Data processing method and related device

Abstract

A data processing method and a related device. The method comprises: acquiring task configuration information of a task (S301); and on the basis of the task configuration information and vehicle information corresponding to N vehicles, determining from among the N vehicles R target vehicles executing the task, wherein the vehicle information comprises resource information indicating the amount of available resources of corresponding vehicles, the amount of available resources of each of the R target vehicles is greater than or equal to a resource consumption amount corresponding to the task, the resource consumption amount is obtained on the basis of the task configuration information, N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1 (S302).

Description

Data processing method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on February 29, 2024, with application number 202410236381.7 and invention name “Data Processing Methods and Related Equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present invention relates to the field of Internet technology, and in particular to a data processing method and related equipment. Background Art

[0003] The technological advancement of autonomous vehicles is inextricably linked to the optimization of the algorithms behind them. To continuously optimize and iterate these algorithms, vehicles must rely on massive amounts of high-quality data input. Within the entire intelligent driving ecosystem, vehicle data collection and management play an essential role. This is not only because they guarantee the vehicle's performance and stability, but also because they provide drivers with a personalized driving experience and provide solid data support for cutting-edge autonomous driving technology.

[0004] Despite this, current vehicle-side data collection and management technologies face a series of challenges in practical applications. When conducting in-depth and extensive data collection in large-scale production vehicles, ensuring the efficiency of collecting this massive amount of data and its generalization across multiple scenarios to meet the stringent standards of cloud-based model training has become a key challenge in research and application.

[0005] Furthermore, resources are always limited, especially in terms of data flow and storage space. Therefore, how to maximize the use of data collected by mass-produced fleets within limited resources to meet the growing data demands of autonomous driving and other related intelligent technologies is also a pressing issue. Summary of the Invention

[0006] The present application provides a data processing method and related equipment to improve the efficiency and quality of data collection.

[0007] The first aspect provides a data processing method. The method includes: obtaining task configuration information of a task; determining R target vehicles to perform the task from the N vehicles based on the task configuration information and vehicle information corresponding to N vehicles, wherein the vehicle information includes resource information indicating the available resource amount of the corresponding vehicle, and the available resource amount of each target vehicle in the R target vehicles is greater than or equal to the resource consumption amount corresponding to the task, and the resource consumption amount is obtained based on the task configuration information; N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1. The vehicle information includes the resource information of the vehicle, so that based on the task configuration information of the task, the target vehicle with sufficient available resources to support the completion of the task can be screened out from the N vehicles, thereby ensuring that the target vehicle can complete the task and improving the efficiency and quality of task completion.

[0008] In one possible implementation, the task configuration information includes a first condition that must be met by a vehicle performing the task, the first condition including target parameter information and / or target location information of the vehicle, the vehicle information including parameter information and / or location information of the corresponding vehicle, and determining R target vehicles for performing the task from among the N vehicles based on the task configuration information and vehicle information corresponding to N vehicles, including: determining M vehicles corresponding to M vehicle information matching the first condition among the vehicle information corresponding to the N vehicles; M is less than or equal to N, and M is an integer less than or equal to 1; and determining the R target vehicles from among the M vehicles. By using the first condition specified in the task configuration information, M vehicles that meet basic requirements are determined from among the N vehicles, and the M vehicles are candidate vehicles for performing the task, which can reduce the scale of subsequent screening of target vehicles.

[0009] In one possible implementation, determining the R target vehicles from the M vehicles includes determining the R target vehicles from the M vehicles based on the resource information and the resource consumption. The R target vehicles are further selected from the M vehicles based on the resource information and the resource consumption corresponding to the task, ensuring that the resources of the target vehicles are sufficient to successfully complete the task. This can alleviate the problem of inefficient task execution progress caused by interruptions during task execution, thereby improving task completion efficiency.

[0010] In one possible implementation, the vehicle information includes usage information of the corresponding vehicle, and determining the R target vehicles from the M vehicles includes: determining K vehicles corresponding to K pieces of vehicle information matching a second condition within the vehicle information corresponding to the M vehicles, where the second condition indicates the target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; and determining the R target vehicles from the K vehicles based on the resource information and the resource consumption. The vehicle usage information indicates a user's preference or habits in vehicle usage, and the second condition matches the task, thereby further screening out target vehicles that are more closely matched to the task from the M vehicles, thereby achieving higher quality and more efficient task completion.

[0011] In one possible implementation, the available resource amount includes the remaining computing resource amount, the resource consumption includes the computing resource consumption, and the remaining computing resource amount is greater than or equal to the computing resource consumption; and / or the available resource amount includes the remaining memory resource amount, the resource consumption includes the memory resource consumption, and the remaining memory resource amount is greater than or equal to the memory resource consumption; and / or the available resource amount includes the remaining storage resource amount, the resource consumption includes the storage resource consumption, and the remaining storage resource amount is greater than or equal to the storage resource consumption; and / or the available resource amount includes the remaining network traffic, the resource consumption includes the network traffic consumption, and the remaining network traffic is greater than or equal to the network traffic consumption.

[0012] In a possible implementation, the task configuration information includes task execution items and task execution duration of the task, and the method further includes: inputting the task execution items and the task execution duration into a resource prediction model to obtain the resource consumption.

