Intelligent data sensing data task distribution processing system and method

Abstract

The invention belongs to the technical field of data processing and task scheduling, and discloses an intelligent data sensing data task allocation processing system and method, which comprises a data acquisition module, a dynamic terminal identification module, a task allocation and scheduling module, a sharing management module and a privacy and user control module, a multi-vehicle sensing collaborative network is constructed through the data acquisition module and the dynamic terminal identification module, data sharing and fusion of vehicles of the same intelligent driving system are realized, repeated acquisition and calculation are reduced, and calculation power and storage resources are saved; the task evaluation and priority distribution module is combined with the task distribution and scheduling module, adaptive distribution and continuous execution of tasks are realized through multi-stage sharing, relay forwarding and caching mechanisms, and the problem of slow response of a fixed strategy is solved; and the sharing management module and the privacy and user control module provide connection number limitation, computing power control and privacy-removing sharing, and both real-time performance and privacy are considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and task scheduling, and particularly relates to a data task allocation processing system and method based on intelligent data sensing. BACKGROUND

[0002] With the development of Internet of Things, artificial intelligence and Internet of Vehicles technologies, more and more application scenarios require real-time collection, analysis and processing of multi-source sensing data, and intelligent scheduling according to computing resources and task types. The existing data task allocation system usually includes data collection, task identification and task processing modules, which realize data analysis and decision execution by classifying and allocating sensing data. In typical applications, intelligent driving vehicles need to process multi-source sensing data from vehicle-mounted cameras, millimeter wave radars, laser radars, GNSS and Vehicle-to-Everything (V2X) while driving in complex environments such as highways, and complete tasks such as obstacle detection, lane line identification and path planning under limited computing power. Therefore, data sensing efficiency and task allocation strategy directly affect the overall operation performance and driving safety of the system.

[0003] The existing intelligent data sensing and task allocation system still has the following deficiencies in practical application: first, there is a lot of repeated collection and processing of multi-source sensing data between different terminals, resulting in waste of computing power and storage resources; second, the task allocation strategy of the existing system is mostly fixed rules, lacking self-adaptive ability to data characteristics, computing power load and environmental changes, and when the scene changes dynamically, the task processing efficiency and response speed are likely to decrease; third, in application scenarios requiring high real-time performance and high reliability, such as high-speed driving conditions of intelligent driving vehicles, the existing system is difficult to balance processing efficiency and task scheduling flexibility, thereby limiting the improvement of overall sensing and decision performance.

[0004] Therefore, there is an urgent need for a data task allocation processing system and method based on intelligent data sensing, which can realize efficient processing and flexible scheduling of multi-source sensing data, improve task allocation efficiency and operation reliability in dynamic, multi-terminal and high real-time application scenarios, to meet the requirements of intelligent driving and other complex scenarios for sensing performance and safety. SUMMARY

[0005] The application aims to provide an intelligent data perception data task allocation processing system and method to solve the problems in the background art, build a multi-vehicle perception collaborative network through a data acquisition module and a dynamic terminal identification module, realize data sharing and fusion of vehicles of the same intelligent driving system, reduce repeated acquisition and calculation, and save computing power and storage resources; a task evaluation and priority allocation module is combined with a task allocation and scheduling module to realize adaptive task allocation and continuous execution through a multi-level sharing, relay forwarding and caching mechanism to solve the problem of slow response of fixed strategies; a sharing management module and a privacy and user control module provide connection number limitation, computing power control and de-privatization sharing, and both real-time performance and privacy are taken into account; the scheme can significantly improve perception efficiency, task scheduling flexibility and driving safety in dynamic multi-terminal scenes such as highways.

