Data acquisition method, electronic equipment, system, medium and computer program product

Abstract

The invention relates to the technical field of automobiles, and discloses a data acquisition method, electronic equipment, a system, a medium and a computer program product. In the method, when a first electronic device detects a first control signal at a first moment, the first electronic device responds to the first control signal and determines that a current data acquisition scene is a first scene. And executing a first data acquisition task corresponding to the first scene, and associating the acquired first data with the first scene to obtain second data. And the first electronic equipment sends the second data to the second electronic equipment. Wherein the generation of the first type control signal is related to the vehicle state change. Thus, the first electronic equipment can accurately collect data, the problems of excessive collected data and redundant collected data are effectively improved, and the first electronic equipment can transmit the data to the second electronic equipment; and data storage and analysis of the second electronic equipment are facilitated.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a data acquisition method, electronic equipment, system, medium and computer program product. Background Art

[0002] In the process of vehicle development and vehicle commissioning, in order to ensure that the vehicle can drive safely and all functions can be used normally, the vehicle side can collect data from the vehicle during development or use based on data collection technology, and upload the collected data such as production and development information data, signal data generated by vehicle driving, user behavior data, Internet ecological data, etc. to the cloud. The cloud management personnel will further analyze and manage the data. For example, by analyzing the data, it can be determined whether the vehicle has a fault and whether the vehicle's functions can be optimized. Summary of the invention

[0003] The present application provides a data collection method, electronic equipment, system, medium and computer program product.

[0004] In a first aspect, the present application provides a data acquisition method, which is applied to a first electronic device, and the method includes: detecting a first type of control signal at a first moment, wherein the generation of the first type of control signal is related to a change in a vehicle state, and the first type of control signal includes a first control signal; in response to the first control signal, determining that a current data acquisition scenario is a first scenario; executing a first data acquisition task corresponding to the first scenario, associating the collected first data with the first scenario to obtain second data; and sending the second data to a second electronic device.

[0005] In some embodiments, the first electronic device may be the vehicle terminal 10 mentioned below, and the second electronic device may be the cloud 20 mentioned below. The first data collection task may be sent from the second electronic device to the first electronic device.

[0006] The first electronic device detects the data corresponding to the first control signal (such as CAN signals, LIN signals and other vehicle type signals), calculates and outputs the first scene signal representing the scene in real time, and thus performs the data collection task according to the first scene signal. The collected first data is associated with the scene corresponding to the first scene signal to obtain the second data, effectively realizing accurate data collection. And the computing tasks and data collection tasks performed by the vehicle end can be configured through the second electronic device, which is conducive to obtaining the running status of the computing tasks and data collection tasks.

[0007] In this way, the first electronic device can accurately collect data, effectively improve the problem of excessive and redundant collected data, facilitate the first electronic device to transmit data to the second electronic device; and facilitate the second electronic device to store and analyze data.

[0008] In a possible implementation of the first aspect above, determining that the current data acquisition scene is the first scene includes: inputting data corresponding to the first control signal into a preset calculation algorithm execution module, and the calculation algorithm execution module outputs a first scene signal; based on the first scene signal, determining that the current data acquisition scene is the first scene; wherein the source of the first control signal includes a signal generated corresponding to an operation performed by a user, or a signal generated corresponding to a change in an operating state during the operation of a device controlled by the first electronic device.

[0009] In a possible implementation of the first aspect above, associating the collected first data with the first scene includes at least one of the following: adding a scene identifier corresponding to the first scene to the first data; and identifying a first matching relationship between the scene field in the first data and the first scene based on a preset matching relationship.

[0010] In a possible implementation of the first aspect above, the first data collection task includes collecting a vehicle range, a signal range, an effective time range, a collection cycle and an upload cycle, wherein the collected vehicle range includes at least one of the following: the vehicle model, the vehicle production date, and the vehicle data version number; the signal range represents the signal range of the first control signal, and the signal range includes the vehicle model signal and the Ethernet-based service signal.

[0011] In a possible implementation of the first aspect above, the method includes: detecting a second control signal included in the first type of control signal at a second moment; in response to the second control signal, determining that the current data acquisition scene is a second scene, and the second scene is different from the first scene; executing a second data acquisition task corresponding to the second scene, associating the collected third data with the second scene, and obtaining fourth data; and sending the fourth data to a second electronic device.

[0012] In a possible implementation of the first aspect above, the first moment and the second moment are within a valid time range.

[0013] In a possible implementation of the first aspect above, the first scenario and the second scenario include: a vehicle failure scenario, a vehicle normal operation scenario, a vehicle supported function switching scenario, and a scenario corresponding to a change in the vehicle operating status.

[0014] In a second aspect, the present application provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device; and a processor, which is one of the processors of the electronic device, for executing the instructions stored in the memory to implement any one of the data acquisition methods provided in the above-mentioned first aspect and various possible implementations.

[0015] In a third aspect, an embodiment of the present application provides a system, the system comprising a first electronic device and a second electronic device, the system being used to execute the data collection method mentioned in the first aspect of the present application and any item of the first aspect.

[0016] In a fourth aspect, the present application provides a readable storage medium having instructions stored thereon. When the instructions are executed on a server, the server executes the first aspect of the present application and the data collection method mentioned in the first aspect.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, including: a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium containing a computer program code for executing the first aspect of the present application and the data acquisition method mentioned in the first aspect.

[0018] The beneficial effects of the second, third, fourth and fifth aspects mentioned above can be referred to the relevant descriptions in the first aspect and various possible implementations of the first aspect, and will not be elaborated here.

