Data processing system and method and electronic equipment

Abstract

The invention provides a data processing system and method and electronic equipment, the data processing system comprises a data storage module, a data workflow module and a data synchronization module, the data storage module is used for storing multi-dimensional vehicle function data, and the data workflow module is used for synchronizing the multi-dimensional vehicle function data according to a task program sent by terminal equipment. Determining a target workflow for executing the target processing task, and obtaining target vehicle function data from the multi-dimensional vehicle function data based on the target workflow and processing the target vehicle function data to generate a task execution result; and the data synchronization module is used for synchronizing the task execution result to the terminal equipment. Through the data processing platform, a user does not need to additionally pay attention to the acquisition and synchronization process of data, function training, optimization and debugging tasks can be directly realized based on the data processing platform, the acquisition and consumption efficiency of vehicle function data is improved, the research and development efficiency for an automatic driving function is improved, and the research and development cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a data processing system, method and electronic device. Background Art

[0002] Currently, the development process of automatic (assisted) driving operating systems requires the consumption of a large amount of various vehicle function data, and the purposes of model training, function optimization, and troubleshooting are achieved through different vehicle function data.

[0003] In the prior art, the above-mentioned vehicle function data is usually stored in different servers or storage devices in a dispersed manner, and then read and consumed from different servers or storage devices through manual triggering based on the needs of R&D users.

[0004] The solutions in the existing technology have the problem of inefficiency in data acquisition and consumption, which leads to reduced R&D efficiency and increased R&D costs for companies for autonomous driving functions. Summary of the invention

[0005] The present application provides a data processing system, method and electronic device to solve the problem of low efficiency in data acquisition and consumption.

[0006] In a first aspect, the present application provides a data processing system, including a data storage module, a data workflow module and a data synchronization module, wherein:

[0007] The data storage module is used to store multi-dimensional vehicle function data, where the multi-dimensional vehicle function data is used to characterize the operation information of the test vehicle in at least two dimensions based on the target constraint conditions;

[0008] The data workflow module is used to determine a target workflow for executing a target processing task according to a task program sent by a terminal device, and to obtain and process target vehicle function data from the multidimensional vehicle function data based on the target workflow to generate a task execution result;

[0009] The data synchronization module is used to synchronize the task execution result to the terminal device.

[0010] In a possible implementation, the data storage module is specifically used to: obtain vehicle function data of the test vehicle collected by different types of data acquisition devices under the target constraint conditions; and generate the multi-dimensional vehicle function data based on at least two groups of the vehicle function data.

[0011] In a possible implementation, the data storage module is further configured to: obtain the environmental information corresponding to the test vehicle under the target constraint conditions, where the environmental information is used to characterize the external environmental characteristics when collecting the vehicle function data of the test vehicle; when generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data, the data storage module is specifically configured to: generate the multi-dimensional vehicle function data according to at least two sets of the vehicle function data and the corresponding environmental information.

[0012] In a possible implementation, before generating the multi-dimensional vehicle function data, the data storage module is further configured to: perform admission detection on the vehicle function data; when generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data, the data storage module is specifically configured to: configure the vehicle function data that meets the admission requirements into the data upload queue according to the result of the admission detection, and generate the multi-dimensional vehicle function data based on the data upload queue.

[0013] In a possible implementation, when obtaining the vehicle function data collected by different types of data collection devices for the test vehicle under the target constraint conditions, the data storage module is specifically configured to: periodically scan the data collection devices to obtain the corresponding device real-time data, and determine whether the device real-time data is newly updated data within the current period; if the device real-time data is the newly updated data within the current period, determine the device real-time data as the period data corresponding to the current period; generate the vehicle function data corresponding to the data collection device according to the period data corresponding to each period and the target constraint conditions.

[0014] In a possible implementation, the multi-dimensional vehicle function data further includes a processing record, where the processing record is used to characterize the historical task execution result; the data synchronization module is further configured to write the task execution result into the multi-dimensional vehicle function data.

[0015] In a possible implementation, the data storage module is further configured to: receive the multi-dimensional retrieval conditions sent by the terminal device; retrieve the multi-dimensional vehicle function data based on the multi-dimensional retrieval conditions to obtain a target data set; when the data workflow module obtains the target vehicle function data from the multi-dimensional vehicle function data based on the target workflow and processes it to generate a task execution result, the data workflow module is specifically configured to: process the target data set based on the target workflow to generate a task execution result.

[0016] In a possible implementation, the data workflow module is specifically configured to: based on the task program, obtain at least one execution step corresponding to the task program; for the execution step, create a corresponding task container, and determine a target workflow based on the task container; based on the target workflow, sequentially call each task container to process the target vehicle function data corresponding to each execution step, and generate a task execution result.

[0017] In a possible implementation, the execution steps include first-class execution steps and second-class execution steps, where the first-class execution steps are steps that can be executed based on the task program, and the second-class execution steps are dependent steps of the first-class execution steps; when the data workflow module creates a corresponding task container for the execution step and determines a target workflow based on the task container, it is specifically configured to: create a target task container corresponding to the first-class execution step based on the program code of the task program; create a dependent task container corresponding to the second-class execution step by accessing a preset library file; generate the target workflow based on the target task container and the dependent task container.

[0018] In a possible implementation, the data workflow module is further configured to: configure a call interface for the task container, where the call interface is used to determine the input data corresponding to the task container when the task container is triggered.