[0006] To achieve the above-mentioned purpose, the application provides the following technical scheme: An intelligent data perception data task allocation processing system comprises a data acquisition module, a dynamic terminal identification module, a task allocation and scheduling module, a sharing management module and a privacy and user control module, the data acquisition module is used to acquire multi-source perception data of a vehicle, including camera data, radar data, GNSS position information and Internet of Vehicles communication data, the dynamic terminal identification module is used to identify other vehicles deployed with the same intelligent driving system within a preset communication range, and based on received position information, speed and lane data, determine the dynamic front vehicle and auxiliary front vehicle topological relationship related to the vehicle, the task evaluation and priority allocation module is used to score and prioritize the collected data tasks, and generate a task scheduling strategy based on the computing load and the environment state, the task allocation and scheduling module is used to allocate data tasks as local processing tasks, shared tasks or forwarding tasks according to the priority and the current communication state, the sharing management module is used to control the number and frequency of data sharing between the vehicle and other vehicles, and support maximum connection number limitation and computing power load control, and the privacy and user control module is used to enable or disable the data sharing function for the user, and de-privatize the shared data.

[0007] Preferably, the dynamic terminal identification module receives position information, speed and lane data of vehicles deployed with the same intelligent driving system in real time based on a V2V communication protocol, dynamically identifies front vehicles and automatically completes front vehicle switching.

[0008] Preferably, the sharing management module sets a maximum collaborative connection number threshold, and vehicles exceeding the threshold forward data through adjacent vehicles as relays to avoid overloading the computing power of the vehicle.

[0009] Preferably, the sharing management module dynamically adjusts the sharing frequency and resolution of non-critical task data according to the computing power load to ensure the real-time performance of critical tasks.

[0010] Preferably, the privacy and user control module de-privatizes the shared data, only sharing key information such as target detection results, path prediction, etc., and providing a user interface to allow the data sharing function to be turned on or off at any time.

[0011] Preferably, the task evaluation and priority assignment module assigns different priorities according to the type of task, with obstacle detection having the highest priority, lane line recognition having the second highest priority, and navigation information having the lowest priority.

[0012] Preferably, the task assignment and scheduling module adopts a multi-level sharing mechanism according to the priority of the task, with high-priority tasks being transmitted at high frequency and processed locally in real time, and low-priority tasks being transmitted at low frequency or relayed.

[0013] Preferably, the dynamic terminal identification module is provided with a short-term cache mechanism to ensure that task data is not interrupted and the task reassignment delay is minimized during the switching of the preceding vehicle.

[0014] An intelligent data sensing data task allocation processing method, comprising the following steps: S1: periodically broadcast and receive the position information, speed and lane data of vehicles equipped with the same intelligent driving system through V2V communication, and identify dynamic preceding vehicles and auxiliary preceding vehicles; S2: collect multi-source perception data of the vehicle, and determine the priority of the task based on the task evaluation module; S3: the preceding vehicle compresses and broadcasts high-value task data, and the following vehicle receives the data and fuses local perception to eliminate redundancy; S4: control the maximum number of connections and the sharing frequency, and vehicles exceeding the number of connections forward data through neighboring vehicles; S5: de-privatize the shared data, and enable or disable data sharing according to user settings; S6: when the preceding vehicle changes, dynamically complete data source switching and task reassignment, and use a cache mechanism to ensure that the task is not interrupted.

[0015] The technical solution has the following advantages compared with the prior art: (1) The present application constructs a multi-vehicle perception collaborative network through the data acquisition module and the dynamic terminal identification module, realizes the sharing and fusion of multi-source perception data of vehicles equipped with the same intelligent driving system, effectively reduces repeated collection and calculation, and significantly saves computing power and storage resources.

[0016] (2) The application combines the task evaluation and priority allocation module with the task allocation and scheduling module, dynamically generates a scheduling strategy according to the task type, data value, computing power load and environment state, and realizes adaptive allocation and continuous execution of tasks through a multi-level sharing, relay forwarding and caching mechanism, solving the problem of slow response of the traditional fixed allocation strategy in complex scenes.

[0017] (3) The application realizes maximum connection number limitation, computing power load control, data de-privatization and user-controllable sharing through the joint action of the sharing management module and the privacy and user control module, while ensuring high real-time performance and high reliability, the user privacy and use experience are also taken into account.