[0019] The beneficial effects of the present application are as follows: the first electronic device calculates and outputs the first scene signal representing the scene in real time according to the data corresponding to the first control signal, thereby performing a data collection task according to the first scene signal. The collected first data is associated with the scene corresponding to the first scene signal to obtain the second data, effectively realizing accurate data collection. It also effectively improves the problem of excessive and redundant collected data, which is beneficial for the first electronic device to transmit data to the second electronic device; and it is beneficial for the second electronic device to store and analyze data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 According to some embodiments of the present application, a schematic diagram of a vehicle data collection scenario is shown;

[0021] Figure 2 According to some embodiments of the present application, a data acquisition system architecture is shown;

[0022] Figure 3 According to some embodiments of the present application, a flow chart of a data collection method is shown;

[0023] Figure 4 According to some embodiments of the present application, a schematic diagram of a data uploading and parsing process is shown;

[0024] Figure 5 According to some embodiments of the present application, a flow chart of a data collection method is shown. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0026] Understandably, Figure 1 A schematic diagram of a vehicle data collection scenario is shown, including a vehicle end 10 and a cloud 20. The vehicle end 10 may be a vehicle control device, also known as an on-board computer or a vehicle terminal, hereinafter referred to as a first electronic device; the cloud 20 may be a server or a server cluster that establishes a communication connection with the vehicle end 10, hereinafter referred to as a second electronic device. The cloud 20 may be used to receive information from different vehicle ends 10 and send information to the vehicle end 10. The information sent by the cloud 20 to the vehicle end may include tasks, scripts, etc., and the vehicle end 10 may receive and execute tasks sent by the cloud 20, such as computing tasks, data collection tasks, etc.; or the vehicle end 10 may receive and execute scripts sent by the cloud 20, such as data collection scripts. Then, the vehicle end 10 may upload the results of executing the tasks to the cloud 20, for example, upload the collected data corresponding to the results of executing the data collection tasks to the cloud 20. The cloud 20 may be used to send the predetermined tasks to the vehicle end 10, receive the results of executing the tasks uploaded by the vehicle end 10, and manage the results of executing the tasks, for example, distribute the collected data corresponding to the results of executing the data collection tasks to other users.

[0027] In some embodiments, the task scheduled by the cloud 20 is a data collection task, which may include a specified data collection method (for example, specifying the use of a buried point method to collect data, etc.), a collection range, a collection method, and a collection mechanism, etc. Among them, the collection mechanism may include: a regular collection mechanism based on a preset collection cycle or upload cycle, etc. Moreover, the corresponding collection scopes may be different for data collection tasks in different scenarios, so the above-mentioned collection scopes may cover: perception data, human-machine data, control instructions, intermediate variables, fault data, execution status, audio files, image files, video files, log files, etc., without limitation here. Collection methods may include: signal collection, sensor collection, etc.

[0028] However, the process of the vehicle end 10 currently performing data collection tasks may have problems such as excessive data collection and redundant data collection. For example, the vehicle's seat heating function has not been turned on. If the vehicle end 10 collects the vehicle's seat adjustment data every 2 seconds through signal collection and sensor collection based on the above-mentioned periodic collection mechanism, there will be a large amount of useless data in the collected data, which will affect the storage and transmission of the data. In addition, the data uploaded by the vehicle end 10 to the cloud 20 may also include data corresponding to the braking function, data corresponding to vehicle acceleration, etc., so it is not conducive to further analysis of the data in the case of excessive data.

[0029] Therefore, in order to solve the above problems, the present application provides a data collection method, in which a first electronic device (such as the above-mentioned vehicle end, etc.) used to perform a data collection task can identify the current data collection scene when it is detected that the data collection conditions are met. Then the data collection operation is performed to collect data associated with the current data collection scene to classify the collected data based on the scene. Furthermore, after the first electronic device sends the collected data to the second electronic device (such as the above-mentioned cloud), the second electronic device can also classify, manage and analyze the data associated with the data collection scene based on the above-mentioned received data. In this way, the first electronic device can accurately collect data, effectively improve the problems of excessive and redundant collected data, facilitate the first electronic device to transmit data to the second electronic device; and facilitate the second electronic device to store and analyze data.

[0030] In some optional embodiments, the first electronic device detecting that the data collection condition is satisfied may include: the first electronic device detecting a first type of control signal, wherein the generation of the first type of control signal is related to a vehicle state change. For example, a signal corresponding to a vehicle state change caused by a user's operation, or a signal corresponding to a change in the operating state of a device controlled by the first electronic device during operation. The first type of control signal may include a first control signal, a second control signal, and the like.

[0031] For example, when the first electronic device detects the first control signal at the first moment, the first electronic device can use the data corresponding to the first control signal as the input of a preset scene algorithm (such as an edge computing algorithm), and then calculate the first scene signal according to the scene algorithm. Thus, the first electronic device can determine that the current data collection scene of the first electronic device is a braking scene by the first scene signal. Then the first electronic device performs data collection operations according to the braking scene, and collects first data such as vehicle speed, braking distance, and whether a seat belt is fastened. And by adding the scene identifier corresponding to the braking scene to the collected first data, the first data is associated with the braking scene to obtain the second data. Then the first electronic device can classify the collected data based on the scene. The first electronic device can send the collected second data to the second electronic device, and the second electronic device analyzes the cause of the braking failure, whether the braking distance is within a safe range, or whether the braking function needs to be optimized based on the second data.

[0032] It can be understood that the second electronic device can also receive data corresponding to the vehicle in various scenarios. For example, the first electronic device can detect the second control signal at the second moment, and use the data corresponding to the first control signal as the input of the preset scene algorithm (such as the edge computing algorithm), and then calculate the second scene signal according to the scene algorithm. Thus, the first electronic device can determine that the current data collection scene of the first electronic device is the parking scene by the second scene signal. Then the first electronic device performs the data collection operation according to the parking scene, and collects the third data such as pictures and videos of the surrounding environment of the vehicle, and the gear position of the vehicle. And by adding the scene identifier corresponding to the parking scene to the collected third data, the third data is associated with the braking scene to obtain the fourth data. In this way, the data collected by the first electronic device is classified according to the data collection scene, which can be beneficial for the second electronic device to analyze the data. And when the first electronic device detects the first type of control signal (such as the first control signal, the second control signal), and then performs the data collection operation, it can effectively reduce the problem of affecting the transmission of the first electronic device to the second electronic device and the storage of data by the second electronic device due to excessive data collection.

[0033] In some optional embodiments, the first scenario and the second scenario may include: a vehicle failure scenario, a vehicle normal operation scenario, a vehicle supported function switching scenario, and a scenario corresponding to a change in the vehicle operating status.