[0013] In one possible implementation, the method further includes: sending control information for instructing the R target vehicles to execute the task; obtaining task execution progress of the R target vehicles; and adjusting the number of target vehicles executing the task based on the task execution progress. By globally monitoring the task execution progress and adjusting the number of target vehicles based on the task execution progress, the task can be completed on schedule.

[0014] In a possible implementation, the task is a vehicle data collection task.

[0015] A second aspect provides a data processing device. The device includes: an acquisition module for acquiring task configuration information of a task; a processing module for determining, from among N vehicles, R target vehicles to perform the task based on the task configuration information and vehicle information corresponding to the N vehicles, wherein the vehicle information includes resource information indicating the amount of available resources of the corresponding vehicle, the amount of available resources of each of the R target vehicles being greater than or equal to the amount of resource consumption corresponding to the task, the amount of resource consumption being obtained based on the task configuration information; N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1.

[0016] In one possible implementation, the task configuration information includes a first condition that needs to be met by a vehicle to perform the task, the first condition including target parameter information and / or target position information of the vehicle, and the vehicle information including parameter information and / or position information of the corresponding vehicle; the processing module is used to determine M vehicles corresponding to the M vehicle information that match the first condition among the vehicle information corresponding to the N vehicles; M is less than or equal to N, and M is an integer less than or equal to 1; the processing module is used to determine the R target vehicles among the M vehicles.

[0017] In a possible implementation, the processing module is configured to determine the R target vehicles among the M vehicles based on the resource information and the resource consumption.

[0018] In one possible implementation, the vehicle information includes usage information of the corresponding vehicle; the processing module is used to determine K vehicles corresponding to K vehicle information that matches a second condition among the vehicle information corresponding to the M vehicles, and the second condition indicates the target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; the processing module is used to determine the R target vehicles among the K vehicles based on the resource information and the resource consumption.

[0019] In one possible implementation, the available resource amount includes the remaining computing resource amount, the resource consumption includes the computing resource consumption, and the remaining computing resource amount is greater than or equal to the computing resource consumption; and / or the available resource amount includes the remaining memory resource amount, the resource consumption includes the memory resource consumption, and the remaining memory resource amount is greater than or equal to the memory resource consumption; and / or the available resource amount includes the remaining storage resource amount, the resource consumption includes the storage resource consumption, and the remaining storage resource amount is greater than or equal to the storage resource consumption; and / or the available resource amount includes the remaining network traffic, the resource consumption includes the network traffic consumption, and the remaining network traffic is greater than or equal to the network traffic consumption.

[0020] In one possible implementation, the task configuration information includes the task execution items and the task execution duration of the task; the processing module is used to input the task execution items and the task execution duration into a resource prediction model to obtain the resource consumption.

[0021] In one possible implementation, the device also includes a transceiver module; the transceiver module is used to send control information, and the control information is used to instruct the R target vehicles to perform the task; the acquisition module is used to obtain the task execution progress of the R target vehicles in performing the task; and the processing module is used to adjust the number of target vehicles performing the task according to the task execution progress.

[0022] In a possible implementation, the task is a vehicle data collection task.

[0023] The third aspect provides a device comprising a processor and a memory, wherein the processor is coupled to the memory, and the processor is configured to execute the data processing method in the first aspect or any possible implementation of the first aspect based on instructions stored in the memory.

[0024] The fourth aspect provides a computer-readable storage medium, characterized in that a computer program or instruction is stored in the computer storage medium, and when the computer program or instruction is executed by a computer, the data processing method in the first aspect or any possible implementation of the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a schematic structural diagram of a data processing system provided by the present application;

[0026] FIG2 is a schematic diagram of the software structure of a task management system provided by this application;

[0027] FIG3 is a flow chart of a data processing method provided by the present application;

[0028] FIG4 is a schematic structural diagram of a data processing device provided by the present application;

[0029] FIG5 is a schematic structural diagram of a device provided in this application. DETAILED DESCRIPTION

[0030] The following describes the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present application, rather than all the embodiments. Those skilled in the art will appreciate that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0031] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. "Multiple" means greater than or equal to two.

[0032] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0033] With the advancement of technology and the increasing intelligence of vehicles, data collected from vehicles can be used to update and iterate vehicle functions and services based on this data, making vehicles safer and more comfortable to use. Real-time data collected by vehicles can also be used to understand traffic conditions, assisting traffic managers in timely traffic scheduling, thereby improving road safety and traffic efficiency. Specifically, the following scenarios can enhance the driver's driving experience by collecting and processing vehicle data.

[0034] 1. Intelligent Traffic Management System:

[0035] System Architecture: A traffic management center that integrates cloud computing, in-vehicle communication technology, and big data analytics. Through this system, city traffic managers can target specific vehicle types or vehicle clusters with specific data collection tasks, such as traffic flow monitoring, road condition assessment, or driving behavior statistics.

[0036] Application scenarios: For example, when traffic congestion occurs on a certain road section, managers can quickly assign tasks to vehicles in the area to collect data, such as whether there is construction or changes to the road structure. This requires collecting data on lane markings, curbs, traffic lights, and traffic signs, enabling city traffic managers to understand real-time traffic conditions and then develop effective traffic scheduling strategies based on the collected data.

[0037] 2. Vehicle health monitoring system:

[0038] System Architecture: A comprehensive system using on-board sensors and cloud computing allows automakers to monitor the health and performance of vehicles on the road in real time.

