CN112631912A - Simulation method, device, equipment and storage medium based on Internet of vehicles - Google Patents

Abstract

The disclosure provides a simulation method, a simulation device, a simulation equipment and a storage medium based on Internet of vehicles, and relates to the fields of intelligent transportation, Internet of vehicles and vehicle road cooperation. The specific implementation scheme is as follows: sending simulation scene data to vehicle-mounted equipment in a scene of the Internet of vehicles; receiving operation result data obtained after the vehicle-mounted equipment carries out simulation based on the simulation scene data; and determining target data from the operation result data corresponding to the simulation scene data so as to perform data processing on the target data corresponding to the simulation scene data. This openly can save the test cost, promotes efficiency of software testing.

Description

Simulation method, device, device and storage medium based on Internet of Vehicles

technical field

The present disclosure relates to the technical field of data processing, and in particular, to the fields of intelligent transportation, Internet of Vehicles, and vehicle-road collaboration.

Background technique

The Internet of Vehicles technology can realize the exchange of information between the car and the outside world. Based on the Internet of Vehicles technology, the in-vehicle equipment can trigger the traffic light countdown, red light warning and green wave speed and other car networking scenarios to assist the driver in driving. For example, when the vehicle is driving to an intersection with traffic lights, the on-board device can trigger the traffic light countdown scene. If there is a truck in front of the vehicle blocking the line of sight, the driver can check the traffic light countdown on the on-board device to know the traffic light status at the intersection ahead. However, the current test of the Internet of Vehicles scenario still uses the real vehicle test, that is, the real vehicle is equipped with in-vehicle equipment to drive in the actual scene for testing.

SUMMARY OF THE INVENTION

The present disclosure provides a simulation method, device, device and storage medium based on the Internet of Vehicles.

According to an aspect of the present disclosure, a simulation method based on the Internet of Vehicles is provided, including:

Send simulated scene data to the in-vehicle device in the Internet of Vehicles scene;

receiving operation result data obtained by the in-vehicle device simulating based on the simulation scene data;

Target data is determined from the running result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data.

According to another aspect of the present disclosure, a simulation device based on the Internet of Vehicles is provided, comprising:

The simulation scene data sending unit is used to send the simulation scene data to the in-vehicle device in the car networking scene;

an operation result data receiving unit, configured to receive the operation result data obtained by the in-vehicle device simulating based on the simulation scene data;

A data processing unit, configured to determine target data from the running result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data.

According to another aspect of the present disclosure, there is provided an electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method in any of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method in any of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program that, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

According to another aspect of the present disclosure, a simulation system is provided, including a simulation tool and a cloud, wherein,

The cloud is used to execute the simulation method based on the Internet of Vehicles of the present disclosure;

The simulation tool is used to customize parameters according to the trajectory, generate simulation scene data, and send the simulation scene data to the cloud.

The technology according to the present disclosure can save the test cost and improve the test efficiency.

It should be understood that what is described in this section is not intended to identify key or critical features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.

Description of drawings

The accompanying drawings are used for better understanding of the present solution, and do not constitute a limitation to the present disclosure. in:

FIG. 1 is a schematic flowchart of the implementation of a simulation method based on the Internet of Vehicles according to an embodiment of the present application;

2 is a schematic diagram of a system architecture in a specific example of a simulation method based on the Internet of Vehicles according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of implementing a simulation method based on the Internet of Vehicles in a specific example according to an embodiment of the present application;

4 is a schematic structural diagram of a simulation device based on the Internet of Vehicles according to an embodiment of the present application;

5 is a block diagram of an electronic device used to implement the simulation method based on the Internet of Vehicles according to the embodiment of the present application;

FIG. 6 is a schematic structural diagram of a simulation system used to implement an embodiment of the present application.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

The solution of the present disclosure provides a simulation method based on the Internet of Vehicles, which is applied to the cloud. Specifically, FIG. 1 is a schematic diagram of the implementation flow of the simulation method based on the Internet of Vehicles according to an embodiment of the present disclosure. As shown in FIG. 1 , the method includes:

Step S101 : Send simulation scene data to the in-vehicle device in the Internet of Vehicles scene.

Step S102: Receive running result data obtained by the in-vehicle device simulating based on the simulation scene data;

Step S103: Determine target data from the operation result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data.

