Vehicle information processing method, equipment and medium

Abstract

The embodiment of the invention discloses a vehicle information processing method and device and a medium, and the method comprises the steps: responding to a received configuration file issued by a cloud, generating a scene triggering model based on the configuration file, and determining a target signal related to the scene triggering model; processing real-time bus data of the vehicle according to the scene triggering model and the target signal to obtain a processing result; and uploading the processing result to a cloud. The purpose that the vehicle end can efficiently and flexibly change the data acquisition strategy according to the requirements of the cloud end is achieved, and therefore the requirements of vehicle full-life-cycle data application such as vehicle development testing, mass production operation, user experience improvement and after-sales troubleshooting can be met.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle information processing, and in particular to a vehicle information processing method, device and medium. Background Art

[0002] As the level of automobile intelligence increases, smart connected vehicles, as mobile computing and data storage platforms, will generate massive amounts of real-time operating data. These data can generate great application value for big data analysis applications in various stages of the vehicle's life cycle, including research and development, testing, mass production operations, user experience improvement, after-sales troubleshooting, vehicle evaluation, and the evolution of intelligent driving functions.

[0003] The existing Internet of Vehicles data collection and upload technology has technical difficulties such as short storage cycle, weak edge computing capabilities, and insufficient data filtering and screening capabilities. This has resulted in the uploading of massive amounts of low-value data and a low available data rate, which in turn has led to frequent occupation of vehicle network bandwidth, increased traffic and storage costs, and at the same time, business departments are unable to quickly obtain data information traceability, seriously hindering the application and value mining of Internet of Vehicles data.

[0004] In view of this, this application is filed. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] The present application provides a vehicle information processing method, which has the beneficial effect of being able to efficiently and flexibly change the vehicle-side data collection strategy according to cloud requirements, thereby meeting the needs of various data applications.

[0007] In a first aspect, an embodiment of the present application provides a vehicle information processing method, which is applied to a vehicle side and includes the following steps: In response to receiving a configuration file sent from the cloud, generating a scene trigger model based on the configuration file, and determining a target signal related to the scene trigger model; The real-time bus data of the vehicle is processed according to the scenario trigger model and the target signal to obtain a processing result; and the processing result is uploaded to the cloud.

[0008] Furthermore, generating a scenario trigger model based on the configuration file includes: Determine a first character string carried by a first preset field in the configuration file; Identify the operators and operands in the first character string, and convert the identified operators and corresponding operands into mathematical expressions; The mathematical expression is determined as the scenario trigger model.

[0009] Furthermore, the real-time bus data of the vehicle is processed according to the scenario trigger model and the target signal to obtain a processing result, including: Acquiring a real-time value of the target signal based on real-time bus data of the vehicle; In response to the real-time value of the target signal satisfying the scenario trigger condition represented by the scenario trigger model, acquiring the real-time value of the setting signal based on the real-time bus data of the vehicle, and caching the real-time value of the setting signal; In response to the real-time value of the target signal not satisfying the scene trigger condition, the processing result is obtained based on the cached real-time value of the setting signal.

[0010] Furthermore, the processing result is obtained based on the real-time value of the cached setting signal, including: in response to determining that the scene trigger condition is not satisfied based on the real-time value of the target signal, processing the cached real-time value of the setting signal according to the processing requirements associated with the scene trigger model to obtain the processing result.

[0011] Furthermore, the real-time value of the target signal is obtained based on the real-time bus data of the vehicle, including: Decomposing the real-time bus data into a plurality of bus frames, and placing each of the bus frames into a bus queue of a target thread; A bus frame is obtained from the bus queue, and a real-time value of the target signal is obtained from the bus frame based on signal characteristic value description information, wherein the signal characteristic value description information is used to indicate the position of the real-time value of the target signal in the bus frame.

[0012] Furthermore, before obtaining the bus frame from the bus queue and obtaining the real-time value of the target signal from the bus frame based on the signal characteristic value description information, the method further includes: Determining the character content carried by the second preset field in the configuration file; The character content is converted into the signal characteristic value description information in a preset format.