[0039] Application scenario: Imagine a new batch of vehicle models has just been launched, and the manufacturer wants to track and evaluate their performance and durability. The manufacturer can send data collection tasks for these specific models, such as engine performance, fuel efficiency, and component wear. The manufacturer can then use the collected data to optimize products or identify potential quality issues in advance.

[0040] 3. In-vehicle entertainment and information service system:

[0041] System architecture: A service platform connecting the in-vehicle infotainment system and cloud content providers.

[0042] Application Scenario: Providers of in-vehicle infotainment systems want to understand user habits and preferences to optimize content recommendations or add new features. They can send data collection tasks to users' vehicles to collect information about how users interact with the system, the features or content types they use most frequently, and then use this data to provide users with more personalized services or content recommendations.

[0043] How to collect data efficiently and with high quality has become a problem that needs to be solved urgently. In order to solve this problem, the present application provides the following embodiments.

[0044] As shown in Figure 1, Figure 1 is a structural diagram of a data processing system provided by the present application. The data processing system shown in Figure 1 includes a task management device and a plurality of task execution devices. The task management device is, for example, a server, a server cluster, a computer, a tablet computer, a smart phone and other devices. The task execution device is, for example, a vehicle. In the present application, the vehicle can be any type of vehicle, such as a motor vehicle (such as a car, a truck, a bus), a small boat or a large ship, a submarine, an airplane, storage equipment, construction equipment, a tractor or other farm equipment. The vehicle in the present application is not limited to a vehicle or any specific type of vehicle, but can be applied to other real or virtual objects as well as non-passenger vehicles and passenger vehicles. The vehicle described in the present application may also refer to an unmanned aerial vehicle (UAV) or other object.

[0045] The task execution device is configured to obtain its own vehicle status information and transmit the vehicle status information to the task management device. Vehicle status information may include, for example, vehicle resource information and usage information. The vehicle resource information may include the vehicle's available resources. The available resources may be the vehicle's remaining resources or the ratio of the remaining resources to the vehicle's total resources. Resources may include at least one of computing resources, memory resources, storage resources, and traffic resources. Traffic resources may be used for communication between the vehicle and the task management device. For example, the vehicle's resource information may include the remaining computing resources of a central processing unit (CPU), an artificial intelligence (AI) chip (such as a graphics processing unit (GPU), a tensor processing unit (TPU), or a neural processing unit (NPU)). The vehicle's resource information may also include the remaining memory resources, available storage resources, and remaining network traffic. The vehicle's resource information may also include the vehicle's used resources. Based on the vehicle's total resources and the vehicle's used resources, the task management device may further determine the vehicle's available resources. Optionally, the vehicle's resource information may also include information about the lifespan or health of the hardware providing the resources, such as the lifespan and health of storage devices such as universal flash storage (UFS) (e.g., the remaining number of erase / write cycles of the UFS or the number of times the UFS has been used). This allows subsequent selection of target vehicles for task execution based on the UFS's lifespan and health information to control and manage the vehicle's UFS erase / write cycles and ensure the UFS's service life. For example, if the vehicle's UFS's remaining erase / write cycles are lower than expected, the vehicle may not be selected for the task.

[0046] Usage information indicates the user's habits / preferences for using the vehicle. For example, usage information includes the user's driving style (such as aggressive, standard or conservative, etc.), usage habits of the intelligent driving system (whether the intelligent driving system is used, the frequency / proportion of use of the intelligent driving system, the scenarios in which the intelligent driving system is used (garage, rural roads, urban areas or highways, etc.)), usage habits of the smart cockpit, etc. Optionally, usage information may also include difficult case distribution information. Difficult cases may include complex environments or scenarios in which the vehicle appears in real use, or environments or scenarios that did not appear or are difficult to reproduce during testing, such as traffic congestion, the car in front opening the door in the middle of the road, unknown obstacles on the road, unusual vehicles, sudden braking, etc. The difficult case distribution information indicates the frequency or probability of the vehicle encountering difficult cases. The vehicle status information may also include vehicle location information. The vehicle location information may be low-precision location information, and the vehicle location information may include, for example, the province or city where the vehicle appears frequently or is located. Optionally, the vehicle status information may also include tasks that the vehicle has been configured for.

[0047] Vehicle status information is dynamic information, and the task execution device can periodically send it to the task management device. For example, a vehicle can send its status information to the task management device on a half-hourly, one-hour, two-hour, four-hour, twelve-hour, one-day, two-day, three-day, or weekly basis. Of course, the task execution device can also send vehicle status information to the task management device aperiodically. For example, a vehicle can send its latest resource information to the task management device when its available resources / used resources reach a threshold, send its latest usage information when its usage information changes, send its latest hard case distribution information when its hard case distribution information changes, and so on. This allows the task management device to obtain the vehicle status information of each vehicle in a timely manner and select a target vehicle currently suitable for task execution based on the vehicle status information. The transmission frequencies of the resource information, usage information, and vehicle location information described above can be the same or different, and are not limited here. At least two of the resource information, usage information, and vehicle location information can be sent to the task management device via the same message. Alternatively, the task execution device can send the resource information, usage information, and vehicle location information to the task management device separately.