[0019] In a second aspect, the present application provides a data processing method, which is applied to the data processing system according to any one of the first aspects of the embodiments of the present application. The method includes:

[0020] Obtain multi-dimensional vehicle function data, where the multi-dimensional vehicle function data is used to characterize the running information of the test vehicle based on the target constraint conditions in at least two dimensions;

[0021] According to the task program sent by the terminal device, determine a target workflow for executing the target processing task, and based on the target workflow, obtain the target vehicle function data from the multi-dimensional vehicle function data and process it to generate a task execution result;

[0022] Synchronize the task execution result to the terminal device.

[0023] In a possible implementation, the obtaining of the multi-dimensional vehicle function data includes: obtaining the vehicle function data collected by different types of data collection devices for the test vehicle under the target constraint conditions; generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data.

[0024] In a possible implementation, it further includes: obtaining the environmental information corresponding to the test vehicle under the target constraint conditions, where the environmental information is used to characterize the external environmental characteristics when collecting the vehicle function data of the test vehicle; the generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data includes: generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data and the corresponding environmental information.

[0025] In a possible implementation, before generating the multi-dimensional vehicle function data, it further includes: performing an admission detection on the vehicle function data; the generating the multi-dimensional vehicle function data according to at least two sets of the vehicle function data includes: according to the result of the admission detection, configuring the vehicle function data that meets the admission requirements into a data upload queue, and generating the multi-dimensional vehicle function data based on the data upload queue.

[0026] In a possible implementation, the obtaining the vehicle function data collected by the data acquisition device includes: periodically scanning the data acquisition device to obtain the corresponding device real-time data, and determining whether the device real-time data is newly updated data within the current period; if the device real-time data is the newly updated data within the current period, determining the device real-time data as the period data corresponding to the current period; generating the vehicle function data corresponding to the data acquisition device according to the period data corresponding to each period and the target constraint conditions.

[0027] In a possible implementation, the multi-dimensional vehicle function data further includes a processing record, where the processing record is used to characterize the historical generated task execution result; it further includes: writing the task execution result into the multi-dimensional vehicle function data.

[0028] In a possible implementation, it further includes: receiving the multi-dimensional retrieval conditions sent by the terminal device; retrieving the multi-dimensional vehicle function data based on the multi-dimensional retrieval conditions to obtain a target data set; the obtaining the target vehicle function data from the multi-dimensional vehicle function data based on the target workflow and performing processing to generate a task execution result includes: processing the target data set based on the target workflow to generate a task execution result.

[0029] In a possible implementation, determining a target workflow for executing a target processing task according to a task program sent by a terminal device, and obtaining and processing target vehicle function data from the multi-dimensional vehicle function data based on the target workflow to generate a task execution result includes: obtaining at least one execution step corresponding to the task program based on the task program; for the execution step, creating a corresponding task container, and determining and generating a target workflow based on the task container; and sequentially calling each task container to process the target vehicle function data corresponding to each execution step based on the target workflow to generate a task execution result.

[0030] In a possible implementation, the execution step includes a first type of execution step and a second type of execution step, where the first type of execution step is a step that can be executed based on the task program, and the second type of execution step is a dependent step of the first type of execution step; for the execution step, creating a corresponding task container, and determining and generating a target workflow based on the task container includes: creating a target task container corresponding to the first type of execution step based on the program code of the task program; creating a dependent task container corresponding to the second type of execution step by accessing a pre-set library file; and generating the target workflow based on the target task container and the dependent task container.

[0031] In a possible implementation, it further includes: configuring a call interface for the task container, where the call interface is used to determine input data corresponding to the task container when the task container is triggered.

[0032] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0033] The memory stores computer execution instructions;

[0034] The processor executes the computer execution instructions stored in the memory to implement the data processing system according to any one of the first aspects of the embodiments of the present application, or the data processing method according to any one of the second aspects of the embodiments of the present application.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium, where computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed by a processor, they are used to implement the data processing system according to any one of the first aspects of the embodiments of the present application, or the data processing method according to any one of the second aspects of the embodiments of the present application.

[0036] According to a fifth aspect of the embodiments of the present application, the present application provides a computer program product, including a computer program, which when executed by a processor, implements the data processing system described in any one of the first aspects of the embodiments of the present application, or the data processing method described in any one of the second aspects of the embodiments of the present application.

[0037] The present application provides a data processing system, method and electronic device. The data processing system includes: a data storage module, a data workflow module and a data synchronization module. Among them, the data storage module is used to store multi-dimensional vehicle function data, and the multi-dimensional vehicle function data is used to characterize the running information of the test vehicle under at least two dimensions based on the target constraint conditions; the data workflow module is used to determine a target workflow for executing the target processing task according to the task program sent by the terminal device, and based on the target workflow, obtain target vehicle function data from the multi-dimensional vehicle function data and perform processing to generate a task execution result; the data synchronization module is used to synchronize the task execution result to the terminal device. While storing the multi-dimensional vehicle function data through the data storage module, the data workflow module receives the task program, constructs a target workflow for executing the target processing task, and based on the target workflow, obtains the target vehicle function data from the multi-dimensional vehicle function data and performs processing to realize the execution of the target processing task. Through the above data processing platform, R & D users do not need to pay additional attention to the process of data acquisition and synchronization, but can directly realize the tasks of function training, optimization and debugging based on the data processing platform, improve the efficiency of vehicle function data acquisition and consumption, and thus improve the R & D efficiency for autonomous driving functions and reduce the R & D cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0039] Figure 1 It is a schematic diagram of an application scenario of a data processing system provided by an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of a data processing system provided by an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of a process for generating multi-dimensional vehicle function data provided by an embodiment of the present application;

[0042] Figure 4 It is a schematic diagram of a process for generating a task execution result provided by an embodiment of the present disclosure;

[0043] Figure 5Flowchart of a data processing method provided by an embodiment of the present application;

[0044] Figure 6 For Figure 5 Flowchart of the specific implementation manner of step S201 in the illustrated embodiment;

[0045] Figure 7 For Figure 6 Flowchart of the specific implementation manner of step S2011 in the illustrated embodiment;

[0046] Figure 8 Flowchart of another data processing method provided by an embodiment of the present application;

[0047] Figure 9 For Figure 8 Flowchart of the specific implementation manner of step S304 in the illustrated embodiment;

[0048] Figure 10 For Figure 9 Flowchart of the specific implementation manner of step S3042 in the illustrated embodiment;

[0049] Figure 11 Structural schematic diagram of an electronic device provided by an embodiment of the present application;

[0050] Figure 12 Structural schematic diagram of another electronic device provided by an embodiment of the present application.