[0013] In a second aspect, the embodiment of the present application further provides a vehicle information processing method, which is applied in the cloud, comprising: Receive model data; Converting the model data into a configuration file of a set format, wherein the configuration file is used for the vehicle to generate a scene trigger model and determine a target signal related to the scene trigger model; Sending the configuration file to the vehicle so that the vehicle processes the real-time bus data according to the scenario trigger model and the target signal to obtain a processing result; The processing result sent by the vehicle according to the configuration file is received.

[0014] Furthermore, the receiving of model data includes: receiving model data configured based on an atomic service icon displayed on an interactive interface, wherein the atomic service icon includes natural language description information for vehicle detection quantities.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle information processing method as described above.

[0016] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle information processing method as described above.

[0017] The vehicle information processing method disclosed in the embodiment of the present application, in response to receiving a configuration file sent from the cloud, generates a scene trigger model based on the configuration file, and determines the target signal related to the scene trigger model; processes the real-time bus data of the vehicle according to the scene trigger model and the target signal to obtain a processing result; uploads the processing result to the cloud, which can efficiently and flexibly change the vehicle-side data collection strategy according to the cloud demand, thereby meeting the needs of various data applications. The cloud demand can be conveyed through the configuration file, the vehicle-side generates a scene trigger model based on the configuration file, and determines the target signal related to the scene trigger model, and then processes the real-time bus data of the vehicle according to the scene trigger model and the target signal. The vehicle-side changes the data collection strategy according to the cloud demand, and realizes the purpose of "customizing" the digital data collection scheme according to the cloud demand, thereby meeting the purpose of various data application needs. Further, by processing the real-time bus data of the vehicle according to the scene trigger model and the target signal, the vehicle-side reduces the amount of useless data, ensures the data quality, reduces the size of the uploaded data, and can effectively improve the user's traffic consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flow chart of a vehicle information processing method provided in an embodiment of the present application Figure 1 ; Figure 2 A flow chart of a vehicle information processing method provided in an embodiment of the present application Figure 2 ; Figure 3 A schematic diagram of a vehicle-end component provided in an embodiment of the present application; Figure 4 A schematic diagram of the interaction between a vehicle and a cloud provided in an embodiment of the present application; Figure 5 A schematic diagram of processing CAN data provided in an embodiment of the present application; Figure 6 A schematic diagram of a scene of an emergency braking model provided in an embodiment of the present application; Figure 7 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] Figure 1 This is a flow chart of a vehicle information processing method on the vehicle side proposed in an embodiment of the present application. The vehicle information processing method can be executed by a first vehicle information processing device. The first vehicle information processing device can be implemented in the form of software and / or hardware. The first vehicle information processing device can be deployed in a vehicle controller.

[0023] like Figure 1 As shown, the vehicle information processing method includes the following steps: S110: In response to receiving a configuration file sent from the cloud, generating a scene trigger model based on the configuration file, and determining a target signal related to the scene trigger model.

[0024] It is understandable that the configuration file is a reflection of the cloud's needs, or in other words, the configuration file contains the cloud's needs. In some examples, the cloud can convey specific needs to the vehicle side through the configuration file, so that the vehicle side can efficiently and flexibly change the vehicle side's collection strategy according to the cloud's data collection needs. It can meet the needs of vehicle data applications throughout the life cycle, from vehicle development and testing, mass production operations, user experience improvement, and after-sales troubleshooting.

[0025] Among them, by generating a scene trigger model based on the configuration file and determining the target signal related to the scene trigger model, the vehicle side can directly identify the target signal and apply the scene trigger model. Therefore, it is beneficial to reduce the design complexity of the vehicle side service, improve the data processing efficiency of the vehicle side, and improve the platform differences between the vehicle and the cloud.

[0026] Optionally, the configuration file is obtained by converting the model data in the cloud, and the model data is configured by the user according to the needs. In order to reduce the user's learning cost, the model data supports configuration through the atomic service icon displayed on the interactive interface, that is, the user does not need to have an in-depth understanding of the bus signal required by the vehicle side, and only needs to use the atomic service icon to configure the model data according to specific needs, thereby reducing the user's learning cost. Among them, the atomic service icon refers to icon information that is easy for humans to understand, or the atomic service icon includes natural language description information for the vehicle detection quantity, such as "vehicle speed>20km / h" is a specific atomic service, and "mileage>10km" is another specific atomic service, which is easy for users to understand. Users do not need to have an in-depth understanding of specific bus signals (such as CAN signals), which reduces the learning cost and is conducive to improving the configuration efficiency of model data.