[0048] Optionally, the task execution device may also send vehicle parameter information to the task management device. Vehicle parameter information may include vehicle hardware parameter information, such as at least one of the vehicle model, sensor configuration, computing power configuration, memory configuration, and traffic configuration. Vehicle sensors may include, for example, at least one of an onboard camera, millimeter-wave radar, ultrasonic radar, infrared sensor, gravity sensor, distance sensor, acceleration sensor, angular velocity sensor, accelerometer, speedometer, odometer, inertial measurement unit, microphone, and touchscreen. Sensor configuration may include, for example, at least one of the sensor's function, model, quantity, and sensor deployment location on the vehicle. Computing power configuration may include, for example, the processor computing power configuration of a computing unit such as a CPU, GPU, TPU, or NPU. Vehicle parameter information may also include vehicle software parameter information, such as software version. For example, software parameter information may include at least one of the vehicle system version number, intelligent driving software version number, and autonomous emergency braking (AEB) system version number.

[0049] Generally speaking, the hardware parameter information in the vehicle parameter information no longer changes after the vehicle leaves the factory, and the vehicle parameter information does not need to be sent multiple times. The vehicle's software parameter information can send the latest software parameter information to the task management device when the software version number changes. Thereby, the communication overhead between the vehicle and the task management device can be reduced. Of course, the vehicle parameter information can also be sent to the task management device periodically, which is not limited here. The vehicle status information and vehicle parameter information sent by the task execution device to the task management device can be carried in the same message or in different messages, which is not limited here. The vehicle information (including vehicle status information and / or vehicle parameter information) sent by the vehicle to the task management device can include the vehicle identification of the vehicle, so that the task management device can determine which vehicle the vehicle information comes from based on the vehicle identification, and can subsequently query the corresponding vehicle or vehicle information based on the vehicle identification.

[0050] The task management device is configured to store vehicle information for N vehicles, where N is an integer greater than or equal to 2. The task management device is further configured to, upon receiving task configuration information corresponding to a task, determine R target vehicles from the N vehicles to execute the task based on the task configuration information and the vehicle information.

[0051] Among them, the user can input the task configuration information through the visual interface. The task configuration information may include at least one of the first condition that the vehicle that performs the task needs to meet, the task execution logic, and the task execution items. The first condition is a static condition or basic condition that the user expects the vehicle that performs the task to meet, such as at least one of the hardware conditions, software version conditions, and location conditions that the vehicle that performs the task needs to meet. For example, the first condition may include at least one of the target vehicle model, target software version, and the country / province / city where the vehicle is active. It should be noted that the static condition here does not mean that the first condition only includes conditions that will not change. The first condition may also include conditions that will not change in real time or frequently, such as the software version and the location of the vehicle.

[0052] Task execution logic may include, for example, the trigger conditions that trigger the vehicle to execute the task and / or the frequency / interval at which the task is executed. Task execution items may include the multiple subtasks that make up the task. For example, for a data collection task, the task execution items may include which sensor data the task needs to collect and report.

[0053] For example, for a data collection task in a difficult scenario where a vehicle opens its door on the road, the trigger condition can be that the vehicle is moving (speed > 0), the obstacle on the road is a car, and the door of the car is open. The frequency of executing this task can be 10 times per second. That is, when it is determined that a vehicle has opened its door on the road, the current environmental data is collected at a rate of 10 times per second. The task execution items of this task may include obtaining and reporting the current environmental data collected by sensors such as the vehicle's front camera, vehicle lidar, vehicle front fisheye camera, and vehicle sensor calibration data.

[0054] Optionally, the task configuration information may also include the total number of vehicles involved in executing the task and / or the total number of times the task has been executed. Taking a data collection task as an example, the task configuration information may include the total number of vehicles involved in collecting data and / or the amount of target data collected (data collected after the trigger conditions are met). Optionally, the task configuration information may also include the task execution deadline. The task execution deadline may include the task start time and the task end time. The task execution duration can be determined based on the task start time and the task end time.

[0055] After obtaining the task configuration information, the task management device can screen the N vehicles multiple times based on the task configuration information and the vehicle information of the N vehicles, so that the screened target vehicles can complete the task efficiently and accurately. Specifically, the task management device can screen out M vehicles that meet the first condition from the N vehicles based on the first condition in the task configuration information and the vehicle information of the N vehicles, and the M vehicles are candidate vehicles for performing the task. For example, the vehicle information includes the vehicle model, vehicle location information, and the intelligent driving software version number. The first condition includes the target vehicle model (for example, the Wenjie M9 MAX version), the target location (for example, Shenzhen), and the target intelligent driving version number (for example, ADS3.0). According to the first condition and the vehicle information of the N vehicles, the vehicle that matches the first condition is screened out from the N vehicles, that is, the vehicle information indicates that the vehicle model is the Wenjie M9 MAX version, the vehicle location is in Shenzhen, and the intelligent driving version number is ADS3.0.

[0056] Furthermore, the task management device selects R target vehicles capable of performing the task from the M vehicles based on the vehicle status information.