[0051] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0052] Here, exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0053] The application scenarios of the embodiments of the present application will be explained below:

[0054] The data processing system provided by the embodiments of the present application can be applied to the application scenarios of the development and debugging of the autonomous driving function of intelligent vehicles. More specifically, for example, in the development and debugging stages of functions such as vehicle driverless and vehicle assisted driving. The data processing system provided by the embodiments of the present application can store multi-dimensional vehicle function data, and by receiving user instructions or programs, implement specific data processing tasks based on the above stored multi-dimensional vehicle function data, so as to implement tasks in development and debugging stages such as model training, function verification, and fault debugging.

[0055] Figure 1 It is a schematic diagram of the application scenario of a data processing system provided by the embodiments of the present application. As Figure 1 shown, on the one hand, the data processing system can obtain vehicle function data of different dimensions from different storage devices or test devices, such as the driving speed, steering data, corresponding camera data, satellite positioning data, environmental temperature data, etc. of a test vehicle within a certain period of time. On the other hand, R & D users access the data processing system (or the server deploying the data processing system) through the terminal device, and upload task programs or instructions to the data processing system to execute corresponding processing tasks based on the above stored vehicle function data, such as tasks in development and debugging stages such as model training, function verification, and fault debugging. Finally, the task execution result is returned to the terminal device, so that the R & D user can obtain the execution result corresponding to the task program.

[0056] In the prior art, the server for storing vehicle function data usually only plays a single role of storing data, and for different types of data, it is usually stored in different storage devices or storage locations. When the R & D users of an enterprise need to consume the above vehicle function data for the function development and debugging of the autonomous driving function, they need to first perform the operation of obtaining the above vehicle function data, that is, obtain the required data from different storage devices or storage locations and download it. After that, based on the downloaded vehicle function data, the corresponding processing process of specific processing tasks is executed locally. Finally, the execution result of the processing task is synchronized to other cloud servers for storage to achieve the synchronization and backup of R & D data.

[0057] In the above solutions of the prior art, R & D users will consume a lot of time in each step of obtaining data, consuming data, and synchronizing execution results, resulting in the problem of low efficiency of data acquisition and consumption, and further leading to a reduction in the R & D efficiency and an increase in the R & D cost of the enterprise for the autonomous driving function. The embodiments of the present application provide a data processing system, method and electronic device to solve the above technical problems.

[0058] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0059] Figure 2 It is a schematic diagram of a data processing system provided by an embodiment of the present application. The data processing system provided by this embodiment can be applied to a cloud server, that is, the data processing system is deployed in the cloud and can be accessed through a terminal device. As Figure 2 shown, the data processing system provided by this embodiment includes: a data storage module, a data workflow module, and a data synchronization module. Among them, the data storage module is used to store multi-dimensional vehicle function data, and the multi-dimensional vehicle function data is used to represent the running information of the test vehicle based on the target constraint conditions in at least two dimensions; the data workflow module is used to determine the target workflow for executing the target processing task according to the task program sent by the terminal device, and obtain the target vehicle function data from the multi-dimensional vehicle function data based on the target workflow and perform processing to generate a task execution result; the data synchronization module is used to synchronize the task execution result to the terminal device.

[0060] Exemplarily, referring to Figure 1 the schematic diagram of the application scenario shown and Figure 2Schematic diagram of the data processing system shown. First, after the data processing system starts up, it obtains multi-dimensional vehicle function data from the outside through the data storage module and stores it. For example, by responding to the user's manual trigger instruction or based on the periodic acquisition logic, it obtains the vehicle function data stored in the data acquisition device. Or, after the data acquisition device acquires the corresponding vehicle function data, it first stores it in the corresponding external storage medium, and the data processing system obtains the vehicle function data stored in the external storage medium by communicating with the external storage medium. The data processing system then processes the vehicle function data of different dimensions through the data storage module and combines them into multi-dimensional vehicle function data. For example, in the same test, the driving speed, steering data, corresponding camera data, satellite positioning data, ambient temperature data, etc. of the test vehicle within a certain period of time are combined into multi-dimensional vehicle function data. Among them, in one possible implementation, the target constraint condition can be time, that is, the multi-dimensional vehicle function data can be combined based on time, that is, the combination of vehicle function data of multiple dimensions within the same period of time. In another possible implementation, the target constraint condition can be a topic, that is, the multi-dimensional vehicle function data can be combined based on the topic. For example, the combination of vehicle function data of multiple dimensions generated during the same test of function A. The target constraint condition can also be the combination of the above single constraint conditions, which can be specifically configured according to needs and will not be limited here.

[0061] Furthermore, for the generation process of multi-dimensional vehicle function data, the data storage module is specifically used for: obtaining the vehicle function data collected by different types of data acquisition devices of the test vehicle under the target constraint condition; generating multi-dimensional vehicle function data according to at least two sets of vehicle function data.