[0027] In order to facilitate the vehicle side to understand the model data configured by the user and improve the platform differences between the vehicle and the cloud, the model data configured by the user needs to be further converted into bus signals that can be understood by the vehicle side. Specifically, the user-configured model data can be converted into corresponding CAN signals in combination with the vehicle's DBC (CanBus DataBases, CAN bus database) file, and the CAN signals can be recorded in a file. The file that records the CAN signals is the configuration file.

[0028] To sum up, the vehicle information processing method provided by this application reduces the learning cost of users and improves the platform differences between the vehicle and the cloud. The vehicle side can accurately identify the intentions of the cloud side, directly parse the required signals, and the specific requirements for signal processing.

[0029] Exemplarily, the generating a scenario trigger model based on the configuration file includes: Determine a first character string carried by a first preset field in the configuration file; identify operators and operands in the first character string, and convert the identified operators and corresponding operands into mathematical expressions; determine the mathematical expression as the scenario trigger model.

[0030] In some examples, the configuration file may be a file in Json format, which includes a first preset field TriggerScene, which is used to represent the trigger scene. The specific trigger logic is stored in the entry corresponding to the first preset field TriggerScene. By identifying the first preset field, the first string representing the trigger logic can be located, the operator and operand in the first string can be identified, and the identified operator and corresponding operand can be converted into a mathematical expression. For example, the operator can be a greater than sign “>”, a less than sign “<”, an equal to sign “=”, or an ampersand “&”. The operand is the data after the operator, and the mathematical expression obtained after the conversion can be “vehicle speed>20km / h and brake pedal opening>50%”. This process involves segmenting the string of TriggerScene in the Json file, identifying operators and operands, and can be implemented using the muParser library (muParser library is a high-performance mathematical expression parsing library). Finally, the mathematical expression is determined as the scene trigger model.

[0031] The string of TriggerScene can be, for example, "BrakePedalScale>0.5&&VehicleSpeed>20&&Duration>1", and the corresponding scene trigger model is "vehicle speed>20km / h, brake pedal opening>50% and lasts for at least 1s". When the conditions represented by the scene trigger model are met, the scene is entered.

[0032] Wherein, determining the target signal related to the scenario trigger model includes: determining the character content related to the second preset field in the configuration file, and identifying the target signal from the character content. Specifically, in the configuration file, there is a second preset field canMsg, and the entry of the second preset field canMsg includes the required CANID.

[0033] S120: Process the real-time bus data of the vehicle according to the scenario trigger model and the target signal to obtain a processing result.

[0034] Exemplarily, the processing of the real-time bus data of the vehicle according to the scene trigger model and the target signal to obtain the processing result includes: obtaining the real-time value of the target signal based on the real-time bus data of the vehicle; in response to the real-time value of the target signal satisfying the scene trigger condition represented by the scene trigger model, obtaining the real-time value of the setting signal based on the real-time bus data of the vehicle, and caching the real-time value of the setting signal; in response to the real-time value of the target signal not satisfying the scene trigger condition, obtaining the processing result based on the cached real-time value of the setting signal. Specifically, obtaining the processing result based on the cached real-time value of the setting signal includes: processing the cached real-time value of the setting signal according to the processing requirements associated with the scene trigger model to obtain the processing result. The processing requirements associated with the scene trigger model can also be notified to the vehicle end through a configuration file.

[0035] Taking the emergency braking scenario as an example, the scenario trigger model is: vehicle speed>20km / h and brake pedal opening>50% and lasts for at least 1s. When the trigger conditions represented by the scenario trigger model are met, it means entering the scenario, and the real-time values ​​of the set signals are cached. The set signals include but are not limited to vehicle speed signals, brake signals, etc. When the above trigger conditions are not met, that is, when exiting the scene, the processing result is obtained based on the real-time values ​​of the cached set signals. For example, the processing result can be the braking distance, the maximum brake opening, and the duration of entering the scene, etc. After the vehicle side determines these data based on the cached signal values, it uploads them to the cloud.

