in the road condition at the target time based on the identification information;

[0161] The driving risk between the target vehicle and other vehicles in the road condition at the target time is obtained from the server.

[0162] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0163] Acquire the driving risk between each vehicle in the road condition at the target time from the server;

[0164] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is obtained from the driving risks between the vehicles in the road condition at the target moment based on the identification information of the target vehicle.

[0165] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0166] Determine the time difference between the target time road condition and the current time road condition;

[0167] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0168] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0169] Acquire a vehicle state change amount of the target vehicle within the time difference;

[0170] Based on the vehicle state change, obtaining the relative driving risk of the target vehicle within the time difference;

[0171] The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0172] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0173] Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, the Taylor formula is expanded with a preset order, wherein the zero-order expansion term of the expanded Taylor formula is used to characterize the driving risk between the target vehicle and other vehicles in the road condition at the target time, and the first-order expansion term of the expanded Taylor formula is used to characterize the relative driving risk;

[0174] The relative driving risk is substituted into the expanded Taylor formula, and the obtained value of the expanded Taylor formula is determined as the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

[0175] Figure 6 A data processing device according to an embodiment of the present disclosure is shown, the device comprising:

[0176] The third acquisition module 610 is configured to acquire first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time;

[0177] The fourth acquisition module 620 is configured to acquire second road condition information including vehicle information of other vehicles;

[0178] The determination module 630 is configured to determine the driving risks between each vehicle in the road condition at the target time based on the first road condition information and the second road condition information, so that the vehicle side warns the target vehicle based on the driving risks between each vehicle in the road condition at the target time.

[0179] In an exemplary embodiment of the present disclosure, the device is configured as follows:

[0180] Acquire first time information corresponding to the first road condition information;

[0181] Acquire second time information corresponding to the second road condition information;

[0182] Based on the time difference between the second time information and the first time information, the vehicle information of the other vehicle is corrected to obtain the vehicle information of the other vehicle corresponding to the road condition at the target time;

[0183] Based on the vehicle information of the target vehicle and the vehicle information of the other vehicles corresponding to the road condition at the target time, the driving risk between the vehicles in the road condition at the target time is determined.

[0184] Reference below Figure 7 The data processing electronic device 70 according to an embodiment of the present disclosure is described. Figure 7 The data processing electronic device 70 shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0185] like Figure 7 As shown, the data processing electronic device 70 is in the form of a general-purpose computing device. The components of the data processing electronic device 70 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, and a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710).

[0186] The storage unit stores program codes, which can be executed by the processing unit 710, so that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention described in the description of the exemplary method described above in this specification. For example, the processing unit 710 can perform the following steps: Figure 2 The steps shown in .

[0187] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7201 and / or a cache storage unit 7202 , and may further include a read-only storage unit (ROM) 7203 .

[0188] The storage unit 720 may also include a program / utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0189] Bus 730 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0190] The data processing electronic device 70 may also communicate with one or more external devices 800 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the data processing electronic device 70, and / or may communicate with any device that enables the data processing electronic device 70 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 750. The input / output (I / O) interface 750 is connected to a display unit 740. In addition, the data processing electronic device 70 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 760. As shown, the network adapter 760 communicates with other modules of the data processing electronic device 70 via a bus 730. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the data processing electronic device 70, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0191] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0192] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is further provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the method described in the above method embodiment.

[0193] According to one embodiment of the present disclosure, a program product for implementing the method in the above method embodiment is also provided, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0194] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0195] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0196] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0197] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as JAVA, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0198] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0199] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0200] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0201] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A data processing method, characterized in that: Applied to a vehicle warning system located at a vehicle end, the method comprises: Sending first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time to a server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and second road condition information including vehicle information of other vehicles; Based on the driving risks between the various vehicles in the road condition at the target time, obtaining the driving risks between the target vehicle and other vehicles in the road condition at the target time; Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, obtaining the driving risk between the target vehicle and other vehicles in the road condition at the current time; Based on the driving risk between the target vehicle and other vehicles in the road condition at the current moment, an early warning is issued to the target vehicle in the road condition at the current moment.

2. The method according to claim 1, characterized in that Based on the driving risks between the vehicles in the road condition at the target time, the driving risks between the target vehicle and other vehicles in the road condition at the target time are obtained, including: Sending the identification information of the target vehicle to the server, so that the server determines the driving risk between the target vehicle and other vehicles in the road condition at the target time from the driving risks between the vehicles in the road condition at the target time based on the identification information; The driving risk between the target vehicle and other vehicles in the road condition at the target time is obtained from the server.