[0018] (4) The application can significantly improve the data sensing efficiency and task allocation flexibility in dynamic, multi-terminal and high real-time scenes such as highways, solving the defects of repeated processing, response lag and computing power bottleneck in the prior art, and further improving the sensing accuracy, processing efficiency and driving safety in complex scenes such as intelligent driving. BRIEF DESCRIPTION OF DRAWINGS

[0019] Fig. 1 The system workflow diagram provided by the application is provided. Fig. 2 The system overall structure schematic diagram provided by the application is provided. Fig. 3 The sharing management and relay forwarding mechanism schematic diagram provided by the application is provided.

[0020] Reference signs: 1, data acquisition module; 2, dynamic terminal identification module; 3, task evaluation and priority allocation module; 4, task allocation and scheduling module; 5, sharing management module; 6, privacy and user control module. DETAILED DESCRIPTION

[0021] With the development of Internet of Things, artificial intelligence and Internet of Vehicles technology, the real-time collection, analysis and intelligent scheduling of multi-source sensing data are increasingly in demand. Typical scenarios such as intelligent driving vehicles driving on highways need to process multi-source sensing data from cameras, millimeter wave radars, laser radars, GNSS and V2X, and complete tasks such as obstacle detection, lane line identification and path planning under limited computing power conditions. Therefore, data sensing efficiency and task allocation strategy directly affect driving safety and system performance.

[0022] Existing intelligent data sensing and task allocation systems have three main shortcomings: First, there is a lot of repetitive data collection and processing across different terminals, resulting in a waste of computing power and storage. Second, task allocation strategies are mostly based on fixed rules and lack the ability to adapt to changes in data characteristics, computing load, and environment, which can easily lead to a decrease in response speed in dynamic scenarios. Third, in applications requiring high real-time performance and high reliability, it is difficult to balance processing efficiency and scheduling flexibility, thus limiting the improvement of overall sensing and decision-making performance.

[0023] Therefore, this invention provides an intelligent data perception data task allocation and processing system and method, which realizes efficient processing and flexible scheduling of multi-source data, and improves task allocation efficiency and operational reliability in dynamic, multi-terminal, and high real-time scenarios.

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments: like Figs. 1-3 The system and method for intelligent data perception and data task allocation and processing, as shown, include a data acquisition module 1, a dynamic terminal identification module 2, a task allocation and scheduling module 4, a sharing management module 5, and a privacy and user control module 6. The data acquisition module 1 is used to acquire multi-source perception data of the vehicle, including camera data, radar data, GNSS location information, and vehicle network communication data. The dynamic terminal identification module 2 is used to identify other vehicles with the same intelligent driving system deployed within a preset communication range, and to determine the dynamic leading vehicle and auxiliary leading vehicle topology relationship related to the vehicle based on the received location information, speed, and lane data. The task evaluation and priority allocation module 3 is used to score the value of the acquired data tasks and prioritize them, and to generate a task scheduling strategy based on the computing load and environmental status. The task allocation and scheduling module 4 is used to allocate data tasks as local processing tasks, sharing tasks, or forwarding tasks according to priority and current communication status. The sharing management module 5 is used to control the amount and frequency of data sharing between the vehicle and other vehicles, and supports maximum connection limit and computing load control. The privacy and user control module 6 is used to enable or disable the data sharing function for users and to perform de-privacy processing on the shared data.

[0025] Example 1: This system aims to solve the problems of redundant calculations, delayed task response, and limited computing resources in existing intelligent data perception and task allocation systems in high-speed, multi-terminal scenarios. It enables data perception collaboration and intelligent task scheduling among multiple intelligent driving vehicles, so that each vehicle is no longer an isolated computing unit, but forms a dynamic data sharing network with surrounding vehicles deployed with the same intelligent driving system, thereby improving overall perception efficiency and driving safety.

[0026] The system first acquires camera images, radar detection results, GNSS positioning information, and vehicle-to-everything (V2X) communication data through data acquisition module 1, providing a complete multi-source perception foundation for task processing. Dynamic terminal identification module 2 perceives the status of vehicles within the same intelligent driving system in real time within the communication range, determines the topological relationship between the lead vehicle and auxiliary lead vehicles based on position, speed, and lane data, and automatically switches when the lead vehicle changes lanes or traffic flow changes, ensuring the continuity of data sources and real-time compatibility of shared data (selecting the same intelligent driving system avoids delays and additional computing power consumption caused by inconsistent data formats).