[0062] Exemplarily, the vehicle function data is the vehicle-related parameters formed by the vehicle under test during driving. Different data acquisition devices collect the corresponding vehicle function data. For example, the driving speed, steering data, corresponding camera data, radar data, etc. are all collected by different data acquisition devices. Since there is a large amount of data collected by the data acquisition device, the data storage module obtains it based on the target constraint condition when obtaining the vehicle function, and then combines the vehicle function data under the same target constraint condition to obtain multi-dimensional vehicle function data, realizing the efficient organization of multi-dimensional vehicle function data. Then, during the process of consuming the above multi-dimensional vehicle function data, it is also possible to first detect the data based on the target constraint condition and execute the data processing steps according to the retrieved results, which can effectively reduce the invalid data and incomplete data that cannot be effectively utilized stored in the data storage module, thereby improving the data storage efficiency of the data storage module.

[0063] Furthermore, the data storage module is also used for:

[0064] Obtain the environmental information corresponding to the test vehicle under the target constraint conditions. The environmental information is used to characterize the external environmental characteristics when collecting the vehicle function data of the test vehicle. Correspondingly, when generating multi-dimensional vehicle function data according to at least two sets of vehicle function data, the data storage module is specifically configured to: generate multi-dimensional vehicle function data according to at least two sets of vehicle function data and the corresponding environmental information. Specifically, the environmental information is, for example, the environmental temperature, humidity, altitude of the driving environment of the vehicle under test, as well as the road surface condition, road traffic flow condition, etc. The above environmental information can be collected by the data collection device of the vehicle under test, or by an external data collection device, or directly obtained based on an external service (such as temperature, humidity, altitude, etc.). Then, after aligning the environmental information and the vehicle function data based on the target constraint conditions, the data storage module jointly generates multi-dimensional vehicle function data.

[0065] In the steps of this embodiment, by adding additional environmental information, the data richness of the multi-dimensional vehicle function data is further improved, enabling the data storage module to provide more dimensions of information. Furthermore, when the data flow working module executes data processing tasks based on the multi-dimensional vehicle function data stored in the data storage module, more diverse and complex workflows can be realized, enabling the data processing system to achieve more types of target processing tasks.

[0066] Figure 3 FIG. is a schematic diagram of a process for generating multi-dimensional vehicle function data provided by an embodiment of the present application. The following combines Figure 3 to further introduce the functions of the above data storage module, as Figure 3As shown in the figure, the data storage module communicates with storage devices D1, D2, and D3 respectively, and periodically stores the data stored in storage devices D1, D2, and D3. Specifically, for example, in storage device D1, the remaining battery power (State Of Charge, SOC) data of the vehicle under test (a type of vehicle function data) is stored. This data is collected by the first data acquisition device installed in the vehicle under test and stored in storage device D1 in real time; in storage device D2, the driving speed data of the vehicle under test (another type of vehicle function data) is stored. This data is collected by the second data acquisition device installed in the vehicle under test and stored in storage device D2 in real time. In storage device D3, the ambient temperature data (a type of environmental information) is stored. This data is collected by the third data acquisition device installed in the vehicle under test and stored in storage device D3 in real time. After the data storage module reads the data in storage devices D1, D2, and D3, it selects the data corresponding to the execution of test task A (target constraint condition), that is, the remaining battery power, driving speed data, and ambient temperature data in the time period from T1 to T2 shown in the figure. And the above vehicle function data is combined into multi-dimensional vehicle function data for test task A (target constraint condition). Based on what is shown in the figure, in the process of the data storage module reading vehicle function data from an external storage device to generate multi-dimensional vehicle function data, the data is selected based on the target constraint condition, thus forming multi-dimensional vehicle function modules based on different target constraint conditions and storing them, further improving the data storage efficiency of the data storage module.

[0067] Furthermore, optionally, the data storage module further includes a detection sub-module. Before generating multi-dimensional vehicle function data, the data storage module also performs admission detection on the vehicle function data through the detection sub-module to obtain the result of the admission detection. Then, according to the result of the admission detection, it decides whether to combine the vehicle function data into multi-dimensional vehicle function data. In one possible implementation, the result of the admission detection is, for example, a boolean value, that is, 0 and 1, representing pass or fail respectively. For example, if the result of the admission detection is 0, indicating fail, then no multi-dimensional vehicle function data is generated; conversely, if the result of the admission detection is 1, indicating that the admission detection passes, then multi-dimensional vehicle function data is generated based on the vehicle function data obtained in the previous steps. In another possible implementation, the result of the admission detection can also be a numerical value representing the degree of matching. When the numerical value is greater than the preset threshold, it indicates pass, and vice versa.

[0068] By setting up the detection sub-module, it is possible to pre-detect the vehicle function data obtained by the data storage module and combine the qualified ones into multi-dimensional vehicle function data, thus avoiding the appearance of invalid multi-dimensional vehicle function data, saving data storage space, and improving data storage efficiency.

[0069] When the data storage module obtains the vehicle function data collected by different types of data acquisition devices for the test vehicle under the target constraint conditions, one implementation method is manual triggering to obtain the vehicle function data collected by the data acquisition device directly or indirectly. Another implementation method is to periodically scan the data acquisition device to obtain the real-time device data collected by the data acquisition device. Specifically, in this process, in each scanning cycle, after the data storage module obtains the real-time device data, it first determines whether the real-time device data is newly updated data in the current cycle. For example, in the T-th scanning cycle, the data storage module obtains the real-time device data D1 from the data acquisition device A and the real-time device data D2 from the data acquisition device B; in the (T + 1)-th scanning cycle, the data storage module obtains the real-time device data D3 from the data acquisition device A and the real-time device data D2 from the data acquisition device B. Then, in the (T + 1)-th scanning cycle, the data acquisition device A has collected newly updated data (i.e., the real-time device data D3), while the data acquisition device B has not collected newly updated data in the (T + 1)-th scanning cycle (i.e., it is still the real-time device data D2 collected in the T-th scanning cycle). In this case, if the real-time device data is newly updated data in the current cycle, the real-time device data is determined as the cycle data corresponding to the current cycle. For example, the real-time device data D3 of the above data acquisition device A is determined as the cycle data corresponding to the (T + 1)-th scanning cycle. Conversely, the cycle data corresponding to the data acquisition device B in the (T + 1)-th scanning cycle is empty data.