[0036] The method of obtaining the real-time value of the target signal based on the real-time bus data of the vehicle includes: decomposing the real-time bus data into multiple bus frames, and placing each of the bus frames into the bus queue of the target thread; obtaining the bus frame from the bus queue, and obtaining the real-time value of the target signal from the bus frame based on the signal characteristic value description information, and the signal characteristic value description information is used to indicate the position of the real-time value of the target signal in the bus frame. Different scene trigger models correspond to different threads to ensure processing efficiency. The signal characteristic value description information is used to indicate the position of the real-time value of the target signal in the bus frame, and the signal characteristic value description information includes, for example: the bus ID, start bit, end bit, etc. of the target signal, which are used to convert the bus data into the actual vehicle data. By referring to the signal characteristic value description information, the actual vehicle data corresponding to the target signal can be parsed from the bus frame.

[0037] Correspondingly, before obtaining the bus frame from the bus queue and obtaining the real-time numerical value of the target signal from the bus frame based on the signal characteristic value description information, it also includes: determining the character content carried by the second preset field in the configuration file; converting the character content into the signal characteristic value description information in a preset format.

[0038] S130, uploading the processing result to the cloud.

[0039] In the vehicle information processing method disclosed in the embodiment of the present application, cloud demands can be conveyed through a configuration file, the vehicle side generates a scene trigger model based on the configuration file, and determines a target signal related to the scene trigger model, and then processes the vehicle's real-time bus data according to the scene trigger model and the target signal, thereby achieving the purpose of changing the vehicle-side data collection strategy according to cloud demands, thereby meeting various data application requirements. Furthermore, by processing the vehicle's real-time bus data according to the scene trigger model and the target signal, the vehicle side reduces the amount of useless data, ensures data quality, reduces the size of uploaded data, and can effectively improve the user's traffic consumption compared to blindly processing the vehicle's real-time bus data.

[0040] In some embodiments, the vehicle information processing method also includes: determining version information of the configuration file; comparing the version information of the configuration file with the version information of an existing configuration file on the vehicle side; in response to the version information of the configuration file being different from the version information of the existing configuration file on the vehicle side, updating the existing configuration file on the vehicle side, so that the vehicle correctly processes the relevant data according to the requirements of the configuration file and reports it to the cloud, thereby reducing the error rate, ensuring the accuracy of information processing, and improving the stability and reliability of information processing.

[0041] Figure 2 This is a flow chart of a vehicle information processing method in the cloud proposed in an embodiment of the present application. The vehicle information processing method can be executed by a second vehicle information processing device, and the second vehicle information processing device can also be implemented in the form of software and / or hardware. The second vehicle information processing device can be deployed in a cloud server.

[0042] like Figure 2 As shown, the vehicle information processing method includes the following steps: S210: Receive model data.

[0043] Specifically, model data configured based on an atomic service icon displayed on an interactive interface may be received, wherein the atomic service icon includes natural language description information for vehicle detection quantities.

[0044] In order to reduce the user's learning cost, the model data supports configuration through the atomic service icon displayed on the interactive interface, that is, the user does not need to have an in-depth understanding of the CAN signal required by the vehicle side, and only needs to use the atomic service icon to configure the model data according to specific needs, thereby reducing the user's learning cost. Among them, the atomic service icon refers to icon information that is easy for humans to understand, or in other words, the atomic service icon includes natural language description information for vehicle detection quantities, such as "vehicle speed>20km / h" is a specific atomic service, and "mileage>10km" is another specific atomic service, which is easy for users to understand. Users do not need to have an in-depth understanding of specific CAN signals, which reduces learning costs and is conducive to improving the configuration efficiency of model data.

[0045] S220, converting the model data into a configuration file in a set format, where the configuration file is used for the vehicle to generate a scene trigger model and determine a target signal related to the scene trigger model.

[0046] Optionally, the model data is converted into a configuration file of a set format according to a DBC file of a corresponding vehicle.