3. The method according to claim 1, characterized in that Based on the driving risks between the vehicles in the road condition at the target time, the driving risks between the target vehicle and other vehicles in the road condition at the target time are obtained, including: Acquire the driving risk between each vehicle in the road condition at the target time from the server; The driving risk between the target vehicle and other vehicles in the road condition at the target moment is obtained from the driving risks between the vehicles in the road condition at the target moment based on the identification information of the target vehicle.

4. The method according to claim 1, characterized in that: Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, the driving risk between the target vehicle and other vehicles in the road condition at the current time is obtained, including: Determine the time difference between the target time road condition and the current time road condition; The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

5. The method according to claim 4, characterized in that The driving risk between the target vehicle and other vehicles in the road condition at the target time is corrected based on the time difference to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current time, including: Acquire a vehicle state change amount of the target vehicle within the time difference; Based on the vehicle state change, obtaining a relative driving risk of the target vehicle within the time difference; The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

6. The method according to claim 5, characterized in that The driving risk between the target vehicle and other vehicles in the road condition at the target moment is corrected based on the relative driving risk to obtain the driving risk between the target vehicle and other vehicles in the road condition at the current moment, including: Based on the driving risk between the target vehicle and other vehicles in the road condition at the target time, the Taylor formula is expanded with a preset order, wherein the zero-order expansion term of the expanded Taylor formula is used to characterize the driving risk between the target vehicle and other vehicles in the road condition at the target time, and the first-order expansion term of the expanded Taylor formula is used to characterize the relative driving risk; The relative driving risk is substituted into the expanded Taylor formula, and the obtained value of the expanded Taylor formula is determined as the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

7. A data processing method, characterized in that: Applied to a server, the method comprises: Acquire first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time; Acquiring second road condition information including vehicle information of other vehicles; The driving risks between each vehicle in the road condition at the target moment are determined based on the first road condition information and the second road condition information, so that the vehicle side obtains the driving risk between the target vehicle and other vehicles in the road condition at the target moment based on the driving risk between each vehicle in the road condition at the target moment, obtains the driving risk between the target vehicle and other vehicles in the road condition at the current moment based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment, and issues an early warning to the target vehicle based on the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

8. The method according to claim 7, characterized in that Determining the driving risk between the vehicles in the road condition at the target time based on the first road condition information and the second road condition information includes: Acquire first time information corresponding to the first road condition information; Acquire second time information corresponding to the second road condition information; Based on the time difference between the second time information and the first time information, the vehicle information of the other vehicle is corrected to obtain the vehicle information of the other vehicle corresponding to the road condition at the target time; Based on the vehicle information of the target vehicle and the vehicle information of the other vehicles corresponding to the road condition at the target time, the driving risk between the vehicles in the road condition at the target time is determined.

9. A data processing device, characterized in that: The vehicle warning system configured at the vehicle end includes: A sending module, configured to send first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time to a server, so that the server determines the driving risk between each vehicle in the road condition at the target time based on the first road condition information and second road condition information including vehicle information of other vehicles; A first acquisition module is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the target time based on the driving risk between each vehicle in the road condition at the target time; A second acquisition module is configured to acquire the driving risk between the target vehicle and other vehicles in the road condition at the current moment based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment; The warning module is configured to warn the target vehicle in the current road condition based on the driving risk between the target vehicle and other vehicles in the current road condition.

10. A data processing device, characterized in that: Configured on a server, the device includes: A third acquisition module is configured to acquire first road condition information including vehicle information of a target vehicle and used to characterize the road condition at a target time; A fourth acquisition module is configured to acquire second road condition information including vehicle information of other vehicles; A determination module is configured to determine the driving risks between each vehicle in the road condition at the target moment based on the first road condition information and the second road condition information, so that the vehicle side obtains the driving risk between the target vehicle and other vehicles in the road condition at the target moment based on the driving risk between each vehicle in the road condition at the target moment, obtains the driving risk between the target vehicle and other vehicles in the road condition at the current moment based on the driving risk between the target vehicle and other vehicles in the road condition at the target moment, and issues an early warning to the target vehicle based on the driving risk between the target vehicle and other vehicles in the road condition at the current moment.

11. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1 to 8.

12. A data processing electronic device, characterized in that: Include: at least one processing unit; At least one storage unit stores program code, and when the program code is executed by the at least one processing unit, the at least one processing unit executes the method according to any one of claims 1 to 8.

13. A program product, characterized in that The method comprises a program code, and when the program code is executed, the method according to any one of claims 1 to 8 is implemented.

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