[0070] After that, according to the cycle data corresponding to each cycle, the vehicle function data is combined. Among them, for the cycles without cycle data (newly updated data), there is no need to perform actual data storage. Therefore, continuous duplicate data in the vehicle function data is avoided, thereby reducing the data volume of the vehicle function data to a certain extent, and further reducing the data volume of the multi-dimensional vehicle function data composed of the vehicle function data, improving the data storage efficiency.

[0071] After the multi-dimensional vehicle function data is constructed and stored through the data storage module, the multi-dimensional vehicle function data is maintained by the data storage module in the data processing system and can obtain the corresponding target vehicle function data in response to the request of the data workflow module. Since the data storage module and the multi-dimensional vehicle function data stored based on the data storage module have the above-described various technical effects, when the data workflow module uses the multi-dimensional vehicle function data in the data storage module to perform corresponding processing tasks, the technical effects of the above multi-dimensional vehicle function data can be inherited, thereby achieving technical effects such as improving data retrieval efficiency and reading efficiency.

[0072] Specifically, the data workflow module is used to determine a target workflow for executing a target processing task according to a task program sent by a terminal device. Exemplarily, the target workflow can be a piece of program code used to represent multiple executable steps. Then, based on the target workflow, target vehicle function data is obtained from the multi-dimensional vehicle function data, and is processed according to the target workflow to generate a task execution result. Among them, the target workflow is composed of multiple workflow nodes, and each workflow node corresponds to a processing step. In a possible implementation, the data workflow module is specifically used to: based on the task program, obtain at least one execution step corresponding to the task program; for the execution step, create a corresponding task container, and determine the generation of the target workflow based on the task container; based on the target workflow, sequentially call each task container to process the target vehicle function data corresponding to each execution step to generate a task execution result.

[0073] Specifically, first, the data workflow module of the data processing system is used to receive a task program uploaded by a user through a terminal device. The task program is, for example, program code for implementing a specific processing task, such as program code for implementing tasks such as model training, function verification, and fault debugging. Then, the data workflow module creates one or more corresponding logical execution steps based on the program code. After that, the data workflow module creates a task container (container image) for each execution step, and realizes the execution of the task program by deploying the program code of the above task program in the task container. Among them, based on the specific content of the task program, the data workflow module can create one or more task containers for it. After that, based on the execution logic of each execution step corresponding to the task program, arrange the corresponding task containers to determine a target workflow for implementing the task program. Finally, execute the target workflow. Based on the execution logic of each task container (execution step) in the target workflow, call the data storage module to obtain a target data set that matches the execution step from the multi-dimensional vehicle function data stored in the data storage module, and input it into the task container for processing to obtain a corresponding step execution result. After all steps are executed, the final task execution result is obtained.

[0074] Further, the execution steps include first - type execution steps and second - type execution steps. Among them, the first - type execution steps are steps that can be executed based on the task program, and the second - type execution steps are dependent steps of the first - type execution steps. In short, among the multiple execution steps corresponding to the task program, some are steps that can be directly executed. For example, an image target recognition algorithm recorded in the task program. The data workflow module creates a corresponding container image for this algorithm and directly executes the algorithm to obtain the corresponding step execution result. However, there are also some that cannot be directly executed and need to call other reference files to execute. For example, the target workflow contains a processing step of adding Gaussian noise to an image, and the generation algorithm of Gaussian noise is not directly included in the task program but is provided by the data workflow module. That is, in the process of generating the target workflow, in addition to the task program input by the user, other programs used to support the execution of this task program can all be provided by the data workflow module. Therefore, the user can focus on the design and implementation of the core processing steps of specific functional processing tasks without having to consider the dependent steps too much. The data workflow module can automatically match and generate the dependent steps of the key steps executed based on the task program, thereby improving the execution efficiency of data processing tasks.

[0075] Figure 4 Schematic diagram of the generation process of a task execution result provided by an embodiment of the present disclosure. As Figure 4 shown, first, the data workflow module receives the task program and creates multiple task containers based on the task program, such as the containers P1, P2, P3, P4 shown in the figure (shown as P1, P2, P3, P4 in the figure). Among them, each of the containers P1, P2, and P3 is used to execute three processing steps S1, processing step S2, and processing step S3 corresponding to the task program, that is, the first - type execution steps. And the container P4 is used to execute the dependent step S31 corresponding to the task processing step S3, that is, the second - type execution steps. Then, based on the above - mentioned containers P1, P2, P3, P4 and the corresponding execution logic, the target task flow L1 is generated. Then, by executing the target task flow L1, the task execution result is generated. Among them, each of the above - mentioned containers obtains the corresponding target vehicle function data from the multi - dimensional vehicle function data stored in the data storage module according to the specific content of the execution steps for consumption to implement the corresponding execution steps.