[0027] The task evaluation and priority allocation module 3 scores and prioritizes data acquisition tasks based on task type, data value, computing load, and environmental conditions, ensuring that critical tasks such as obstacle detection are processed first. The task allocation and scheduling module 4, considering the current communication status, rationally divides tasks into local processing, sharing, or relay forwarding, forming an adaptive multi-level scheduling strategy to achieve dynamic balance between computing power and bandwidth. The sharing management module 5 controls the maximum number and frequency of data sharing connections and employs a nearby vehicle relay mechanism when thresholds are exceeded to avoid single-vehicle computing power overload and network congestion. The privacy and user control module 6 performs de-anonymization processing before data sharing, outputting only key information such as target detection results and path predictions, and allows users to enable or disable the sharing function at any time through the user interface, balancing data security and user experience.

[0028] Through the linkage of the above modules, this system can efficiently complete the sharing of multi-source perception data, adaptive task allocation, and optimized scheduling of computing power in dynamic, multi-terminal scenarios such as highways. This significantly reduces redundant data collection and calculation, lowers response latency, and improves the perception accuracy, safety, and system stability of intelligent driving, while also meeting the needs of user privacy protection.

[0029] The dynamic terminal identification module 2 is based on the V2V communication protocol. It receives the location information, speed and lane data of vehicles with the same intelligent driving system in real time, dynamically identifies the vehicle in front and automatically completes the switching of the vehicle in front.

[0030] Example 2: The design purpose of the dynamic terminal identification module 2 is to let the vehicle always know who the current most important front vehicle is. On the highway, the front vehicle is not fixed and can change lanes, slow down or be overtaken by other vehicles. The dynamic terminal identification module 2 periodically receives the speed, position and lane information of surrounding vehicles through V2V communication, and then automatically determines which vehicle is the main front vehicle of the vehicle and which vehicles are the auxiliary front vehicles. Once the original main front vehicle leaves, the module will immediately switch to the new front vehicle and keep the data continuous through a short-term caching mechanism. The beneficial effect is to ensure uninterrupted data sharing and reduce the perception gap caused by changes in the front vehicle. The dynamic terminal identification module 2 also works with the task allocation and scheduling module 4 to reassign tasks at the moment of switching to ensure smooth operation of the system.

[0031] The sharing management module 5 sets a maximum cooperative connection threshold. Vehicles exceeding the threshold forward data through neighboring vehicles as relay nodes to avoid overloading the vehicle's computing power.

[0032] Example 3: The design purpose of the sharing management module 5 is to prevent a front vehicle from being overwhelmed by providing data to too many vehicles at the same time. Its working principle is as follows: the sharing management module 5 sets the maximum number of vehicles that can simultaneously share data to 3. When the number of vehicles requesting connection exceeds this number, the vehicle will automatically select the nearest rear vehicle as a relay node to continue transmitting data to more distant vehicles. The beneficial effect of this design is to ensure that the front vehicle's computing power and bandwidth are not overloaded, while also making the data flow of the entire vehicle fleet more stable and dispersed. The sharing management module 5 also works with the dynamic terminal identification module 2 to ensure that the relay selection is always the nearest and most suitable rear vehicle, reducing latency.

[0033] The sharing management module 5 dynamically adjusts the sharing frequency and resolution of non-critical task data based on computing power load to ensure the real-time performance of critical tasks.

[0034] Example 4: The sharing management module 5 acts like an intelligent throttle. When the vehicle's computing resources are tight, it will prioritize high-frequency sharing of critical tasks such as obstacle detection and emergency braking. For low-priority tasks such as lane detection or navigation assistance, it will automatically reduce the sharing frequency or compress the data resolution. The beneficial effect is to ensure that critical tasks always respond quickly and avoid delays or missed detections due to overloaded computing power. The sharing management module 5 closely interacts with the task evaluation and priority allocation module 3 to automatically adjust resource allocation based on task value.