[0057] In one possible implementation, the task configuration information includes the task execution duration, task execution logic, and task execution items, and the task management device determines the R target vehicles from the M vehicles based on the resource information, task execution logic, and task execution items. Specifically, the task management device determines the resource consumption required for the vehicle to execute the task based on the task execution duration, task execution logic, and task execution items. Resource consumption, for example, includes at least one of computing resource consumption, memory resource consumption, storage resource consumption, and traffic consumption. Optionally, the task management device inputs information such as the task execution logic and task execution items into a resource prediction model, and the resource prediction model outputs the resource consumption corresponding to the vehicle executing the task. The resource prediction model is, for example, a hardware-in-the-loop (HIL) simulation verification model. The HIL simulation verification model provides a hardware simulation environment that is the same as that of a real vehicle, so that the HIL simulation verification model can predict the resource consumption corresponding to the real vehicle executing the task based on information such as the task execution logic and task execution items.

[0058] After obtaining the resource consumption corresponding to the task, the task management device compares the resource consumption with the available resources corresponding to the M vehicles, thereby determining the R target vehicles from the M vehicles. The available resources of each of the R target vehicles are greater than or equal to the resource consumption. When the resource consumption includes at least two of the following: computing resource consumption, memory resource consumption, storage resource consumption, and traffic consumption, and the available resources include at least two corresponding to the following: remaining computing resources, remaining memory resources, remaining storage resources, and remaining network traffic, and at least two of the vehicle's available resources are greater than or equal to at least two of the resource consumptions, the vehicle is determined to be a target vehicle.

[0059] Therefore, the R target vehicles determined based on the vehicle resource information and the resource consumption of the task can meet the resources required to perform the task, reduce the risk of task interruption and inability to proceed normally due to insufficient vehicle resources, ensure that the vehicle can smoothly perform the task, and improve the efficiency of task completion.

[0060] In another possible implementation, the task configuration information includes task execution logic and task execution items. The task management device determines the R target vehicles from the M vehicles based on resource information, usage information, task execution logic, and task execution items. Specifically, the task management device determines resource consumption based on the task execution logic and task execution items. Based on the resource information and resource consumption, the task management device determines P vehicles from the M vehicles. Then, the task management device determines the R target vehicles from the P vehicles based on the usage information to select target vehicles whose usage preferences align with the task, further improving task completion efficiency and quality. A second condition is obtained based on the task characteristics, and R target vehicles from the P vehicles whose usage information matches the second condition are identified. For example, if the task is to collect environmental data when a vehicle triggers AEB, the second condition may include an AEB trigger rate greater than an AEB trigger rate threshold, thereby selecting target vehicles that are more likely to trigger AEB, improving data collection efficiency and quality. Of course, it is also possible to first determine K vehicles from the M vehicles according to the second condition, and then determine R target vehicles from the K vehicles according to the resource information and resource consumption. There is no limitation here.

[0061] Optionally, the task management device can divide the task to complete the task in batches. Since the target vehicles are screened based on the vehicle status information, and the vehicle status information changes dynamically, it may result in different vehicles meeting the conditions for executing the task in different time periods. Therefore, the target vehicles for executing the task can be determined in batches and time periods, and the target vehicles for executing the task can be dynamically adjusted to improve the efficiency and quality of task completion. For example, if the task configuration information indicates that 1,000 vehicles are to perform the task, and at time A it is determined that the number of target vehicles that meet the conditions for executing the task is 300, then the 300 target vehicles can first perform the task; at time B it is determined that the number of target vehicles that meet the conditions for executing the task is 400, then it can be determined that the 400 target vehicles will continue to perform the task. When there is overlap between target vehicles in different batches, and there is overlap in the time when different batches execute tasks, the target vehicles in different batches can be deduplicated.

[0062] After determining R target vehicles, the task management device sends control information to the R target vehicles, instructing them to execute the task. This control information, for example, includes the task execution logic and task execution items, enabling the target vehicles to execute the task according to the task execution logic and task execution items. If the task involves vehicle data collection, the target vehicles can send the collected target data to the task management device in real time or with a delay. Alternatively, the target vehicles can also send the collected target data to a data storage device in real time or with a delay.

[0063] Optionally, the task management device can also obtain the task execution progress of R target vehicles performing the task, and adjust the number of target vehicles performing the task according to the task execution progress. Taking the vehicle data collection task as an example, if the target vehicle sends the collected target data to the task management device, the task management device can determine the task execution progress based on the number of target data currently received and the number of target data indicated by the task configuration information. Alternatively, when the target vehicle sends the target data to the data storage device, the task management device can query the data storage device for the current task execution progress. If the remaining time of the task is less than the time threshold and the task execution progress is less than the progress threshold, the task management device can increase the number of target vehicles (the process of screening the newly added target vehicles can be found in the above description and will not be repeated here) to improve the task execution efficiency and ensure that the task is completed within the task execution period.

[0064] The task management device can notify the target vehicle to stop executing the task when the task execution progress reaches 100% or the time reaches the task end time of the task execution deadline.

[0065] In this embodiment, the vehicle information includes the resource information of the vehicle, so that according to the task configuration information of the task, it is possible to screen out the target vehicle with sufficient available resources to support the completion of the task from N vehicles, thereby ensuring that the target vehicle can complete the task and improving the efficiency and quality of task completion. In addition, the task management device will globally monitor and automatically adjust the progress of task execution until the preset goal is reached, greatly reducing the need for manual maintenance and thus reducing the overall investment cost. When creating tasks and screening target vehicles, the real-time status of the vehicle (vehicle status information) is used as the decision basis for screening target vehicles, realizing asynchronous processing of tasks, thereby improving the overall task execution efficiency. By storing the vehicle information of the vehicle, the maintenance complexity of massive vehicle fleets is reduced, and the need for manual intervention and management is reduced.