[0076] Further, in a possible implementation, before the task execution result is generated by the data workflow module, the user can send multi-dimensional retrieval conditions to the data storage module of the data processing system through the terminal device. The multi-dimensional retrieval conditions are the union of multiple detection conditions. For example, the union of conditions such as vehicle driving speed range, time range, test task, etc., can be understood as a more accurate retrieval condition compared to the single-dimensional retrieval condition. Then, based on the multi-dimensional retrieval conditions, multi-dimensional vehicle function data is retrieved to obtain a target data set, and based on the target data set, the workflow module is called to generate the task execution result. In this embodiment, since the multi-dimensional vehicle function data composed of vehicle function data of different dimensions is uniformly stored in the data storage module, the data storage module can accept and use the multi-dimensional retrieval conditions for data retrieval, so as to obtain a more accurate data range that meets the user's usage requirements, and improve the effect of the data workflow module performing subsequent functional tasks based on the target data set.

[0077] Further, in a possible implementation, the data workflow module is also used to configure a call interface for the task container. The call interface is used to determine the input data corresponding to the task container when the task container is triggered. That is, when creating or after creating the task container, the data workflow module configures the corresponding call result for the task container, so as to control the input data used by the task container when executing the corresponding data processing task steps, and realize more refined control over the specific implementation process of the target workflow, further improving the complexity of the target processing task that the target workflow can achieve, and thus improving the data processing performance of the data processing system.

[0078] Further, after the task execution result is generated, on the one hand, the data synchronization module in the data processing system synchronizes the task execution result to the terminal device, so that the user on the terminal device side can obtain the processing result of the target processing task. On the other hand, the task execution result will be persistently saved in the data processing system, and other authorized users can obtain the task execution result corresponding to the current target processing task, as well as intermediate variable data such as the corresponding target workflow and target vehicle function data by accessing the data processing system. There is no need for the user on the terminal device side to synchronize data by recording and uploading data. In a possible implementation, the data storage module is also used to write the task execution result into the multi-dimensional vehicle function data to form a processing record, and the processing record is used to represent the task execution result generated historically. In a more complex application scenario, the user can, according to needs, obtain the processing record through the data storage module in the data processing system, so as to reproduce the task execution result generated historically, and then input the task program for processing tasks such as data comparison and function effect comparison, and call the data workflow module to generate the corresponding task execution result.

[0079] The data processing system provided in this embodiment includes: a data storage module, a data workflow module, and a data synchronization module. Among them, the data storage module is used to store multi-dimensional vehicle function data, and the multi-dimensional vehicle function data is used to characterize the running information of the test vehicle under at least two dimensions based on the target constraint conditions; the data workflow module is used to determine the target workflow for executing the target processing task according to the task program sent by the terminal device, and obtain and process the target vehicle function data from the multi-dimensional vehicle function data based on the target workflow to generate a task execution result; the data synchronization module is used to synchronize the task execution result to the terminal device. While storing the multi-dimensional vehicle function data through the data storage module, the task program is received through the data workflow module, the target workflow for executing the target processing task is constructed, and the target vehicle function data is obtained and processed from the multi-dimensional vehicle function data based on the target workflow to implement the execution of the target processing task. Through the above data processing platform, R & D users do not need to pay additional attention to the process of data acquisition and synchronization, but can directly implement tasks such as function training, optimization, and debugging based on the data processing platform, improving the efficiency of obtaining and consuming vehicle function data, and further improving the R & D efficiency for autonomous driving functions and reducing R & D costs.

[0080] Figure 5 It is a flowchart of a data processing method provided in an embodiment of the present application, which can be applied to Figures 1 - 4 any data processing system shown in the corresponding embodiment, and the data processing method provided in this embodiment will be introduced below in combination with Figures 1 - 4 the data processing system shown. As Figure 5 shown, the data processing method provided in this embodiment includes:

[0081] Step S201: Obtain multi-dimensional vehicle function data, where the multi-dimensional vehicle function data is used to characterize the running information of the test vehicle under at least two dimensions based on the target constraint conditions.

[0082] Step S202: Determine the target workflow for executing the target processing task according to the task program sent by the terminal device, and obtain and process the target vehicle function data from the multi-dimensional vehicle function data based on the target workflow to generate a task execution result.

[0083] Step S203: Synchronize the task execution result to the terminal device.

[0084] Among them, the execution subject of the method embodiment can be Figures 1 - 4The data processing system provided by any possible implementation manner in the corresponding embodiment, or an electronic device deploying the data processing system, such as a server. Among them, in some embodiments, the electronic device can implement the data processing method provided by the embodiments of the present application by running various computer-executable instructions or computer programs. For example, the computer-executable instructions can be program-level commands, machine instructions, or software instructions. The computer program can be a native program or software module in the operating system; it can be a local application, that is, a program that needs to be installed in the operating system to run, or a program running based on the browser environment. In summary, the above computer-executable instructions can be instructions in any form, and the above computer programs can be application programs, modules, or plugins in any form, and the specific implementation form can be configured according to needs. Further, in the process of implementing the data processing method provided by the embodiments of the present application, the electronic device can execute the method by running the computer-executable instructions or computer programs set locally, or can execute the method by calling the computer-executable instructions or computer programs set in an external server. In some embodiments, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud storage, cloud communication, cloud databases, cloud computing, cloud functions, network services, middleware services, domain name services, security services, content delivery network (CDN), and big data and artificial intelligence platforms. Among them, the cloud service can be an interactive processing service for terminal devices to call.

[0085] For the specific implementation methods of the above steps S201 - S203, reference can be made to the steps respectively executed by the data storage module, the data workflow module, and the data synchronization module in the previous embodiments. The specific implementation manners are similar and will not be elaborated here.

[0086] Further, in a possible implementation manner, as Figure 6 shown, the specific implementation manner of step S201 includes:

[0087] Step S2011: Obtain vehicle function data collected by different types of data collection devices for the test vehicle under target constraint conditions.