[0034] For example, a vehicle failure scenario may refer to: the data in the first control signal detected by the first electronic device includes vehicle speed, braking distance, and driving distance, the data corresponding to the first control signal is input into a preset calculation algorithm execution module, and the calculation algorithm execution module outputs a first scenario signal. The first electronic device may determine that the vehicle's brakes have failed based on the first scenario signal, which is a vehicle failure scenario.

[0035] For another example, the scenario corresponding to the change in the vehicle's operating state may mean that the data in the first control signal detected by the first electronic device includes data such as vehicle speed change, and it can be determined that the scenario is that the vehicle's operating state changes from driving to parking.

[0036] In some embodiments, the second electronic device may send a data collection task and a computing task corresponding to the scenario algorithm to the first electronic device.

[0037] In some embodiments, the information contained in the data acquisition task may include: vehicle range, signal range, effective time range, acquisition cycle, upload cycle, etc. Among them, the vehicle range may include the model of the vehicle, the production date of the vehicle, and the data version number of the vehicle. The signal range may represent the range of the first control signal, such as vehicle model signals, Ethernet-based service signals, etc. Vehicle model signals may include: Controller Area Network (CAN) signals, Local Interconnect Network (LIN) signals, Advanced Streaming Format (ASF) signals, Direct Digital Synthesis (DDS) signals, and Shadow signals. The information contained in the computing task includes scene algorithms, such as edge computing algorithms.

[0038] In some embodiments, for ease of description, the data collection method mentioned in the embodiments of the present application is introduced below by taking the first electronic device as the vehicle end 10, the second electronic device as the cloud end 20, and the first electronic device detecting the first control signal as an example.

[0039] Taking the scene algorithm as an edge computing algorithm as an example, the data acquisition system architecture of the data acquisition method mentioned in this application is introduced below.

[0040] like Figure 2 As shown, the data acquisition system architecture may include: a domain controller on the vehicle side 10 , a vehicle-cloud communication layer, and a management platform on the cloud side 20 .

[0041] For example, the domain controller of the vehicle side 10 may refer to controllers corresponding to the body domain, chassis domain, power domain, smart cabin domain, smart driving domain, smart connection domain, relevant vector machine, remote diagnosis, etc.

[0042] The domain controller of the vehicle side 10 may include: a vehicle side edge computing engine 1100 and a vehicle side data collection engine 1200.

[0043] In some specific implementations, the computing task may be performed by the vehicle-side edge computing engine 1100. The vehicle-side edge computing engine 1100 may include: an edge computing script management module 1101, a data subscription module 1102, an edge computing task execution module 1103, an edge computing result distribution module 1104, and an application log 1105.

[0044] In some embodiments, the data acquisition system architecture may also include an operation support system (OSS) (not shown) for storing edge computing algorithm task scripts and data acquisition scripts generated by the cloud 20, as well as some data uploaded by the vehicle 10, such as the unstructured data mentioned below.

[0045] In some embodiments, the edge computing script management module 1101 can be used to manage edge computing algorithm task scripts. For example, the edge computing script management module 1101 can download and parse the edge computing algorithm task script stored in the operation support system (OSS) through the APN2 communication channel, extract the edge computing algorithm corresponding to the edge computing algorithm task script, and the input and output corresponding to the edge computing algorithm. Among them, the input corresponding to the edge computing algorithm can be the data corresponding to the first control signal mentioned above, and the corresponding output can be the first scene signal representing the scene.

[0046] The data subscription module 1102 can be used to subscribe to the input required by the edge computing algorithm. For example, the data subscription module 1102 initiates data subscription to the data pool module 1202 of the vehicle-side data acquisition engine 1200. The data pool module 1202 distributes the data corresponding to the first control signal required by the edge computing algorithm in all the data received by the data pool module 1202 to the edge computing task execution module 1103 according to the data subscription.

[0047] The edge computing task execution module 1103 (corresponding to the computing algorithm execution module mentioned above) can be used to execute the edge computing algorithm and send the first scene signal representing the scene output by the edge computing algorithm to the edge computing result distribution module 1104. For example, corresponding to the detection of the first control signal by the vehicle end 10, the edge computing task execution module 1103 can use the data corresponding to the first control signal as the input of the edge computing algorithm, and then output the first scene signal. Thus, the vehicle end 10 can determine the current data collection scene of the vehicle end 10 according to the first scene signal.

[0048] The edge computing result distribution module 1104 can be used to receive the first scene signal representing the scene output by the edge computing algorithm sent by the edge computing task execution module 1103, and distribute the first scene signal representing the scene to other data users, such as the vehicle-side data acquisition engine 1200.

[0049] In this way, in the vehicle-side edge computing engine 1100, the edge computing script management module 1101 downloads and parses the edge computing algorithm task script stored in the operation support system through the APN2 communication channel to obtain the edge computing algorithm preset in the cloud 20; and then the data subscription module 1102 subscribes to data according to the input of the edge computing algorithm. Then, the edge computing task execution module 1103 executes the computing task according to the data corresponding to the first control signal corresponding to the input, determines the first scene signal representing the scene output by the edge computing algorithm and sends it to the edge computing result distribution module 1104. The edge computing result distribution module 1104 distributes the scene to the vehicle-side data acquisition engine 1200. Thus, the vehicle-side data acquisition engine 1200 executes the data acquisition task according to the scene.

[0050] The application log 1105 can be used to record the operations performed by the vehicle-side edge computing engine 1100 and the modules included therein.

[0051] In some specific implementations, the data collection task may be performed by the vehicle-side data collection engine 1200. The vehicle-side data collection engine 1200 may include a data collection script management module 1201, a data pool module 1202, and a data collector module 1203.

[0052] In some embodiments, the data acquisition script management module 1201 can be used to manage the data acquisition script. For example, the data acquisition script management module 1201 can download and parse the downloaded and parsed data acquisition script stored in the operation support system through the APN2 communication channel, and distribute the parsed data acquisition script to the data collector module 1203 corresponding to the domain controller of the vehicle end 10 (for example, DC.CAN, DC.LIN, DC.ASF, DC.TP, DC.DDS and other data collectors deployed in the data collector module 1203). And collect the status information of the data collector module 1203, and transmit the collected status information to the cloud 20. For example, the status information of the data collector module 1203 can be the status information corresponding to the normal working state of the data collector module 1203, and the status information corresponding to the abnormal working state.