[0047] In order to facilitate the vehicle side to understand the model data configured by the user and improve the platform differences between the vehicle and the cloud, the model data configured by the user needs to be further converted into CAN signals that can be understood by the vehicle side. Specifically, the model data configured by the user can be converted into corresponding CAN signals in combination with the vehicle's DBC file, and the CAN signals can be recorded in a file. The file that records the CAN signals is the configuration file.

[0048] S230: Send the configuration file to the vehicle, so that the vehicle processes the real-time bus data according to the scenario trigger model and the target signal to obtain a processing result.

[0049] S240: Receive the processing result sent by the vehicle according to the configuration file.

[0050] The configuration file contains the specific requirements of the cloud. By conveying the specific requirements to the vehicle side in the form of a configuration file, the vehicle side can efficiently and flexibly change the vehicle side's collection strategy according to the data collection requirements of the cloud side. It can meet the needs of vehicle data application throughout the life cycle, from vehicle development and testing, mass production operations, user experience improvement, and after-sales troubleshooting.

[0051] In some implementations, the vehicle information processing method provided in this embodiment is called an edge computing technology solution. Multiple vehicle terminals share a cloud. For the cloud, the vehicle terminal belongs to the edge category. The edge computing technology solution uses the "cloud-vehicle terminal" business architecture for data collection. The overall core business process is: The cloud provides edge computing configuration, which can be remotely sent to the vehicle side to complete flexible edge computing model configuration. The vehicle side computing components should include a model-driven intelligent distributed architecture and an on-board edge computing platform to enable the synergy between edge computing and cloud processing, including an end-to-end framework for development, deployment, and operation. Figure 3 As shown, the vehicle-side components should include the following core services to realize computing functions: edge computing execution service, deployed in the in-vehicle domain controller, implements the parsing definition of the configuration file, and generates an independent algorithm running thread based on the configuration file. Each algorithm running thread receives relevant signals and then executes according to the calculation model combination. Edge computing management service, used to receive the configuration sent from the cloud, and update and delete existing configuration files based on the new configuration. When it is detected that an existing configuration file needs to be updated, this module controls all current algorithm running threads to stop, updates the existing configuration file, and then re-reads the configuration to control the edge computing execution service to restart. Network communication service: The vehicle-side screens signals and scenes according to cloud requirements, and sends relevant data to the cloud through network services.

[0052] like Figure 4 As shown in Figure 1, based on the overall implementation architecture, data flow types can be divided into control flow and data flow. The cloud interacts with the vehicle through network services. First, use the control flow to start, update, stop, and destroy the edge computing model configuration, such as Figure 4 In the control flow 11, 12, 13; then after the edge computing management service is started, the edge computing execution service will be reconfigured according to the configuration file, thereby opening the overall data flow link. The vehicle-side signal data will be screened and pre-processed by the edge computing execution service and then uploaded to the cloud.

[0053] The cloud side implements flexible configuration of the vehicle side by sending down configuration files. This solution focuses on data interaction between the vehicle and the cloud, as well as the vehicle side collecting CAN data from the underlying ECU (Electronic Control Unit), which can increase the compatibility of the vehicle side software with a variety of computing scenarios. The versatility of the solution can make the overall configuration process more flexible, and users can configure different edge computing solutions based on a variety of operators. The technical difficulty lies in vehicle-cloud interaction, and how the vehicle side can accurately identify the requirements sent by the cloud side in an environment with limited resources, convert them into algorithms that can process specific data, and then upload the relevant data to the cloud server based on the cloud needs. Because the data is screened, the amount of reported data is reduced, thereby achieving the purpose of occupying less network bandwidth and saving traffic.

[0054] To achieve the above purpose, first of all, the atomic service of edge computing execution service is defined between the car and the cloud (the single condition that constitutes the entry into the edge computing scenario is called the atomic service). The further explanation of the atomic service is as follows: vehicle speed, mileage and other items are atoms suitable for human understanding in edge computing, and they can be collectively referred to as atomic services in combination with their implementation conditions. For example, vehicle speed>20km / h, mileage>10km, etc. are different atomic services. The advantage of this is that the operator does not need to deeply understand the CAN signal required by the vehicle end, and can set it according to the actual edge computing trigger conditions, which reduces the learning cost of users.