In an embodiment of the present disclosure, the simulation scene data may include information such as a starting intersection, a destination intersection, vehicle speed information, and yaw information. Based on the simulation scene data, the simulation scene can be divided into normal or abnormal simulation scenes such as acceleration, deceleration, traffic jam, and U-turn. The cloud, such as a cloud server, can save various simulation scene data. When it is necessary to control a certain in-vehicle device to simulate a certain scene, the cloud server can send the simulation scene data corresponding to the scene to the in-vehicle device. Based on the simulation scene data, the simulation is performed on the in-vehicle device, and the operation results corresponding to the simulation scene data are obtained, for example, the trajectory file, log information, screen recording file of the in-vehicle device, and the record of triggering the Internet of Vehicles scene, etc. After the vehicle-mounted device returns the operation result data to the cloud server, the cloud server can analyze the operation result data to determine the target data and perform data processing.

The disclosed solution can introduce simulation scene data into the vehicle-mounted equipment, so that the vehicle-mounted equipment can perform simulation based on the simulation scene data to obtain the simulation data without manual on-site road test, and the simulation scene can also cover normal or extreme scenes, so the test cost can be saved, Improve test efficiency.

Moreover, since the present disclosure conducts a simulation test on the in-vehicle device through the simulation scene data, if the in-vehicle device is in an abnormal state during the simulation process, the in-vehicle device can be re-tested based on the simulation scene data, which is convenient for locating and reproducing the vulnerability.

In addition, the solution of the present disclosure performs data processing based on the target data, which facilitates the discovery of functional deficiencies of the in-vehicle device and provides data support for the function evaluation of the in-vehicle device.

The executive body of the disclosed solution may be a server such as the cloud, and the cloud may be connected with the vehicle-mounted device to realize data transmission. In addition, the cloud also has data storage and processing, which can store simulation scene data and its corresponding operation result data, and Exception handling is performed.

In the solution of the present disclosure, the in-vehicle equipment may include intelligent rear-view mirrors, in-vehicle intelligent multimedia central control systems, vehicle-machine platforms, and other in-vehicle equipment with vehicle networking functions, or may be mobile phones, mobile tablets, etc. Internet-enabled mobile devices.

In a specific example of the solution of the present disclosure, determining target data from the operation result data corresponding to the simulation scene data includes: determining the following target data from the operation result data corresponding to the simulation scene data At least one of: after running the simulation scene data, the in-vehicle device normally triggers the relevant data of the preset operation; after running the simulation scene data, the in-vehicle device satisfies the trigger condition but does not trigger the relevant data of the preset operation The trigger condition is associated with the simulation scene data; after running the simulation scene data, the vehicle-mounted device triggers the relevant data of the preset operation under the condition that the trigger condition associated with the simulation scene data is not satisfied ; After running the simulation scene data, the in-vehicle device delays triggering the relevant data of the preset operation. In this way, for each simulation scene in the simulation process, the trigger condition is used to determine whether the in-vehicle device corresponds to triggering the preset operation correctly. For example, if the simulation scene does not meet the trigger condition, the in-vehicle device triggers the preset operation in the simulation scene. The data is extracted as target data for data processing in the cloud, so as to realize automatic evaluation and reduce manual workload.

It should be noted that, in the above specific example, the preset operation may include a preset operation triggered for an Internet of Vehicles scenario, and may include a driving information prompt or a driving operation. IoV scenarios can also be called V2X (vehicle to everything) scenarios. For example, IoV scenarios can include traffic light scenarios, green wave speed scenarios, or red light running scenarios. For example, the trigger condition of the red light running scenario is that the vehicle runs a red light, and the corresponding preset operation is the red light running warning. That is, when the vehicle passes through a signal-controlled intersection, the on-board device receives the location of the intersection and the real-time status of the traffic lights. If it is judged that the vehicle does not drive according to the signal light, it is considered that the current scene belongs to the Internet of Vehicles scene of running a red light, and the driver will perform a preset operation of warning for running a red light. For another example, the green wave speed scenario: the trigger condition is that when the vehicle drives to the intersection controlled by the signal light, after receiving the road data and the real-time status data of the signal light sent by the roadside unit, and determining the green wave speed of the intersection, Then, a preset operation that will give the driver a suggested speed range (ie, the green wave speed) is triggered, so that the vehicle can pass the signal intersection economically, comfortably, and without waiting for parking.

In a specific example of the solution of the present disclosure, performing data processing on the target data corresponding to the simulation scene data, the method includes: according to the target data corresponding to the simulation scene data, statistics the preset Trigger information corresponding to the operation. In this way, the automatic statistical trigger information reduces the amount of manual calculation and facilitates the visual display of test results.