[0035] The privacy and user control module 6 de-privatizes shared data, only sharing critical information such as target detection results and path prediction, and provides a user interface to allow data sharing to be turned on or off at any time.

[0036] Example 5: The privacy and user control module 6 is designed to allow users to enjoy the benefits of cooperative driving without worrying about privacy leakage. The working principle is that the original image, in-vehicle camera or personal information is not directly shared, but only the result data such as target box and trajectory prediction is sent after local processing. At the same time, users can turn off or turn on the cooperative sharing mode on the central control interface. The beneficial effect is to improve user trust and system compliance. The privacy and user control module 6 cooperates with the sharing management module 5 to realize instant synchronization of user operation and sharing strategy.

[0037] The task evaluation and priority allocation module 3 assigns different priorities according to the type of task, with obstacle detection having the highest priority, lane line recognition having the second highest priority, and navigation information having the lowest priority.

[0038] Embodiment 6: The task evaluation and priority allocation module 3 is equivalent to the dispatcher of the system, which decides which tasks must be processed first and which tasks can be processed later or at a lower frequency. The working principle is that the task evaluation and priority allocation module 3 analyzes the safety importance of each perception task and ranks them, for example, front obstacle detection must have the highest priority because it is directly related to safety, while navigation information can be lower. The beneficial effect is to ensure that the system can also ensure normal operation of critical functions when the network or computing power is tight. The task evaluation and priority allocation module 3 works with the task allocation and scheduling module 4 to realize high-frequency sharing of high-priority tasks and low-frequency sharing of low-priority tasks.

[0039] The task allocation and scheduling module 3 adopts a multi-level sharing mechanism according to the priority of the task. High-priority tasks are transmitted at high frequency and processed locally in real time, while low-priority tasks are transmitted at low frequency or relayed.

[0040] Embodiment 7: The task allocation and scheduling module 4 is responsible for sending each task to the most suitable processing channel. The working principle is that high-priority tasks are directly processed locally and shared at high speed to other vehicles; medium and low-priority tasks can be delayed, reduced in frequency or forwarded through relay vehicles if the network is busy or the computing power is tight. The beneficial effect is to make full use of the computing resources of the entire vehicle network, avoid repeated calculation and communication bottlenecks, and the task allocation and scheduling module 4 closely cooperates with the dynamic terminal identification module 2 and the sharing management module 5 to ensure that the task always flows in the optimal path.

[0041] The dynamic terminal identification module 2 has a short-term cache mechanism to ensure that task data is not interrupted during the switching of the preceding vehicle and the delay of task reallocation is minimized.

[0042] Embodiment 8: The dynamic terminal identification module 2 is like a buffer, which avoids data interruption when the front vehicle is switched, and works as follows: the dynamic terminal identification module 2 buffers the latest key perception data in advance, and hands over the data to the task allocation and scheduling module 4 for continuous processing immediately after the front vehicle is switched, so that the rear vehicle almost does not feel the data interruption. The beneficial effect is to prevent the perception window from appearing when the vehicle is switched, and to improve the safety and continuity in the high-speed scene. The dynamic terminal identification module 2 cooperates with the sharing management module 5 to ensure smooth transition of the data flow.

[0043] A data task allocation processing method of intelligent data perception, comprising the following steps: S1: periodically broadcast and receive the position information, speed and lane data of the vehicles deployed with the same intelligent driving system through V2V communication, and identify the dynamic front vehicle and the auxiliary front vehicle; S2: collect the multi-source perception data of the vehicle, and determine the task priority based on the task evaluation module; S3: the front vehicle compresses and broadcasts the high-value task data, and the rear vehicle receives the data and fuses the local perception to eliminate the redundancy; S4: control the maximum number of connections and the sharing frequency, and the vehicles exceeding the number of connections forward the data through the relay of the adjacent vehicles; S5: de-privatize the shared data, and enable or disable the data sharing according to the user settings; S6: when the front vehicle is switched, dynamically complete the data source switching and task re-allocation, and use the buffer mechanism to ensure that the task is not interrupted.