[0055] The cloud will edit the corresponding atomic services according to the scene logic and combine them into edge computing configuration. Among them, the difficulty of implementation lies in how to isolate the platform differences between the car and the cloud, and enable the vehicle to accurately identify the configuration. To solve this difficulty, in the configuration stage, cloud operators can use existing atomic services according to needs in the interface, and perform graphical configuration in combination with trigger conditions. The backend will parse the atomic services into corresponding CAN data signals according to the DBC file of the vehicle model, form a configuration file, and send it to the vehicle for processing. The configuration file can use Json files and can be divided into the following parts: Model scene name - "modelName", representing the name of the model scene. Signal parsing method - used to parse the signal required by the model, which can be recorded through the preset field "canMsg", including CAN Id, the start bit and end bit of the signal in the CAN data, and the way to convert CAN data into actual vehicle data. Trigger scene model - used to indicate specific trigger conditions, which can be recorded through the preset field "TriggerScene", mainly a string representation of entering the model scene.

[0056] When the edge computing management service on the vehicle side receives the configuration file sent from the cloud, it first verifies whether the format of the configuration file is correct, then converts the character content related to the preset field "canMsg" in the configuration file into signal characteristic value description information that can be recognized by the vehicle side, and converts the character content related to the preset field "TriggerScene" into a scene trigger model, and provides the signal characteristic value description information and the scene trigger model to the edge computing service. The edge computing service processes the real-time bus data of the vehicle according to the scene trigger model and the signal characteristic value description information.

[0057] The vehicle-side bottom layer uses the general protocol Busmirror to collect Can messages forwarded from the gateway. First, the protocol header and data Data are parsed. The more important ones are the timestamp and data length, which can be used to verify the correctness of the data. Each data also contains two parts: the data header and the data. Based on the data header, the source and meaning of the message can be classified. Figure 5As shown, after receiving the CAN message 510, the edge computing management service will further decompose the message into a single CAN frame 520 and send it to the bus frame queue 530 in the current edge computing processing thread. Each thread has its own bus frame queue. If the signal required to enter the scene exists in the CAN data, the thread will obtain the signal in the CAN data, update the data in the signal buffer, and determine whether to enter or exit the scene based on the scene trigger model combined with the buffer data. If you enter the scene, it will continue to record the arrival of each signal and update the buffer data after the scene is triggered, which will serve as the historical data of entering the scene; when it is detected that you have exited the scene, the target data will be determined according to the cloud requirements in the edge computing scene, and timestamped, and the target data will be uploaded to the cloud server.

[0058] In order to more intuitively illustrate the implementation process of this solution, Figure 6 As shown, take the emergency braking scenario as an example. The scenario trigger model is: vehicle speed>20km / h and brake pedal opening>50% and lasts for at least 1s. The signal requirements are as follows: signal timestamp (Timestamp), vehicle brake pedal status bit (BrakePedalStatus), brake pedal opening ratio (1%~100%) (BrakePedalScale), brake pedal duration (Duration) and vehicle speed (VehicleSpeed).

[0059] According to the above scenarios and signal requirements, users configure the required atomic services in the cloud web interface. The cloud splits the corresponding signals required to enter the scene according to the vehicle DBC (Can Bus DataBases) file and the Json file specifications described in the configuration, and sends them to the vehicle-side platform. The character content used to describe the scene trigger model is as follows: BrakePedalScale>0.5&&VehicleSpeed>20&&Duration>1. The vehicle side first parses the corresponding signal, updates the configuration in the memory, and converts the character content into a scene trigger model, and then collects the data, and records the collection in the memory. When exiting the scene, the data required for the scene is processed and uploaded. For example, in an emergency braking scenario, when exiting the scene, the cloud usually needs to obtain the braking distance, maximum brake opening, and duration of entering the scene. After the vehicle side determines these data, it uploads them to the cloud.