In a specific example of the solution of the present disclosure, the trigger information includes at least one of a trigger rate, a false trigger rate, and a trigger delay of the preset operation. . In this way, the automatic calculation of multiple data indicators such as trigger rate, false trigger rate and trigger delay can clearly understand the triggering accuracy, error rate and trigger timeliness of the Internet of Vehicles scene, and at the same time, it is also for the effect of subsequent Internet of Vehicles scenarios. The assessment provides comprehensive data support.

In a specific example of the disclosed solution, performing data processing on the target data corresponding to the simulation scene data further includes at least one of the following: generating alarm information based on the target data; determining whether the vehicle-mounted device is based on the target data is in an abnormal state; sending at least part of the target data to the simulation tool, such as the running log of the in-vehicle device, so as to use the simulation tool to adjust the simulation scene data. In this way, the disclosed solution provides operations such as alarming target data (such as abnormal data such as false trigger or trigger timeout), and submitting abnormal state information (such as vulnerability information, bug) and log information that occur during the operation of the in-vehicle device. Therefore, on the one hand, it supports monitoring and alarming of simulation anomalies, as well as submitting abnormal status information and logs to achieve real-time monitoring. Reproduce to achieve loophole closed-loop effect.

In a specific example of the solution of the present disclosure, the simulation method based on the Internet of Vehicles further includes: receiving batch-generated simulation scene data from a simulation tool. The simulation tool can construct multiple simulation scene data through trajectory parameters such as the starting point, destination point, and driving speed set by the user, and send the simulation scene data to the cloud for storage. In this way, users can design a variety of simulation scenarios for simulation through simulation tools.

The solution of the present disclosure will be described in further detail below with reference to specific examples. Specifically, referring to the implementation system architecture diagram shown in FIG. 2 , the system in FIG. 2 includes the following structures:

Business Layer:

Business layer, including simulation tools. The business layer can include modules such as custom trajectory module, custom scene module, simulation module, result display module and bug closed loop. Examples of functions implemented by each module are as follows:

The customized trajectory module is used to receive customized trajectory parameters input by the user, and the customized trajectory parameters include full trajectory (ie, all trajectories), acceleration and deceleration, and the like. For example, the user can select a starting point and a destination point, formulate a driving route, and also set acceleration and deceleration operations during driving. Based on these custom trajectory parameters, simulation scenarios are generated in batches.

The customized scene module is used to receive customized information of the IoV scene to be simulated. For example, the custom scene module stores options for multiple connected car scenarios, such as traffic lights, green wave speed, and red light running. The user can select at least one of the multiple options as the IoV scenario to be simulated.

The simulation module is used to realize functions such as simulation playback, real-time simulation and 2D/3D (two-dimensional/three-dimensional) simulation. Among them, the simulation playback function may include that the user can play back the simulation process. Real-time simulation capabilities, which can include support for users to view real-time simulation processes. 2D/3D simulation, which can include presenting the simulation process in 2D or 3D.

The result display module is used for result display, such as displaying the preset operation trigger information about the Internet of Vehicles scenario that is counted by the cloud platform. The trigger information may include trigger rate, false trigger rate, and delay.

Bug closed-loop module for problem location and algorithm optimization verification. Among them, the problem location may include locating the problems occurring in the simulation process according to the bug information and log information submitted by the cloud platform. The algorithm optimization verification may include, after receiving the optimization algorithm information, optimizing the IoV scene and re-simulating the optimization algorithm information to verify the optimization effect of the algorithm.

Cloud platform:

Cloud platform, also known as cloud. The cloud includes a storage layer module, a simulation (Mock) module, a data processing module, a monitoring and alarm module, a vulnerability (Bug) management module and an effect evaluation module. Among them, the functions implemented by each module are as follows:

The storage layer module is used to store simulation scene data and its corresponding running result data, target data, and trigger information based on target data statistics. The storage layer module can include DB (data base) database and ES (ElasticSearch) database. Among them, ES database is a highly scalable and open source full-text search and analysis engine, which can store, search and analyze massive data in quasi-real time.

The simulation (Mock) module is used to obtain data related to simulation, such as traffic data and flash server data corresponding to the simulation scene. The traffic data may include real-time traffic data, such as traffic flow data, status information data of signal lights, and the like. The data of the flash transmission server includes the data of the flash transmission server corresponding to the in-vehicle device.