[0066] As shown in Figure 2, which is a schematic diagram of the software architecture of a task management system provided by this application, the task management system includes a user interaction module, a task automation care module, a task issuing module, and a vehicle information maintenance module.

[0067] The user interaction module is used to provide a user graphical interface to receive task configuration information input by the user.

[0068] The vehicle information maintenance module is used to store the vehicle information of N vehicles and dynamically adjust the vehicle information of the corresponding vehicle according to the latest vehicle information sent by the vehicle, so that the vehicle information maintenance module stores the latest vehicle information.

[0069] The task automation care module is used to screen the target vehicles for executing the task based on the task configuration information and the vehicle information of N vehicles in the vehicle information maintenance module. The method for the task automation care module to screen the target vehicles can be found in the relevant description of the task management device in Figure 1 and will not be repeated here. The automated care module matches the vehicle information maintenance module according to the set cycle or trigger conditions (such as the completion of the task with the highest priority), and uses the task configuration information to determine the target vehicles suitable for executing the task. The task automation care module can also manage the execution and completion of the task, and will no longer execute the task after the task execution deadline is reached or the predetermined goal is achieved. The task automation care module monitors the overall task execution progress and adjusts the number of vehicles executing the task according to the task execution progress to ensure that the task is completed within the specified task execution deadline. The vehicle selection strategy can be random batches, reverse order by remaining resources, etc.

[0070] The task delivery module is used to send control information to the target vehicle to instruct it to execute the task. The vehicle control information includes the task execution logic and task execution items.

[0071] As shown in FIG3 , FIG3 is a flow chart of a data processing method provided by the present application. The steps of this embodiment are performed by the task management device in FIG1 . This embodiment includes the following steps:

[0072] S301: Obtain task configuration information of the task.

[0073] The task configuration information is, for example, input by a user through a user graphical interface, or the task configuration information may also be input through a configuration file corresponding to the task.

[0074] The task configuration information may include the first condition that the vehicle must meet to perform the task, the task execution items, the task execution duration (or task execution deadline), the task objectives (e.g., how many vehicles' data to collect, how much data to collect), etc. The task configuration information is described above and will not be repeated here.

[0075] S302: Based on the task configuration information and the vehicle information corresponding to the N vehicles, R target vehicles to perform the task are determined among the N vehicles. The vehicle information includes resource information indicating the available resource amount of the corresponding vehicle. The available resource amount of each of the R target vehicles is greater than or equal to the resource consumption corresponding to the task. The resource consumption amount is obtained based on the task configuration information. N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1.

[0076] In one possible implementation, the first condition includes target vehicle parameter information and / or target location information, and the vehicle information includes parameter information and / or location information of the corresponding vehicle. M vehicles corresponding to M pieces of vehicle information matching the first condition can then be determined from the N vehicles, and R target vehicles can be further determined from the M vehicles. The first condition is used to perform a preliminary screening of the N vehicles, thereby narrowing the screening process and ensuring that M vehicles meet the user's expectations.

[0077] Furthermore, R target vehicles are determined from the M vehicles based on the resource information and resource consumption. The method of selecting R target vehicles from the M vehicles based on the resource information and resource consumption can be found in the relevant description in FIG1 and will not be repeated here.

[0078] Optionally, R target vehicles can be determined from the M vehicles based on vehicle usage information. For example, K vehicles corresponding to K pieces of vehicle information matching a second condition are determined from the vehicle information corresponding to the M vehicles, where the second condition indicates target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; and R target vehicles are determined from the K vehicles based on resource information and resource consumption. Alternatively, P vehicles are determined from the M vehicles based on resource information and resource consumption, and R target vehicles are determined based on R pieces of vehicle information matching the second condition from the vehicle information corresponding to the P vehicles.

[0079] In this embodiment, the available resources include the remaining computing resources, the resource consumption includes the computing resource consumption, and the remaining computing resources of the target vehicle are greater than or equal to the computing resource consumption. The available resources include the remaining memory resources, the resource consumption includes the memory resource consumption, and the remaining memory resources of the target vehicle are greater than or equal to the memory resource consumption. The available resources include the remaining storage resources, the resource consumption includes the storage resource consumption, and the remaining storage resources of the target vehicle are greater than or equal to the storage resource consumption. The available resources include the remaining network traffic, the resource consumption includes the network traffic consumption, and the remaining network traffic of the target vehicle is greater than or equal to the network traffic consumption.

[0080] In this embodiment, vehicle information includes vehicle resource information. Based on the task configuration information, a target vehicle with sufficient available resources to complete the task can be selected from N vehicles, ensuring that the target vehicle can complete the task and improving the efficiency and quality of task completion. Furthermore, measurement information includes vehicle usage information. Based on this information, a target vehicle that is more likely to complete the task can be selected, thereby improving the efficiency and quality of task completion.

[0081] The following describes the device used to implement the above method in the embodiment of the present application with reference to the accompanying drawings.

[0082] As shown in FIG4 , which is a schematic diagram of the structure of a data processing device provided by the present application, the data processing device 400 includes an acquisition module 401 , a processing module 402 , and a transceiver module 403 .