[0088] Step S2012: Generate multi-dimensional vehicle function data according to at least two sets of vehicle function data.

[0089] Optionally, in another possible implementation manner, step S201 further includes:

[0090] Step S2013: Obtain the environmental information corresponding to the test vehicle under the target constraint conditions, where the environmental information is used to characterize the external environmental characteristics when collecting the vehicle function data of the test vehicle.

[0091] Correspondingly, the specific implementation manner of step S2012 includes:

[0092] Step S2012A: Generate multi-dimensional vehicle function data according to at least two groups of vehicle function data and the corresponding environmental information.

[0093] Further, in this embodiment, before step S201 is executed, it further includes:

[0094] Step S200: Perform admission detection on the vehicle function data.

[0095] Correspondingly, the specific implementation manner of step S2012 includes:

[0096] Step S2012B: According to the result of the admission detection, configure the vehicle function data that meets the admission requirements and the corresponding environmental information into the data upload queue, and generate multi-dimensional vehicle function data based on the data upload queue.

[0097] Further, as Figure 7 shown, the specific implementation manner of step S2011 includes:

[0098] Step S2011-1: Periodically scan the data acquisition device to obtain the corresponding real-time device data, and determine whether the real-time device data is newly updated data within the current period;

[0099] Step S2011-2: If the real-time device data is newly updated data within the current period, determine the real-time device data as the periodic data corresponding to the current period;

[0100] Step S2011-3: Generate the vehicle function data corresponding to the data acquisition device according to the periodic data corresponding to each period and the target constraint conditions.

[0101] Figure 8 This is a flowchart of another data processing method provided by the embodiment of the present application, which can be applied to Figures 1 - 4 any data processing system corresponding to the shown embodiments. The data processing method provided by this embodiment will be introduced below in conjunction with Figures 1 - 4 the shown data processing system. As Figure 8 shown, the data processing method provided by this embodiment includes:

[0102] Step S301: Obtain multi-dimensional vehicle function data, where the multi-dimensional vehicle function data is used to characterize the operation information of the test vehicle based on the target constraint conditions in at least two dimensions.

[0103] Step S302: Receive the multi-dimensional retrieval conditions sent by the terminal device.

[0104] Step S303: Retrieve multi-dimensional vehicle function data based on the multi-dimensional retrieval conditions to obtain a target data set.

[0105] Step S304: Determine a target workflow for executing the target processing task according to the task program sent by the terminal device, and process and generate a task execution result by obtaining the target vehicle function data from the target data set based on the target workflow.

[0106] Exemplarily, as Figure 9 shown, the specific implementation manner of Step S304 includes:

[0107] Step S3041: Based on the task program, obtain at least one execution step corresponding to the task program.

[0108] Step S3042: For the execution step, create a corresponding task container, and determine and generate a target workflow based on the task container.

[0109] Step S3043: Based on the target workflow, sequentially call each task container to process the target vehicle function data corresponding to each execution step, and generate a task execution result.

[0110] Among them, optionally, the execution steps include first-class execution steps and second-class execution steps. Among them, the first-class execution steps are the steps that can be executed based on the task program, and the second-class execution steps are the dependent steps of the first-class execution steps. As Figure 10 shown, the specific implementation steps of Step S3042 include:

[0111] Step S3042-1: Based on the program code of the task program, create a target task container corresponding to the first-class execution step.

[0112] Step S3042-2: By accessing the preset library file, create a dependent task container corresponding to the second-class execution step.

[0113] Step S3042-3: Based on the target task container and the dependent task container, generate a target workflow.

[0114] Optionally, after Step S3042-2, it further includes:

[0115] Step S3042-4: Configure a call interface for the task container, and the call interface is used to determine the input data corresponding to the task container when the task container is triggered.

[0116] Step S305: Write the task execution result into the multi-dimensional vehicle function data.

[0117] Step S306: Synchronize the task execution result to the terminal device.

[0118] For the relevant descriptions of the steps in the above embodiments, reference can be made to the relevant descriptions and effects in the corresponding embodiments of the data processing system, and details are not elaborated here.

[0119] Figure 11 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 11 shown, the electronic device 4 includes:

[0120] a processor 41 and a memory 42 communicatively connected to the processor 41;

[0121] The memory 42 stores computer-executable instructions.

[0122] The processor 41 executes the computer-executable instructions stored in the memory 42 to implement the data processing system or data processing method provided in the above embodiments.

[0123] Optionally, the processor 41 and the memory 42 are connected through a bus 43.

[0124] For relevant descriptions, reference can be made to Figures 2 - 10 the relevant descriptions and effects corresponding to the steps in the corresponding embodiments, and details are not elaborated here.

[0125] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the data processing system or data processing method provided in any one of the corresponding embodiments of the present application when executed by a processor. Figures 2 - 10 when executed by a processor, are used to implement the data processing system or data processing method provided in any one of the corresponding embodiments of the present application.

[0126] An embodiment of the present application provides a computer program product, including a computer program, which implements the data processing system or data processing method provided in any one of the corresponding embodiments of the present application when executed by a processor. Figures 2 - 10 when executed by a processor, implements the data processing system or data processing method provided in any one of the corresponding embodiments of the present application.

[0127] To implement the above embodiments, an embodiment of the present application further provides an electronic device.

[0128] Refer to Figure 12, which shows a schematic structural diagram of an electronic device 900 suitable for implementing the embodiments of the present application. The electronic device 900 can be a terminal device or a server. Among them, the terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers, portable media players (PMPs), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 12 The electronic device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0129] As Figure 12 shown, the electronic device 900 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage device 908 into the random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The input / output (I / O) interface 905 is also connected to the bus 904.