The data processing module is used to implement functions such as three-terminal comparison, video analysis, and log analysis. Among them, the three-terminal comparison may include comparing the data of the client, the server, and the cloud platform itself of the in-vehicle device to determine the target data. The video analysis can include analyzing the screen recording data during the simulation process of the in-vehicle equipment to determine the target data. The log analysis may include analyzing the log information in the simulation process of the vehicle-mounted device to determine the target data.

The monitoring and alarming module is used to monitor the problems in the simulation process of the on-board equipment and perform alarm processing. The scope of monitoring can include: push simulation scenarios refer to vehicle-mounted device timeouts, intersections with simulation exceptions, online data exceptions, etc.

The bug management module is used to discover the bugs in the simulation, create bug file information according to the discovered bugs, and verify the bugs.

The effect evaluation module is used to count trigger information such as trigger rate, false trigger rate, and accuracy rate according to the target data. Using these trigger information, the trigger effect of the in-vehicle device on the IoV scene can be evaluated.

Transport layer:

The transport layer is used to realize data transmission between the cloud platform and the mobile platform by using the Internet of Vehicles (IOV) long-link service.

mobile platform:

Mobile platform, including application (Application, APP), component layer and device layer. Among them, the application and component layers are installed and run on the device layer.

Applications include: receiving cloud tasks, executing track routes, monitoring Global Positioning System (GPS) updates, map track display, recording video, real-time display of results, video playback, data upload, etc. Based on these applications, the device layer can achieve: receive tasks sent by the cloud platform, the task content includes executing the simulation scene data; according to the simulation scene data in the task, execute the trajectory route, and update the driving position in real time; display the simulation scene on the display screen The map track corresponding to the data; perform screen recording operation on the content displayed on the display screen to obtain the recorded video; display the results of the Internet of Vehicles scene triggered by the simulation scene data in real time; play back the recorded video; and correspond to the running simulation scene data The running result data is uploaded to the cloud platform.

Component layer, including: Flash transmission software toolkit (Software Development Kit, SDK), map software toolkit and location management (Location Manager) and so on. Among them, the flash transmission software toolkit can be used to realize data transmission with the cloud platform. The map software toolkit can be used to obtain map information and lay the foundation for executing trajectory routes based on simulated scene data. Location management is used to realize the location of in-vehicle equipment.

The device layer may include in-vehicle devices that run simulated scene data, such as smart rearview mirrors, in-vehicle intelligent multimedia central control systems, or vehicle-machine platforms.

As shown in Figure 3, the implementation flowchart of this example, specifically, the process includes the following steps:

In the first step, the user can use the simulation tool to select a city that supports the IoV scenario based on the IoV scenario that needs to be simulated, and the subsequent simulation can trigger the IoV scenario. Then, based on the city selected by the user, the simulation tool provides a customized trajectory interface corresponding to the city for trajectory customization, and provides the triggering range in which the city can trigger the IoV scenario.

In the second step, with the help of simulation tools, the user selects the starting intersection, destination intersection, vehicle speed, etc. to construct various scenarios such as driver acceleration, traffic jam, U-turn, etc., which can be normal or abnormal. Then, based on the user's selection, the simulation tool generates simulation scenarios in batches and stores them in the cloud.

In the third step, with the help of simulation tools, the user can select the IoV scenarios that need to be simulated, such as one or more of traffic lights, green wave speed, and running red lights, and feed back the IoV scene information to the cloud.

In the fourth step, the cloud pushes these simulation scenarios to the simulation devices that need to be simulated (such as smart rearview mirrors), so that the simulation devices can simulate driving behaviors according to the simulation scenarios. Among them, the user can use the simulation tool to select the simulation device to be run, and the simulation tool feeds back the information of the simulation device to be run to the cloud.

The fifth step is to judge whether the simulation device can run the simulation scene normally. If the simulation can be performed, the simulation result is transmitted to the cloud; if the simulation cannot be performed, the simulation is ended.

In the sixth step, the cloud performs data processing according to the operation result data sent by the simulation device. Specifically, it judges each simulation scene that triggers the IoV scene, and judges whether the simulation scene satisfies the trigger condition, for example, whether the traffic light scene satisfies the trigger condition, Whether the green wave speed scene meets the trigger conditions or whether the red light scene meets the trigger conditions. If the trigger condition is not met, it is judged whether the simulation scene is triggered by mistake. If the trigger condition is met, the delay time of the car networking scene corresponding to the simulation scene is calculated, and according to the delay, it is judged whether the simulation scene has timed out to trigger the car. networking scene.