[0060] The embodiment of the present application adjusts both the vehicle and the cloud according to actual usage requirements. By optimizing the trigger scenario conditions into a composition of multiple signals, it becomes an independent and reusable atomic service. This method increases the flexibility of cloud-side configuration. At the same time, it parses the character logic and computing semantics in the edge computing model into an actual algorithm model, which facilitates the vehicle-side to parse and execute different computing strategies, and supports multiple strategies to be sent to the vehicle-side and run simultaneously, which is the key to the rapid expansion of computing scenarios in the later stage.

[0061] Figure 7 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 7 , which shows a structural schematic diagram of an electronic device 500 suitable for implementing the embodiments of the present disclosure. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0062] like Figure 7 As shown, the electronic device 500 may include a processing device 501 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device 508 into a random access memory (RAM) to implement the method of the embodiment described in the present disclosure. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. The I / O interface 505 is also connected to the bus 504. The input device 506, the output device 507, the storage device 508, and the communication device 509 are all connected to the I / O interface 505.

[0063] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart, thereby implementing the vehicle information processing method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0064] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-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 of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer 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. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0065] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device executes the method steps in the present application.

[0066] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0067] 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 conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer 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 foregoing.

[0068] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0069] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and its core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the embodiments of this application, some improvements, modifications or changes can be made, and the above technical features can be combined in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the concept and technical solution of the invention to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A vehicle information processing method, applied to a vehicle side, characterized in that: include: In response to receiving a configuration file sent from the cloud, generating a scene trigger model based on the configuration file, and determining a target signal related to the scene trigger model; Processing the real-time bus data of the vehicle according to the scenario trigger model and the target signal to obtain a processing result; The processing results are uploaded to the cloud.

2. The vehicle information processing method according to claim 1, characterized in that: The generating a scenario trigger model based on the configuration file includes: Determine a first character string carried by a first preset field in the configuration file; Identify the operators and operands in the first character string, and convert the identified operators and corresponding operands into mathematical expressions; The mathematical expression is determined as the scenario triggering model.

3. The vehicle information processing method according to claim 1 or 2, characterized in that: The processing of the real-time bus data of the vehicle according to the scenario trigger model and the target signal to obtain a processing result includes: Acquiring a real-time value of the target signal based on real-time bus data of the vehicle; In response to the real-time value of the target signal satisfying the scenario trigger condition represented by the scenario trigger model, acquiring the real-time value of the setting signal based on the real-time bus data of the vehicle, and caching the real-time value of the setting signal; In response to the real-time value of the target signal not satisfying the scene trigger condition, the processing result is obtained based on the cached real-time value of the setting signal.

4. The vehicle information processing method according to claim 3, characterized in that: The obtaining of the processing result based on the cached real-time value of the setting signal comprises: According to the processing requirements associated with the scene trigger model, the cached real-time value of the setting signal is processed to obtain the processing result.

5. The vehicle information processing method according to claim 3, characterized in that: The step of acquiring the real-time value of the target signal based on the real-time bus data of the vehicle comprises: Decomposing the real-time bus data into a plurality of bus frames, and placing each of the bus frames into a bus queue of a target thread; A bus frame is obtained from the bus queue, and a real-time value of the target signal is obtained from the bus frame based on signal characteristic value description information, wherein the signal characteristic value description information is used to indicate the position of the real-time value of the target signal in the bus frame.

6. The vehicle information processing method according to claim 5, characterized in that: Before obtaining the bus frame from the bus queue and obtaining the real-time value of the target signal from the bus frame based on the signal characteristic value description information, the method further includes: Determining the character content carried by the second preset field in the configuration file; The character content is converted into the signal characteristic value description information in a preset format.

7. A vehicle information processing method, applied to the cloud, characterized in that: include: Receive model data; Converting the model data into a configuration file of a set format, wherein the configuration file is used for the vehicle to generate a scene trigger model and determine a target signal related to the scene trigger model; Sending the configuration file to the vehicle so that the vehicle processes the real-time bus data according to the scenario trigger model and the target signal to obtain a processing result; The processing result sent by the vehicle according to the configuration file is received.

8. The vehicle information processing method according to claim 7, characterized in that: The receiving model data comprises: Model data configured based on an atomic service icon displayed on an interactive interface is received, wherein the atomic service icon includes natural language description information for a vehicle detection quantity.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle information processing method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle information processing method as described in any one of claims 1 to 8 is implemented.

Images