[0053] Among them, the data acquisition script can include structured data acquisition scripts and unstructured data acquisition scripts. The structured data acquisition script can be used to collect structured data, and the structured data is the data contained in the CAN acquisition script file, LIN acquisition script file, ASF acquisition script file, and DDS acquisition script file. The unstructured data acquisition script can be used to collect unstructured data, and the unstructured data includes pictures, audio and video, log files and other data.

[0054] The data pool module 1202 can be used to receive the data collected by the data collector module 1203. According to the data corresponding to the first control signal subscribed by the data subscription module 1102, the data corresponding to the first control signal in the data collected by the data collector module 1203 is distributed to the data subscription module 1102. It can be used to package the data collected by the data collector module 1203 to generate files, and execute the summary generation, compression, caching, and upload strategies of the files. It can also be used to perform encryption and decryption upload operations on files of classified, confidential, sensitive, and restricted levels; verify and update file names, high-precision timestamps, and file storage and hard disk life protection.

[0055] In some embodiments, the data received by the data pool module 1202 may include audio and video (Audio, Video), data collected by the laser radar in the vehicle (Lidar), data collected by Ethernet-based service signals (SomeIP), and data collected by the data acquisition module deployed in the vehicle (DC / TP).

[0056] The data collector module 1203 can collect data through deployed data collectors such as DC.CAN data collector, DC.LIN data collector, DC.ASF data collector, DC.DDS data collector, DC.Shadow data collector, etc.

[0057] Among them, the DC.CAN data collector, DC.LIN data collector, DC.ASF data collector and DC.DDS data collector deployed in the data collector module 1203 can parse the structured data collection script file, execute data collection tasks, and collect structured data.

[0058] Specifically, DC.CAN data collector can be used to parse CAN acquisition script files and execute data acquisition tasks to collect CAN signals and data corresponding to CAN signals.

[0059] DC.LIN data collector: can be used to parse LIN acquisition script files and execute data acquisition tasks to collect LIN signals and the data corresponding to LIN signals.

[0060] DC.ASF data collector: can be used to parse ASF acquisition script files, execute data acquisition tasks, and collect ASF signals and data corresponding to ASF signals.

[0061] DC.DDS data collector: can be used to parse DDS acquisition script files, execute data acquisition tasks, and collect DDS signals and data corresponding to DDS signals.

[0062] The DC.Shadow data collector deployed in the data collector module 1203 can parse the unstructured data collection script file, execute the data collection task, and collect unstructured data, such as collecting Shadow signals and data corresponding to the Shadow signals.

[0063] In this way, in the vehicle-side data acquisition engine 1200, the vehicle-side data acquisition engine 1200 performs data acquisition tasks according to the scene corresponding to the first scene signal distributed by the edge computing result distribution module 1104. Specifically, the data acquisition script management module 1201 downloads and parses the data acquisition script, and sends the parsed data acquisition script to the data collector module 1203. The data collector module 1203 executes the data acquisition task according to the data acquisition script, collects CAN signals, LIN signals, ASF signals, DDS signals, Shadow signals and the first data corresponding to each signal, and associates the collected first data with the first scene to obtain the second data. It can be understood that the data collector module 1203 obtains the second data with a scene identifier. And the second data is sent to the data pool module 1202, and the data pool module 1202 distributes the data according to the subscription data. And the vehicle-side data acquisition engine 1200 uploads the second data to the cloud 20, and the cloud 20 further parses the data.

[0064] It can be understood that the management platform of the cloud 20 may include: a cloud edge computing management module 1300 , a cloud data collection management module 1400 , and a cloud data parsing engine 1500 .

[0065] The cloud edge computing management module 1300 can be used to generate computing tasks (such as edge computing algorithm definition and release), computing task distribution and synchronization, and computing task monitoring and statistics.

[0066] Specifically, the edge computing algorithm is defined and published: the cloud edge computing management module 1300 determines that the input of the edge computing algorithm is the data corresponding to the first control signal and the output is the first scene signal according to the preset scene algorithm as the edge computing algorithm, thereby generating a computing task according to the edge computing algorithm.

[0067] Edge computing task distribution and synchronization: The cloud-based edge computing management module 1300 distributes the edge computing algorithm task script corresponding to the generated computing task to the vehicle-side 10, and synchronizes the distribution and execution status of the computing tasks between the vehicle-side 10 and the cloud-side 20 (vehicle-cloud).

[0068] Edge computing task monitoring and statistics: The cloud edge computing management module 1300 monitors and counts the running status and triggering status of computing tasks, and performs data analysis.

[0069] The cloud data collection management module 1400 can be used to manage and issue data collection tasks based on edge computing algorithms. Specifically, it includes:

[0070] Signal pool management: The cloud data acquisition management module 1400 manages the signal pools of CAN signals, LIN signals, ASF signals, DDS signals, Shadow signals, etc. For example, adding, modifying, deleting, and importing signals.

[0071] Data collection task management: The cloud data collection management module 1400 generates data collection tasks according to information such as the collection vehicle range, signal range, effective time range, collection cycle, upload cycle, etc. The collection vehicle range includes the vehicle model, the vehicle production date, and the vehicle data version number. The signal range can represent the range of the first control signal, including vehicle model signals (such as CAN signals, LIN signals, ASF signals, DDS signals, Shadow signals), Ethernet-based service signals, and other signals.

[0072] Data collection task issuance and synchronization: The cloud data collection management module 1400 sends the data collection script corresponding to the generated data collection task to the vehicle-side data collection engine 1200 of the vehicle-side 10, and synchronizes the issuance and execution status of the data collection tasks between the vehicle-side 10 and the cloud 20.

[0073] Statistical monitoring: The cloud data collection management module 1400 collects data on the running status of data collection tasks and the size of vehicle data collection traffic in real time, and displays it on a multi-dimensional dashboard, so that you can drill down to analyze details, set traffic threshold monitoring warnings, notifications, etc.