[0083] An acquisition module 401 is used to acquire task configuration information of a task; a processing module 402 is used to determine R target vehicles that perform the task among the N vehicles based on the task configuration information and vehicle information corresponding to the N vehicles, wherein the vehicle information includes resource information indicating the available resource amount of the corresponding vehicle, and the available resource amount of each target vehicle in the R target vehicles is greater than or equal to the resource consumption corresponding to the task, and the resource consumption is obtained based on the task configuration information; N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1.

[0084] In one possible implementation, the task configuration information includes a first condition that needs to be met by a vehicle to perform the task, the first condition including target parameter information and / or target position information of the vehicle, and the vehicle information including parameter information and / or position information of the corresponding vehicle; the processing module 402 is used to determine M vehicles corresponding to the M vehicle information that match the first condition among the vehicle information corresponding to the N vehicles; M is less than or equal to N, and M is an integer less than or equal to 1; the processing module 402 is used to determine the R target vehicles among the M vehicles.

[0085] In a possible implementation, the processing module 402 is configured to determine the R target vehicles from the M vehicles according to the resource information and the resource consumption.

[0086] In one possible implementation, the vehicle information includes usage information of the corresponding vehicle; the processing module 402 is used to determine K vehicles corresponding to K vehicle information that matches a second condition among the vehicle information corresponding to the M vehicles, and the second condition indicates the target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; the processing module 402 is used to determine the R target vehicles among the K vehicles based on the resource information and the resource consumption.

[0087] In one possible implementation, the available resource amount includes the remaining computing resource amount, the resource consumption includes the computing resource consumption, and the remaining computing resource amount is greater than or equal to the computing resource consumption; and / or the available resource amount includes the remaining memory resource amount, the resource consumption includes the memory resource consumption, and the remaining memory resource amount is greater than or equal to the memory resource consumption; and / or the available resource amount includes the remaining storage resource amount, the resource consumption includes the storage resource consumption, and the remaining storage resource amount is greater than or equal to the storage resource consumption; and / or the available resource amount includes the remaining network traffic, the resource consumption includes the network traffic consumption, and the remaining network traffic is greater than or equal to the network traffic consumption.

[0088] In one possible implementation, the task configuration information includes the task execution items and the task execution duration of the task; the processing module 402 is used to input the task execution items and the task execution duration into a resource prediction model to obtain the resource consumption.

[0089] In one possible implementation, the device also includes a transceiver module 403; the transceiver module 403 is used to send control information, and the control information is used to instruct the R target vehicles to perform the task; the acquisition module 401 is used to obtain the task execution progress of the R target vehicles performing the task; the processing module 402 is used to adjust the number of target vehicles performing the task according to the task execution progress.

[0090] In a possible implementation, the task is a vehicle data collection task.

[0091] As shown in Figure 5, which is a schematic diagram of the structure of a device provided by this application, in this embodiment, the device 500 can be a server, server cluster, computer, tablet computer, smart wearable device, smart home device, car computer, smart phone, or other device with computing power.

[0092] The device 500 includes a bus 501 , a processor 502 , a communication interface 503 , and a memory 504 . The processor 502 , the memory 504 , and the communication interface 503 communicate with each other via the bus 501 .

[0093] Bus 501 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified as address buses, data buses, control buses, etc. For ease of illustration, FIG5 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0094] The processor 502 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0095] The memory 504 may include volatile memory, such as random access memory (RAM). The memory 504 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0096] The memory 504 may be used to store software codes related to the data processing method, and the processor 502 may execute the steps of the data processing method and may also schedule other units to implement corresponding functions.

[0097] It should be understood that the data processing device 500 can be a centralized or distributed device, and the processor 502 in the data processing device 500 can be a hardware circuit (such as an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a general-purpose processor, a digital signal processor (DSP), a microprocessor or a microcontroller, etc.), or a combination of these hardware circuits. For example, the processor can be a hardware system with an instruction execution function, such as a CPU, DSP, etc., or a hardware system without an instruction execution function, such as an ASIC, FPGA, etc., or a combination of the above-mentioned hardware systems without an instruction execution function and hardware systems with an instruction execution function.

[0098] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, implements the data processing method flow of any of the above-mentioned method embodiments.

[0099] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, implements the data processing method flow of any of the above-mentioned method embodiments.

[0101] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical or other forms.

[0103] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A data processing method, characterized in that: The method comprises: Get the task configuration information of the task; Based on the task configuration information and the vehicle information corresponding to N vehicles, R target vehicles that perform the task are determined among the N vehicles, the vehicle information includes resource information indicating the available resource amount of the corresponding vehicle, the available resource amount of each target vehicle in the R target vehicles is greater than or equal to the resource consumption corresponding to the task, and the resource consumption is obtained based on the task configuration information; N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1.

2. The method according to claim 1, characterized in that The task configuration information includes a first condition that a vehicle performing the task needs to meet, the first condition including target parameter information and / or target position information of the vehicle, the vehicle information including parameter information and / or position information of the corresponding vehicle, and determining R target vehicles to perform the task from the N vehicles based on the task configuration information and the vehicle information corresponding to the N vehicles, including: Determining M vehicles corresponding to M pieces of vehicle information matching the first condition among the vehicle information corresponding to the N vehicles; M is less than or equal to N, and M is an integer less than or equal to 1; The R target vehicles are determined among the M vehicles.