In the seventh step, the cloud will notify or alarm by email the simulated intersection data, trajectory file data, simulated scene data and abnormal information corresponding to the erroneously triggered IoV scene and the simulated scene with timeout trigger. In addition, the cloud can also automatically submit relevant vulnerability information and log information to the simulation tool based on the target data, so that users can easily learn and locate problems through research and development.

In the eighth step, the cloud can also generate a simulation report by using the target data extracted from the operation result data corresponding to the simulation scene data, and the statistical data processed based on the target data. The content of the simulation report includes triggering information such as the trigger rate of the Internet of Vehicles scene, the correct rate and delay of the Internet of Vehicles scene. In addition, the cloud can also send the simulation report to the simulation tool, and the simulation report can be visually displayed through the simulation tool.

In a specific example of the present disclosure, a simulation platform is used to replace manual testing, and at the same time, the functions of product effect evaluation, problem recurrence, problem location, and email alarm are realized.

In this way, the specific example of the present disclosure adopts a simulation scenario, covers normal or extreme scenarios, does not require testers to rent a car and travel, etc., the testing cost is low, and the testing period is shortened.

Moreover, the specific example of the present disclosure supports video playback, parameter recording, simulation results, and cloud recording of running trajectories and simulation logs for loopholes discovered by simulation, which can facilitate the location and reproduction of bugs and achieve a closed-loop effect.

In addition, the specific example of the present disclosure can record data such as simulation scenes and logs, and can also process the recorded data to determine whether the car networking scene is correctly triggered, whether there is a delay, and whether it is triggered incorrectly. It also supports operations such as monitoring alarms, submitting vulnerability information and log information to achieve the purpose of real-time monitoring.

The disclosed solution provides a simulation device based on the Internet of Vehicles, which is applied to the cloud. Specifically, as shown in FIG. 4 , it includes:

The simulation scene data sending unit 401 is used for sending simulation scene data to the vehicle-mounted device in the car networking scene;

an operation result data receiving unit 402, configured to receive the operation result data obtained by the in-vehicle device performing simulation based on the simulation scene data;

The data processing unit 403 is configured to determine target data from the running result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data.

In a specific example of the solution of the present disclosure, the data processing unit is specifically configured to: determine at least one of the following target data from the running result data corresponding to the simulation scene data:

After running the simulation scene data, the in-vehicle device normally triggers the relevant data of the preset operation;

After running the simulation scene data, the in-vehicle device satisfies the trigger condition but does not trigger the relevant data of the preset operation; the trigger condition is associated with the simulation scene data;

After running the simulation scene data, the in-vehicle device triggers the relevant data of the preset operation under the condition that the trigger condition associated with the simulation scene data is not satisfied;

After running the simulation scene data, the in-vehicle device delays triggering the relevant data of the preset operation.

In a specific example of the solution of the present disclosure, the apparatus further includes: a statistical unit, wherein the statistical unit is configured to count the triggers corresponding to the preset operations based on the target data corresponding to the simulation scene data information.

In a specific example of the solution of the present disclosure, the trigger information includes at least one of a trigger rate, a false trigger rate, and a trigger delay of the preset operation.

In a specific example of the solution of the present disclosure, the data processing unit is further configured to:

Generate alarm information based on target data;

Based on the target data, determine whether the in-vehicle equipment is in an abnormal state;

Sending at least part of the target data to a simulation tool to adjust the simulation scene data using the simulation tool.

In a specific example of the solution of the present disclosure, the apparatus further includes: a simulation scene data acquisition unit, wherein the simulation scene data acquisition unit is configured to receive simulation scene data generated from a simulation tool.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

5 shows a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 5 , the device 500 includes a computing unit 501 that can execute various functions according to a computer program stored in a read only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503 appropriate action and handling. In the RAM 503, various programs and data necessary for the operation of the device 500 can also be stored. The computing unit 501 , the ROM 502 , and the RAM 503 are connected to each other through a bus 504 . An input output (I/O) interface 505 is also connected to bus 504 .

Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk, etc. ; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

Computing unit 501 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 501 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as a simulation method based on the Internet of Vehicles. For example, in some embodiments, a car networking-based simulation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508 . In some embodiments, part or all of the computer program may be loaded and/or installed on device 500 via ROM 502 and/or communication unit 509 . When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the simulation method based on the Internet of Vehicles described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured by any other suitable means (eg, by means of firmware) to perform the simulation method based on the Internet of Vehicles.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, no limitation is imposed herein.

Referring to FIG. 6 , the solution of the present disclosure further provides a simulation system, including a simulation tool and a cloud.