[0074] The cloud data analysis engine 1500 can be used to receive the second data collected by the vehicle end 10 and analyze the data.

[0075] Specifically, in some embodiments, since the data collected by the vehicle end 10 includes structured data and unstructured data, and there are differences in size between structured data and unstructured data, the process of data upload processing is also different. For example, unstructured data occupies too much memory and cannot be uploaded to the cloud in real time. Therefore, the cloud data parsing engine 1500 can process structured data through a structured upload gateway. For example, the vehicle end 10 compresses and uploads structured data to the cloud 20 in a binary file manner. After uploading, the cloud 20 notifies the cloud data parsing engine 1500 to use the structured data parsing service to parse the structured data through a message platform (such as a kafka message middleware). In addition, the cloud data parsing engine 1500 can process unstructured data through an unstructured upload gateway. For example, the vehicle end 10 uploads unstructured data of large files such as pictures, audio and video, and log files to the cloud 20. After uploading, the cloud 20 stores the unstructured data in the operation support system, and notifies the cloud data parsing engine 1500 to parse the unstructured data according to the operation support system address of the unstructured data and the unstructured parsing service through the kafka message middleware.

[0076] In some embodiments, the structured data analysis service of the cloud data analysis engine 1500 may specifically refer to: according to the data version number of the vehicle uploaded by the vehicle end 10, the corresponding structured analysis rules are retrieved to analyze the collected structured data, and after the analysis is completed, the data is distributed through the kafka message middleware. It can be understood that the structured analysis rules may be different for different vehicle data version numbers.

[0077] In some embodiments, the unstructured data analysis service of the cloud data analysis engine 1500 may specifically refer to: according to the data version number of the vehicle uploaded by the vehicle end 10, the corresponding unstructured analysis rules are called up to parse, convert, and merge the unstructured data uploaded by the vehicle end 10, and the processed data is stored in the operation support system, and the operation support system address of the processed file is distributed through the Kafka message middleware.

[0078] Vehicle-cloud communication layer: includes APN1 communication channel and APN2 communication channel.

[0079] Among them, the APN1 communication channel refers to: a vehicle-cloud communication channel based on the Message Queuing Telemetry Transport (MQTT) protocol. It is used for the upload and download interaction of computing tasks between the vehicle end 10 and the cloud end 20, and the upload and download interaction of data collection tasks between the vehicle end 10 and the cloud end 20, which can ensure the normal vehicle-cloud communication of the vehicle in a weak network condition.

[0080] APN2 communication channel refers to: a data transmission channel for uploading and downloading files based on Https protocol and TLS Proxy protocol. It is used for downloading edge computing algorithm task scripts, data collection scripts, and uploading collected data, and encrypting the data transmission channel to ensure the security of the data transmission channel.

[0081] In this way, the cloud 20 can send data collection tasks and computing tasks to the vehicle 10 through the APN1 communication channel. The vehicle 10 can download the data collection script corresponding to the data collection task and the edge computing algorithm task script corresponding to the computing task through the APN2 communication channel. Then, the vehicle 10 can execute the data collection task, associate the collected data with the calculated scene, and upload the collected data associated with the scene to the cloud 20 through the APN2 communication channel.

[0082] The following is a detailed introduction to the process of the data acquisition method mentioned in this application based on the above-mentioned data acquisition system structure and in combination with the accompanying drawings.

[0083] Understandably, Figure 3 A schematic diagram of a process of a data collection method is shown, and the process may mainly include the following parts: the cloud 20 performs configuration management, the vehicle end 10 executes tasks, and the cloud 20 performs data processing.

[0084] Specifically, the configuration management performed by the cloud 20 may include:

[0085] S301: Cloud 20 imports vehicle model signal.

[0086] In some optional embodiments, the cloud data acquisition management module 1400 of the cloud 20 can create a signal pool and manage the signal pool. The cloud data acquisition management module 1400 imports the signal contained in the signal range of the first control signal of the edge computing algorithm into the signal pool according to the computing task. For example, under this signal pool, corresponding vehicle model signals (such as CAN signals, LIN signals, ASF signals, DDS signals, Shadow signals), Ethernet-based service signals and other signals are imported. The following is an introduction to the cloud data acquisition management module 1400 importing vehicle model signals into the signal pool as an example.

[0087] S302: The cloud 20 executes vehicle model signal baseline binding.

[0088] In some optional embodiments, corresponding to the cloud data acquisition management module 1400 importing the vehicle model signal (including CAN signal, LIN signal, ASF signal, DDS signal and Shadow signal), the cloud data acquisition management module 1400 can further perform the operation of binding the vehicle model baseline. The CAN signal, LIN signal, ASF signal, DDS signal and Shadow signal in the vehicle model signal are bound to the corresponding vehicle model baseline, that is, the CAN signal, LIN signal, ASF signal, DDS signal and Shadow signal are bound to the data version number of the vehicle. In this way, the data corresponding to the CAN signal, LIN signal, ASF signal, DDS signal and Shadow signal can be used as the data corresponding to the first control signal input by the edge computing algorithm, and then the first scene signal representing the scene is output according to the data corresponding to the first control signal.

[0089] S303: The cloud 20 generates a computing task.

[0090] In some optional embodiments, the scene algorithm may include an edge computing algorithm. The cloud edge computing management module 1300 may generate computing tasks according to a preset edge computing algorithm. Thus, the vehicle end 10 may determine the current data collection scene according to the input vehicle model signal.

[0091] S304: The cloud 20 generates a data collection task.

[0092] In some optional embodiments, the cloud data collection management module 1400 can generate data collection tasks based on information such as the collection vehicle range, signal range, effective time range, collection cycle, and upload cycle.