3. The method according to claim 2, characterized in that Determining the R target vehicles from the M vehicles includes: The R target vehicles are determined from the M vehicles according to the resource information and the resource consumption.

4. The method according to claim 2, characterized in that The vehicle information includes usage information of the corresponding vehicle, and determining the R target vehicles from the M vehicles includes: Determining K vehicles corresponding to K pieces of vehicle information matching a second condition among the vehicle information corresponding to the M vehicles, where the second condition indicates target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; The R target vehicles are determined from the K vehicles according to the resource information and the resource consumption.

5. The method according to any one of claims 1 to 4, characterized in that The available resource amount includes the remaining computing resource amount, the resource consumption amount includes the computing resource consumption amount, and the remaining computing resource amount is greater than or equal to the computing resource consumption amount; and / or The available resource amount includes the remaining memory resource amount, the resource consumption amount includes the memory resource consumption amount, and the remaining memory resource amount is greater than or equal to the memory resource consumption amount; and / or The available resource amount includes the remaining storage resource amount, the resource consumption amount includes the storage resource consumption amount, and the remaining storage resource amount is greater than or equal to the storage resource consumption amount; and / or The available resource amount includes remaining network traffic, the resource consumption amount includes network traffic consumption, and the remaining network traffic amount is greater than or equal to the network traffic consumption.

6. The method according to any one of claims 1 to 5, characterized in that The task configuration information includes the task execution items and task execution duration of the task, and the method further includes: The task execution item and the task execution duration are input into a resource prediction model to obtain the resource consumption.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Sending control information, where the control information is used to instruct the R target vehicles to perform the task; Obtaining the task execution progress of the R target vehicles in executing the task; The number of target vehicles executing the task is adjusted according to the task execution progress.

8. The method according to any one of claims 1 to 6, characterized in that The task is a vehicle data collection task.

9. A data processing device, characterized in that: The device comprises: The acquisition module is used to obtain the task configuration information of the task; A processing module is used to determine R target vehicles that perform the task among the N vehicles based on the task configuration information and vehicle information corresponding to the N vehicles, the vehicle information including resource information indicating the available resource amount of the corresponding vehicle, the available resource amount of each of the R target vehicles being greater than or equal to the resource consumption corresponding to the task, the resource consumption being obtained based on the task configuration information; N is an integer greater than or equal to 2, and R is an integer greater than or equal to 1.

10. The device according to claim 9, characterized in that The task configuration information includes a first condition that needs to be met by a vehicle performing the task, the first condition including target parameter information and / or target position information of the vehicle, and the vehicle information includes parameter information and / or position information of the corresponding vehicle; The processing module is configured to determine M vehicles corresponding to M pieces of vehicle information matching the first condition among the vehicle information corresponding to the N vehicles; M is less than or equal to N, where M is an integer less than or equal to 1; The processing module is used to determine the R target vehicles among the M vehicles.

11. The device according to claim 10, characterized in that The processing module is used to determine the R target vehicles among the M vehicles according to the resource information and the resource consumption.

12. The device according to claim 10, characterized in that The vehicle information includes usage information of the corresponding vehicle; The processing module is configured to determine K vehicles corresponding to K pieces of vehicle information matching a second condition among the vehicle information corresponding to the M vehicles, wherein the second condition indicates target usage information required to perform the task; K is less than or equal to M, and K is an integer less than or equal to 1; The processing module is used to determine the R target vehicles from the K vehicles according to the resource information and the resource consumption.

13. The device according to any one of claims 9 to 12, characterized in that The available resource amount includes the remaining computing resource amount, the resource consumption amount includes the computing resource consumption amount, and the remaining computing resource amount is greater than or equal to the computing resource consumption amount; and / or The available resource amount includes the remaining memory resource amount, the resource consumption amount includes the memory resource consumption amount, and the remaining memory resource amount is greater than or equal to the memory resource consumption amount; and / or The available resource amount includes the remaining storage resource amount, the resource consumption amount includes the storage resource consumption amount, and the remaining storage resource amount is greater than or equal to the storage resource consumption amount; and / or The available resource amount includes remaining network traffic, the resource consumption amount includes network traffic consumption, and the remaining network traffic amount is greater than or equal to the network traffic consumption.

14. The device according to any one of claims 9 to 13, characterized in that The task configuration information includes the task execution items and task execution duration of the task; The processing module is used to input the task execution item and the task execution duration into a resource prediction model to obtain the resource consumption.

15. The device according to any one of claims 9 to 14, characterized in that The device also includes a transceiver module; The transceiver module is used to send control information, and the control information is used to instruct the R target vehicles to perform the task; The acquisition module is used to obtain the task execution progress of the R target vehicles in performing the task; The processing module is used to adjust the number of target vehicles executing the task according to the task execution progress.

16. The device according to any one of claims 9 to 15, characterized in that The task is a vehicle data collection task.

17. A device, characterized in that The device includes a processor and a memory, wherein the processor is coupled to the memory, and the processor is configured to execute the data processing method according to any one of claims 1 to 8 based on instructions stored in the memory.

18. A computer-readable storage medium, characterized in that The computer storage medium stores a computer program or instruction. When the computer program or instruction is executed by a computer, the data processing method according to any one of claims 1 to 8 is implemented.

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