[0130] Generally, the following devices can be connected to the I / O interface 905: an input device 906 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication device 909 can allow the electronic device 900 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 12 shows the electronic device 900 with various devices, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices can be alternatively implemented or had.

[0131] In particular, according to an embodiment of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device 909, or installed from a storage device 908, or installed from a ROM 902. When the computer program is executed by a processing device 901, the above-described functions defined in the methods of the embodiments of the present application are performed.

[0132] It should be noted that the above-mentioned computer-readable medium in the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can 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 application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable signal medium can 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 can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0133] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately and not be assembled into the electronic device.

[0134] The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device is caused to execute the methods shown in the above embodiments.

[0135] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0137] The units or modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the unit or module does not, in some cases, constitute a limitation on the unit itself.

[0138] The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on a chip (SOC), complex programmable logic devices (CPLD), and so on.

[0139] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of devices or modules can be in electrical, mechanical, or other forms.

[0141] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0142] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A data processing system, characterized in that, It includes a data storage module, a data workflow module, and a data synchronization module, where: The data storage module is used to store multi-dimensional vehicle function data, and the multi-dimensional vehicle function data is used to characterize the operation information of the test vehicle under at least two dimensions based on the target constraint conditions; The data workflow module is used to determine a target workflow for executing a target processing task according to the task program sent by the terminal device, and obtain and process target vehicle function data from the multi-dimensional vehicle function data based on the target workflow to generate a task execution result; The data synchronization module is used to synchronize the task execution result to the terminal device.

2. The data processing system according to claim 1, wherein Specifically, the data storage module is used to: Obtain the vehicle function data collected by different types of data collection devices of the test vehicle under the target constraint conditions; Generate the multi-dimensional vehicle function data according to at least two sets of the vehicle function data.

3. The data processing system according to claim 2, wherein The data storage module is further used to: Obtain the environmental information corresponding to the test vehicle under the target constraint conditions, and the environmental information is used to characterize the external environmental characteristics when collecting the vehicle function data of the test vehicle; When the data storage module generates the multi-dimensional vehicle function data according to at least two sets of the vehicle function data, specifically: Generate the multi-dimensional vehicle function data according to at least two sets of the vehicle function data and the corresponding environmental information.

4. The data processing system according to claim 2, wherein Before generating the multi-dimensional vehicle function data, the data storage module is further used to: Perform admission detection on the vehicle function data; When the data storage module generates the multi-dimensional vehicle function data according to at least two sets of the vehicle function data, specifically: According to the result of the admission detection, configure the vehicle function data that meets the admission requirements into a data upload queue, and generate the multi-dimensional vehicle function data based on the data upload queue.

5. The data processing system according to claim 2, wherein: When the data storage module obtains the vehicle function data collected by different types of data collection devices of the test vehicle under the target constraint conditions, specifically: Periodically scan the data collection device to obtain the corresponding device real-time data, and determine whether the device real-time data is newly updated data within the current period; If the device real-time data is the newly updated data within the current period, determine the device real-time data as the period data corresponding to the current period; Generate the vehicle function data corresponding to the data collection device according to the period data corresponding to each period and the target constraint conditions.

6. The data processing system according to claim 1, wherein: The multi-dimensional vehicle function data further includes a processing record, and the processing record is used to characterize the historically generated task execution result; The data synchronization module is further used to write the task execution result into the multi-dimensional vehicle function data.

7. The data processing system according to claim 1, wherein The data storage module is further used to: Receive the multi-dimensional retrieval conditions sent by the terminal device; Retrieve the multi-dimensional vehicle function data based on the multi-dimensional retrieval conditions to obtain a target data set; When the data workflow module obtains target vehicle function data from the multi-dimensional vehicle function data based on the target workflow and processes it to generate a task execution result, it is specifically used for: Processing and generating a task execution result by processing the target vehicle function data obtained from the target data set based on the target workflow.

8. The data processing system according to claim 1, characterized in that The data workflow module is specifically used for: Based on the task program, obtaining at least one execution step corresponding to the task program; For the execution step, creating a corresponding task container and determining a generated target workflow based on the task container; Based on the target workflow, sequentially calling each task container to process the target vehicle function data corresponding to each execution step to generate a task execution result; The data workflow module is further used for: Configuring a call interface for the task container, and the call interface is used to determine the input data corresponding to the task container when the task container is triggered.

9. The data processing system according to claim 8, wherein The execution steps include first-class execution steps and second-class execution steps. Among them, the first-class execution steps are steps that can be executed based on the task program, and the second-class execution steps are dependent steps of the first-class execution steps; When the data workflow module creates a corresponding task container for the execution step and determines a generated target workflow based on the task container, it is specifically used for: Based on the program code of the task program, creating a target task container corresponding to the first-class execution step; By accessing a pre-set library file, creating a dependent task container corresponding to the second-class execution step; Based on the target task container and the dependent task container, generating the target workflow.

10. A data processing method, characterized in that, Applied to the data processing system according to any one of claims 1 to 9, the method includes: Obtaining multi-dimensional vehicle function data, where the multi-dimensional vehicle function data is used to characterize the running information of a test vehicle under at least two dimensions based on target constraint conditions; According to the task program sent by the terminal device, determining a target workflow for executing a target processing task, and obtaining and processing target vehicle function data from the multi-dimensional vehicle function data based on the target workflow to generate a task execution result; Synchronizing the task execution result to the terminal device.

11. An electronic device, characterized in that, Including: A processor and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the data processing system according to any one of claims 1 to 9 or to implement the data processing method according to claim 10.

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