[0093] In some embodiments, the vehicle collection range may refer to: determining the vehicle for data collection based on the vehicle model, the vehicle production date, the vehicle data version number, etc. The signal range may refer to vehicle model signals, Ethernet-based service signals, and other signals, such as CAN signals, LIN signals, ASF signals, DDS signals, and Shadow signals. The effective time range may refer to: the vehicle end 10 performs data collection within the set effective time range and uploads the collected data to the cloud 20. The collection cycle may refer to: the vehicle end 10 performs data collection according to the set collection cycle, for example, the collection cycle is 5s, and the vehicle end 10 can collect data every 5s. The upload cycle may refer to: the period for the vehicle end 10 to upload data to the cloud 20, for example, the vehicle end 10 can upload the collected data to the cloud 20 every 1min.

[0094] S305: The cloud 20 generates an edge computing algorithm task script and a data collection script and uploads them to the operation support system.

[0095] In some optional embodiments, the cloud edge computing management module 1300 can generate an edge computing algorithm task script corresponding to the computing task according to the computing task, and the cloud data collection management module 1400 can generate a data collection script corresponding to the data collection task according to the data collection task. The edge computing algorithm task script and the data collection script are uploaded to the operation support system.

[0096] S306: The cloud 20 executes the script and sends it.

[0097] In some optional embodiments, after the cloud 20 generates the edge computing algorithm task script and the data collection script, the cloud edge computing management module 1300 can send the computing task to the vehicle-side edge computing engine 1100 of the vehicle-side 10 through the APN1 communication channel. The cloud data collection management module 1400 can send the data collection task to the vehicle-side data collection engine 1200 of the vehicle-side 10 through the APN1 communication channel.

[0098] Specifically, the tasks performed by the vehicle end 10 may include:

[0099] S307: The vehicle end 10 executes script downloading.

[0100] In some optional embodiments, based on the edge computing tasks and data collection tasks received from the cloud 20, the vehicle-side edge computing engine 1100 downloads the edge computing algorithm task script stored in the operation support system from the cloud 20 through the APN2 communication channel, and the vehicle-side data collection engine 1200 downloads the data collection script stored in the operation support system from the cloud 20 through the APN2 communication channel.

[0101] S308: The vehicle end 10 performs script parsing.

[0102] In some optional embodiments, corresponding to the vehicle-side edge computing engine 1100 downloading the edge computing algorithm task script, the edge computing script management module 1101 can obtain the edge computing algorithm and the input and output corresponding to the edge computing algorithm by parsing the edge computing algorithm task script. Among them, the input corresponding to the edge computing algorithm can be the data corresponding to the first control signal, and the corresponding output can be the first scene signal representing the scene.

[0103] In some optional embodiments, the data collection script management module 1201 parses the data collection script corresponding to the vehicle-side data collection engine 1200 downloading the data collection script, and distributes the parsed data collection script to the data collector module 1203 corresponding to the domain controller of the vehicle-side 10 .

[0104] S309: The vehicle end 10 executes script distribution.

[0105] In some optional embodiments, the edge computing script management module 1101 can distribute the parsed edge computing algorithm task script to the edge computing task execution module 1103. The data acquisition script management module 1201 distributes the parsed data acquisition script to the data collector module 1203 corresponding to the domain controller of the vehicle end 10 (for example, DC.CAN, DC.LIN, DC.ASF, DC.TP, DC.DDS and other data collectors deployed in the data collector module 1203).

[0106] S310: The vehicle end 10 performs a computing task.

[0107] In some optional embodiments, the edge computing task execution module 1103 of the vehicle-side edge computing engine 1100 executes the edge computing algorithm according to the received edge computing algorithm task script. For example, corresponding to the vehicle-side 10 detecting the first control signal at the first moment, in response to the first control signal, the edge computing task execution module 1103 uses the data corresponding to the first control signal as the input of the edge computing algorithm, and outputs the first scene signal according to the edge computing algorithm. It can be understood that the first moment is within the effective time range.

[0108] S311: The vehicle end 10 outputs the data collection scene.

[0109] In some optional embodiments, the edge computing task execution module 1103 executes the edge computing algorithm according to the edge computing algorithm task script. When the edge computing task execution module 1103 determines that the data corresponding to the input first control signal can output the first scene signal representing the scene, the first scene signal representing the scene is output to determine the current data collection scene. When the edge computing task execution module 1103 determines that the data corresponding to the input first control signal cannot output the first scene signal representing the scene, no signal is output.

[0110] For example, according to the edge computing algorithm, when a CAN signal with a flag of 1 and a LIN signal with a flag of 0 are input at the same time, the edge computing task execution module 1103 determines that a first scene signal representing a braking scene can be output. When a CAN signal with a flag of 1 and a LIN signal with a flag of 0 are not input at the same time, or a CAN signal with a flag of 0 and a LIN signal with a flag of 1 are input at the same time, the edge computing task execution module 1103 does not output a signal.

[0111] S312: The vehicle end 10 performs a data collection task.

[0112] In some optional embodiments, the vehicle-side data acquisition engine 1200 distributes the first scene signal representing the scene output by the vehicle-side edge computing engine 1100 to the data acquisition module 1203. The data acquisition module 1203 performs data acquisition, associates the collected first data with the scene represented by the first scene signal, and obtains second data. The second data is sent to the data pool module 1202 of the vehicle-side data acquisition engine 1200.

[0113] In some optional embodiments, associating the collected first data with the scene represented by the first scene signal may mean: adding a scene identifier corresponding to the first scene to the first data to obtain second data; or, based on a preset matching relationship, identifying a first matching relationship between the scene field in the first data and the first scene to obtain second data.

[0114] S313: The vehicle end 10 packages and stores the collected data.

[0115] In some optional embodiments, the vehicle-side data collection engine 1200 may perform data packaging, compression, and disk storage operations on the second data based on the second data collected by the data collector module 1203 and received by the data pool module 1202, for example, packaging data associated with the same scene into one file.

[0116] S314: The vehicle end 10 uploads the triggering event and the collected data to the cloud 20.

[0117] In some optional embodiments, the trigger event may refer to a scene output by the vehicle end 10 executing a computing task. The vehicle end data acquisition engine 1200 packages and compresses the collected second data, uploads the unstructured data in the collected second data to the cloud data analysis engine 1500 through the APN2 communication channel, and compresses the structured data in the collected second data in a binary file manner and uploads it to the cloud data analysis engine 1500 through the APN2 communication channel, and the cloud data analysis engine 1500 further processes the data.