The simulation tool 601 is used to customize parameters according to the trajectory, generate simulation scene data, and send the simulation scene data to the cloud.

The cloud 602 is used to execute the simulation method based on the Internet of Vehicles in the solution of the present disclosure. For the content of the simulation method, reference may be made to the description of the above-mentioned embodiment, which will not be repeated here.

In addition, the simulation system also includes an in-vehicle device 603, and the cloud and the in-vehicle device are connected.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.

Claims (16)

1. A simulation method based on the Internet of Vehicles, applied to the cloud, comprising: Send simulated scene data to the in-vehicle device in the Internet of Vehicles scene; receiving operation result data obtained by the in-vehicle device simulating based on the simulation scene data; Target data is determined from the running result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data. 2. The method according to claim 1, wherein determining target data from the running result data corresponding to the simulation scene data comprises: At least one of the following target data is determined from the running result data corresponding to the simulation scene data: After running the simulation scene data, the in-vehicle device normally triggers the relevant data of the preset operation; After running the simulation scene data, the in-vehicle device satisfies the trigger condition but does not trigger the relevant data of the preset operation; the trigger condition is associated with the simulation scene data; After running the simulation scene data, the in-vehicle device triggers the relevant data of the preset operation under the condition that the trigger condition associated with the simulation scene data is not satisfied; After running the simulation scene data, the in-vehicle device delays triggering the relevant data of the preset operation. 3. The method according to claim 2, wherein performing data processing on the target data corresponding to the simulation scene data comprises: counting the preset operations based on the target data corresponding to the simulation scene data corresponding trigger information. 4. The method according to claim 3, wherein the trigger information comprises at least one of a trigger rate, a false trigger rate and a trigger delay of the preset operation. 5. The method according to any one of claims 1 to 4, performing data processing on the target data corresponding to the simulation scene data, further comprising at least one of the following: Generate alarm information based on target data; Based on the target data, determine whether the in-vehicle equipment is in an abnormal state; Sending at least part of the target data to a simulation tool to adjust the simulation scene data using the simulation tool. 6. The method of any one of claims 1 to 4, wherein the method further comprises: Receive generated simulation scene data from a simulation tool. 7. A simulation device based on the Internet of Vehicles, applied to the cloud, comprising: The simulation scene data sending unit is used to send the simulation scene data to the in-vehicle device in the car networking scene; an operation result data receiving unit, configured to receive the operation result data obtained by the in-vehicle device simulating based on the simulation scene data; A data processing unit, configured to determine target data from the running result data corresponding to the simulation scene data, so as to perform data processing on the target data corresponding to the simulation scene data. 8. The apparatus according to claim 7, wherein the data processing unit is specifically configured to: determine at least one of the following target data from the operation result data corresponding to the simulation scene data: After running the simulation scene data, the in-vehicle device normally triggers the relevant data of the preset operation; After running the simulation scene data, the in-vehicle device satisfies the trigger condition but does not trigger the relevant data of the preset operation; the trigger condition is associated with the simulation scene data; After running the simulation scene data, the in-vehicle device triggers the relevant data of the preset operation under the condition that the trigger condition associated with the simulation scene data is not satisfied; After running the simulation scene data, the in-vehicle device delays triggering the relevant data of the preset operation. 9 . The apparatus according to claim 8 , wherein the apparatus further comprises: a statistical unit, wherein the statistical unit is configured to count the preset operations based on the target data corresponding to the simulation scene data. 10 . corresponding trigger information. 10 . The apparatus of claim 9 , wherein the trigger information includes at least one of a trigger rate, a false trigger rate, and a trigger delay of the preset operation. 11 . 11. The device according to any one of claims 7 to 10, wherein the data processing unit is further configured to: Generate alarm information based on target data; Based on the target data, determine whether the in-vehicle equipment is in an abnormal state; Sending at least part of the target data to a simulation tool to adjust the simulation scene data using the simulation tool. 12. The apparatus according to any one of claims 7 to 10, further comprising: a simulation scene data acquisition unit, wherein the simulation scene data acquisition unit is configured to receive simulation scene data generated from a simulation tool. 13. An electronic device, characterized in that, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any of claims 1-6 Methods. 14. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method of any one of claims 1-6. 15. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-6. 16. A simulation system, comprising a simulation tool and a cloud, wherein, The cloud is used to execute the simulation method based on the Internet of Vehicles according to any one of claims 1-6; The simulation tool is configured to customize parameters according to the trajectory to generate simulation scene data; and send the simulation scene data to the cloud.

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