[0044] The above is only an embodiment of the present application, and the specific technical solutions and / or common knowledge of the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, some modifications and improvements can be made, which should also be considered as the protection scope of the present application, and these will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. An intelligent data-aware data task allocation processing system, characterized by, Comprise: Data acquisition module (1), the data acquisition module (1) is used to obtain the multi-source perception data of vehicle, including camera data, radar data, GNSS position information and telematics communication data; Dynamic terminal identification module (2), the dynamic terminal identification module (2) is used to identify other vehicles deployed with the same intelligent driving system within the preset communication range, and determine the dynamic front vehicle and auxiliary front vehicle topology related to the vehicle based on the received position information, speed and lane data; Task evaluation and priority assignment module (3), the task evaluation and priority assignment module (3) is used to value score and priority sort the collected data task, and generate task scheduling strategy based on the calculation load and environment state; Task allocation and scheduling module (4), the task allocation and scheduling module (4) is used to allocate data task as local processing task, shared task or forwarding task according to priority and current communication state; Shared management module (5), the shared management module (5) is used to control the data sharing quantity and frequency between vehicle and other vehicles, and support maximum connection number limit and computing power load control; Privacy and user control module (6), the privacy and user control module (6) is used to realize the enablement or shutdown of data sharing function by user, and de-privatize the shared data.

2. A system for intelligent data-aware data task allocation processing as claimed in claim 1, wherein: The dynamic terminal identification module (2) receives the position information, speed and lane data of vehicles deployed with the same intelligent driving system in real time based on V2V communication protocol, dynamically identifies the front vehicle and automatically completes the front vehicle switching.

3. The intelligent data-aware data task allocation processing system of claim 1, wherein: The shared management module (5) sets the maximum cooperative connection number threshold, and the vehicles exceeding the threshold forward data through adjacent vehicles as relay to avoid the overload of the vehicle computing power.

4. The intelligent data-aware data task allocation processing system of claim 1, wherein: The shared management module (5) dynamically adjusts the sharing frequency and resolution of non-critical task data according to the computing power load to ensure the real-time performance of critical task.

5. The intelligent data-aware data task allocation processing system of claim 1, wherein: The privacy and user control module (6) de-privatizes the shared data, only shares the key information such as target detection result and path prediction, and provides user interface to allow to start or stop data sharing function at any time.

6. A system for intelligent data-aware data task allocation processing as claimed in claim 1, wherein: The task evaluation and priority assignment module (3) assigns different priorities according to task type, wherein the obstacle detection priority is the highest, the lane line recognition is the second, and the navigation information priority is the lowest.

7. A system for intelligent data-aware data task allocation processing as claimed in claim 1, wherein: The task allocation and scheduling module (3) adopts multi-level sharing mechanism according to task priority, high priority task adopts high frequency transmission and local instant processing, and low priority task adopts low frequency transmission or relay sharing.

8. The intelligent data-aware data task allocation processing method of claim 1, wherein: The dynamic terminal identification module (2) is provided with short-term cache mechanism to ensure that task data is not interrupted and task reallocation delay is minimum during front vehicle switching.

9. The intelligent data-aware data task allocation processing method of any of claims 1-8, wherein, Comprise the following steps: S1: periodically broadcast and receive the position information, speed and lane data of vehicles deployed with the same intelligent driving system through V2V communication, identify dynamic front vehicle and auxiliary front vehicle; S2: collect multi-source perception data of vehicle, and determine task priority based on task evaluation module; S3: front vehicle selects high value task data for compression and broadcast, rear vehicle receives data and fuses local perception, and eliminates redundancy. S4: Control the maximum number of connections and sharing frequency, vehicles exceeding the number of connections relay data through adjacent vehicles; S5: De-privatize shared data and enable or disable data sharing according to user settings; S6: When the current vehicle changes, dynamically complete data source switching and task reallocation, and use the cache mechanism to ensure uninterrupted tasks.

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