[0118] As an example, Figure 4 A data upload and analysis process is shown, which may include data collection, file creation, file compression, file encryption, file decryption, and file decompression.

[0119] In some optional embodiments, the vehicle end 10 may include a telematics box (TBox), so the vehicle end 10 uploading the collected second data to the cloud 20 may include: the telematics module of the vehicle end 10 creates a corresponding file for the collected second data, compresses and encrypts the file, and then transmits the encrypted file to the cloud 20 through the APN2 communication channel using the TLSProxy protocol or the Https protocol.

[0120] Specifically, the data processing performed by the cloud 20 may include:

[0121] S315: The cloud 20 receives the uploaded data.

[0122] As an example, continue to see Figure 4 , the cloud 20 receiving the second data uploaded by the vehicle end 10 may include: the cloud 20 decrypting and decompressing the compressed encrypted file corresponding to the second data, and then compressing and storing it in the data lake.

[0123] In some optional embodiments, the second data uploaded by the vehicle end 10 and received by the cloud data parsing engine 1500 of the cloud end 20 may include structured data and unstructured data.

[0124] Among them, structured data may include data collected by DC.CAN data collector, DC.LIN data collector, DC.ASF data collector, DC.DDS data collector, and DC.Shadow data collector. Unstructured data may include data such as pictures, audio and video, and log files. For example, a camera may be set in a vehicle, and the unstructured data may be the audio and video collected by the camera.

[0125] S316: The cloud 20 receives the data and stores it.

[0126] In some optional embodiments, there are differences in the size of structured data and unstructured data, and the data upload processing process is also different. For example, the unstructured data occupies too much memory and cannot be uploaded to the cloud 20 in real time. Therefore, the cloud data parsing engine 1500 can store the unstructured data in the operation support system, and store the operation support system address of the unstructured data in the Kafka message middleware.

[0127] S317: The cloud 20 analyzes the data.

[0128] In some optional embodiments, the cloud data parsing engine 1500 may parse the structured data according to the structured data parsing service, and parse the unstructured data according to the operation support system address of the unstructured data and the unstructured parsing service. For example, decompression and decryption operations are performed on the received data, and the data is classified according to the data collection scenario.

[0129] S318: The cloud 20 distributes the data.

[0130] In some optional embodiments, corresponding to the cloud 20 completing the analysis of the second data collected by the vehicle end 10, the cloud 20 can distribute the analyzed second data to other data users, such as data factories, business systems, etc., for use by other data users.

[0131] Therefore, this application uses the edge computing algorithm to take the data corresponding to the first control signal (such as CAN signal, LIN signal and other vehicle model signals) as input, and calculates and outputs the first scene signal representing the scene in real time through the vehicle side, so as to perform the data collection task according to the first scene signal, associate the collected first data with the scene corresponding to the first scene signal, and obtain the second data, effectively realizing accurate data collection. And the computing tasks and data collection tasks performed by the vehicle side can be configured through the cloud, which is conducive to obtaining the running status of the computing tasks and data collection tasks.

[0132] In this way, the scope of collection can be unlimited. By distributing data collection tasks to domain controllers, cross-domain integration can be achieved, structured data and unstructured data can be accurately collected, and the collection methods can be diversified. In addition, the collected data can support business application requirements such as the relevant vector machine (RVM) platform, remote diagnosis platform, and data simulation training in the data factory.

[0133] Figure 5 According to an embodiment of the present application, a flow chart of another data collection method is shown. The flow chart of the data collection method may include:

[0134] S501: A first type of control signal is detected at a first moment.

[0135] In some embodiments, generation of the first type of control signal is related to a change in a vehicle state, and the first type of control signal may include a first control signal and a second control signal.

[0136] In some embodiments, the source of the first control signal may include a signal generated in response to an operation performed by a user, or a signal generated in response to a change in an operating state during the operation of a device controlled by the first electronic device.

[0137] For example, the signal generated when the user brakes, the signal generated when the user adjusts the seat, etc.; or the signal generated when the vehicle speed changes during the operation of the first electronic device that controls the vehicle speed, etc.

[0138] S502: In response to a first control signal, determining that a current data collection scene is a first scene.

[0139] In some embodiments, the first electronic device may input data corresponding to the first control signal into a preset calculation algorithm execution module in response to the first control signal, and the calculation algorithm execution module may output a first scene signal. Thus, the first electronic device determines that the current data collection scene is the first scene based on the first scene signal.

[0140] For example, the computing algorithm execution module may be the edge computing task execution module 1103 mentioned above. Figure 3 To avoid repetition, S310 and S311 are not described in detail here.

[0141] S503: Execute a first data collection task corresponding to the first scene, associate the collected first data with the first scene, and obtain second data.

[0142] In some embodiments, the first data collection task may include a vehicle range, a signal range, an effective time range, a collection period, and an upload period. The vehicle range may include: the model of the vehicle, the production date of the vehicle, and the data version number of the vehicle. The signal range indicates the signal range of the first control signal, and the signal range may include a vehicle model signal and an Ethernet-based service signal.

[0143] In some embodiments, associating the collected first data with the first scene may mean: adding a scene identifier corresponding to the first scene to the first data; or, based on a preset matching relationship, identifying a first matching relationship between a scene field in the first data and the first scene.

[0144] For example, the data collector module 1203 may perform the first data collection task corresponding to the first scene, and the details may refer to the above. Figure 3 To avoid repetition, the S312 shown is not described in detail here.

[0145] For example, the first data collection task may be sent from the second electronic device to the first electronic device. For details, please refer to the above Figure 3 To avoid repetition, S301 - S309 shown are not described in detail here.

[0146] S504: Send second data to a second electronic device.

[0147] In some embodiments, the first electronic device may send the collected data to the second electronic device, so that the second electronic device may perform classification management and analysis based on the received data associated with the data collection scenario.

[0148] For example, the first electronic device sending the second data to the second electronic device can refer to the above Figure 3 To avoid repetition, S313-S314 shown are not described in detail here.

[0149] For example, the second electronic device receives the second data, and further manages and analyzes the second data. Figure 3 To avoid repetition, S315-S318 shown are not described in detail here.

[0150] In some other embodiments, the first electronic device may detect a second control signal included in the first type of control signal at the second moment. In response to the second control signal, the first electronic device may determine that the current data collection scenario is the second scenario. It is understood that the second scenario is different from the first scenario.

[0151] In some embodiments, the first electronic device may execute a second data collection task corresponding to the second scene, associate the collected third data with the second scene, obtain fourth data, and then send the fourth data to the second electronic device.

[0152] It can be understood that the first moment and the second moment are within the effective time range.

[0153] In some embodiments, the first scenario and the second scenario include: a vehicle failure scenario, a vehicle normal operation scenario, a vehicle supported function switching scenario, and a scenario corresponding to a change in the vehicle operating status.

[0154] For example, a vehicle failure scenario may refer to: the data in the first control signal detected by the first electronic device includes vehicle speed, braking distance, and driving distance, the data corresponding to the first control signal is input into a preset calculation algorithm execution module, and the calculation algorithm execution module outputs a first scenario signal. The first electronic device may determine that the vehicle's brakes have failed based on the first scenario signal, which is a vehicle failure scenario.

[0155] For another example, the scenario corresponding to the change in the vehicle's operating state may mean that the data in the first control signal detected by the first electronic device includes data such as vehicle speed change, and it can be determined that the scenario is that the vehicle's operating state changes from driving to parking.

[0156] In this way, the first electronic device can accurately collect data, effectively improve the problem of excessive and redundant collected data, facilitate the first electronic device to transmit data to the second electronic device; and facilitate the second electronic device to store and analyze data.

[0157] An embodiment of the present application provides an electronic device, comprising: one or more processors; one or more memories; one or more memories storing one or more programs, and when one or more programs are executed by one or more processors, the electronic device executes the data acquisition method provided by the embodiment of the present application.

[0158] An embodiment of the present application provides a system, which includes a first electronic device and a second electronic device, wherein the first electronic device is used to execute the steps executed by the first electronic device in the data collection method mentioned in the present application, and the second electronic device is used to execute the steps executed by the second electronic device in the data collection method mentioned in the present application.

[0159] An embodiment of the present application provides a computer-readable medium, on which instructions are stored. When the instructions are executed, a first electronic device executes the data collection method provided by the embodiment of the present application.

[0160] An embodiment of the present application provides a computer program product, including: a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium contains a computer program code for executing the data acquisition method mentioned in the present application.

[0161] It should be noted that the units / modules mentioned in the various device embodiments of the present application are all logical units / modules. Physically, a logical unit / module can be a physical unit / module, or a part of a physical unit / module, or can be implemented as a combination of multiple physical units / modules. The physical implementation method of these logical units / modules themselves is not the most important. The combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed by the present application. In addition, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units / modules that are not closely related to solving the technical problems proposed by the present application, which does not mean that there are no other units / modules in the above-mentioned device embodiments.

[0162] It should be noted that in the examples and description of this patent, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises one" does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0163] Although the present application has been illustrated and described with reference to certain embodiments thereof, it will be apparent to those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

Claims

1. A data collection method, characterized in that: Applied to a first electronic device, the method includes: detecting a first type of control signal at a first moment, wherein generation of the first type of control signal is related to a change in a vehicle state, and the first type of control signal comprises a first control signal; In response to the first control signal, determining that the current data collection scene is a first scene; executing a first data collection task corresponding to the first scenario, associating the collected first data with the first scenario, and obtaining second data; The second data is sent to a second electronic device.

2. The method according to claim 1, characterized in that Determining the current data collection scene as the first scene includes: Inputting data corresponding to the first control signal into a preset calculation algorithm execution module, and the calculation algorithm execution module outputs a first scene signal; Based on the first scene signal, determining that the current data collection scene is the first scene; wherein, The source of the first control signal includes a signal generated in response to an operation performed by a user, or a signal generated in response to a change in an operating state during the operation of a device controlled by the first electronic device.

3. The method according to claim 2, characterized in that The associating the collected first data with the first scene comprises at least one of the following: Adding a scene identifier corresponding to the first scene to the first data; Based on a preset matching relationship, a first matching relationship between the scene field in the first data and the first scene is identified.

4. The method according to claim 1, characterized in that: The first data collection task includes collecting vehicle range, signal range, effective time range, collection cycle and upload cycle. The vehicle range to be collected includes at least one of the following: vehicle model, vehicle production date, and vehicle data version number; The signal range represents the signal range of the first control signal, and the signal range includes a vehicle type signal and an Ethernet-based service signal.

5. The method according to claim 4, characterized in that The method comprises: detecting, at a second moment, a second control signal included in the first type of control signal; In response to the second control signal, determining that the current data collection scene is a second scene, where the second scene is different from the first scene; executing a second data collection task corresponding to the second scene, associating the collected third data with the second scene, and obtaining fourth data; The fourth data is sent to the second electronic device.

6. The method according to claim 5, characterized in that The first time and the second time are within the effective time range.

7. The method according to claim 5, characterized in that The first scenario and the second scenario include: a vehicle failure scenario, a vehicle normal operation scenario, a vehicle supported function switching scenario, and a scenario corresponding to a change in the vehicle operating status.

8. An electronic device, characterized in that: include: A memory for storing instructions executed by one or more processors of the electronic device, and the processor, which is one of the one or more processors of the electronic device, is used to execute the data collection method described in any one of claims 1 to 7.

9. A system, characterized in that: The system includes a first electronic device and a second electronic device, wherein the first electronic device is used to execute the steps executed by the first electronic device in the data collection method described in any one of claims 1 to 8, and the second electronic device is used to execute the steps executed by the second electronic device in the data collection method described in any one of claims 1 to 8.

10. A computer-readable medium, characterized in that The readable medium stores instructions, which, when executed on a computer, enable the computer to execute the data collection method according to any one of claims 1 to 8.

11. A computer program product, characterized in that The method comprises a computer program / instruction, which implements the data collection method according to any one of claims 1 to 8 when executed by a processor.

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