Edge calculation method and system for low-altitude unmanned aerial vehicle real-time supervision task, terminal and storage medium

Abstract

The invention discloses an edge calculation method and system for a low-altitude unmanned aerial vehicle real-time supervision task, a terminal and a storage medium, and the method comprises the steps: carrying out the quantitative expression of the space-time dependence characteristics of the low-altitude unmanned aerial vehicle supervision task, and carrying out the quantitative evaluation of the low-altitude unmanned aerial vehicle supervision task, and obtaining a quantitative evaluation result; performing fine-grained decomposition of the edge end space-time dependence task according to the quantitative evaluation result, and decomposing the low-altitude unmanned aerial vehicle supervision task into a plurality of fine-grained sub-tasks; a fine-grained task execution plan dynamically adapting to resource constraint and calculation real-time performance in the edge environment is constructed, and a process-thread multi-granularity mixed edge end fine-grained task parallel execution strategy is established; and based on the priority and the constraint condition, carrying out preliminary allocation on the edge end computing resources, and carrying out dynamic allocation and adaptive optimization on the edge end computing resources. According to the method, the edge calculation real-time performance of large-scale low-altitude unmanned aerial vehicle supervision tasks can be remarkably improved, and the edge calculation resource utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of drone data processing technology, and in particular to an edge computing method, system, terminal and computer-readable storage medium for real-time monitoring tasks of low-altitude drones. Background Art

[0002] Real-time calculation and timely feedback of regulatory tasks such as drone anomaly detection and conflict warning are the key to ensuring the safe flight of drones. In solving the problem of multi-drone collisions, path planning algorithms and various collision avoidance decision algorithms based on artificial intelligence are currently mainly used, such as A* search algorithm (heuristic search algorithm), ant colony algorithm, particle swarm optimization algorithm, etc. The above algorithms all focus on pre-task planning, and have a certain delay in dealing with emergencies encountered when drones are carrying out tasks. In the face of unfamiliar mission environments, it is impossible to obtain a safe flight path through planning algorithms.

[0003] At present, the research on behavior anomaly detection is mostly focused on large drones, large manned aircraft, vehicles and crowds. The behavior anomaly detection algorithms for low-altitude small drones include the isolation forest algorithm and support vector machine. These methods require a large amount of drone data and a lot of training time, and cannot achieve real-time calculation and real-time feedback processing. At present, relevant drone supervision platforms have been developed, but most of the drone supervision tasks have problems such as poor real-time performance, and cannot respond to emergencies encountered during drone operation in a timely manner, and there is often a large delay when calculating large amounts of data.

[0004] Therefore, in order to solve the problem of large computational delays and inability to respond in a timely manner in large-scale low-altitude UAV supervision tasks, it is urgent to use edge computing technology to optimize the spatiotemporal flow computing mode of supervision tasks such as UAV anomaly detection and early warning conflicts, so as to improve the real-time performance of large-scale low-altitude UAV supervision tasks. Real-time computing and timely feedback on supervision tasks such as anomaly detection and conflict prediction at the edge have obvious advantages. However, under the constraints of limited computing and storage resources of edge devices, there are still many challenges in achieving real-time response of local UAV supervision tasks.

[0005] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0006] The main purpose of the present invention is to provide an edge computing method, system, terminal and computer-readable storage medium for real-time supervision tasks of low-altitude UAVs, aiming to solve the problems of large calculation delays and failure to respond in a timely manner for large-scale low-altitude UAV supervision tasks in the prior art.

[0007] To achieve the above object, the present invention provides an edge computing method for real-time monitoring tasks of low-altitude UAVs, and the edge computing method for real-time monitoring tasks of low-altitude UAVs comprises the following steps: Quantitatively express the spatiotemporal dependency characteristics of the low-altitude UAV supervision task, and quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result; Perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks; According to the multiple fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established; Initial allocation of edge computing resources is performed based on priorities and constraints, and edge computing resources are dynamically allocated and adaptively optimized.

[0008] Optionally, the edge computing method for the real-time supervision task of low-altitude UAVs, wherein the temporal and spatial dependence characteristics of the low-altitude UAV supervision task are quantitatively expressed, and the low-altitude UAV supervision task is quantitatively evaluated according to the quantitative expression result to obtain the quantitative evaluation result, specifically includes: Comprehensively analyze the timestamps, spatial location information, and dependencies between tasks in real-time computing tasks, and build a spatiotemporal graph model that expresses the spatiotemporal dependencies of tasks. The spatiotemporal graph model is used to build task dependencies, quantify task execution constraints, and dynamically adjust task execution strategies. Based on the changing characteristics of real-time computing tasks over time and space, the time series prediction method is used to model the task change trend, and combined with historical data analysis, the dynamic evolution law of time and space dependency characteristics is depicted; Based on the quantitative expression of spatiotemporal dependency features, computable patterns of different tasks are obtained. According to the computable patterns, the low-altitude UAV supervision task is quantitatively evaluated under the condition of limited resources at the edge to obtain quantitative evaluation results.

[0009] Optionally, the edge computing method for the real-time supervision task of low-altitude UAVs, wherein the quantitative evaluation of the low-altitude UAV supervision task under the condition of limited resources at the edge end specifically includes: Construct an objective function that reflects the requirements of spatiotemporal flow computing tasks and is suitable for optimization, and integrate resource consumption indicators into a unified computing workload evaluation model. The resource consumption indicators include CPU usage, memory usage, network transmission overhead, and energy consumption budget. Define the computational evaluation model: ; in, represents the computational workload evaluation model, represents the task load function, Indicates the number of drones, Indicates the total number of turning points in the running trajectory, representing the complexity of the trajectory, Function representing the time complexity calculation of the drone supervision algorithm.

[0010] Optionally, the edge computing method for the real-time supervision task of low-altitude UAVs, wherein the quantitative evaluation results include functional requirements, input and output requirements, performance indicators and key performance indicators.

[0011] Optionally, the edge computing method for the real-time supervision task of low-altitude UAVs, wherein the fine-grained decomposition of the spatiotemporal dependent tasks at the edge is performed according to the quantitative evaluation results, and the low-altitude UAV supervision task is decomposed into multiple fine-grained subtasks, specifically including: Draw detailed spatiotemporal dependency graphs based on functional requirements, input and output requirements, performance indicators, and key performance indicators, including data flow, control flow, and resource dependencies, to visually display the relationships between the various parts of the task; Select modeling tools based on task characteristics, intuitively represent task structure and temporal and spatial characteristics, and build a comprehensive state space for the task, including all task states and transition rules between task states; According to the functional logic of the task, the large task is subdivided into multiple small-scale and logically independent fine-grained subtasks, clear input and output interfaces are defined, and the state transfer equation is constructed using the dynamic programming algorithm to find the optimal decomposition solution that meets the time and space dependency constraints.

[0012] Optionally, the edge computing method for the real-time supervision task of a low-altitude UAV, wherein, according to the multiple fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established, specifically including: For the fine-grained subtasks after fine-grained decomposition, the priorities are divided according to the time urgency, data importance, and computing amount of each fine-grained subtask, and the resource demand of each fine-grained subtask is evaluated. Combined with the real-time performance indicators of the edge device, a time window planning model is constructed. The execution order and start and end time of each fine-grained subtask are arranged through the time window planning model to ensure that all fine-grained subtasks are completed within the specified time window; According to the characteristics of different types of fine-grained subtasks, a process-thread multi-granularity hybrid fine-grained task parallel execution model is established, and hybrid-level parallel computing is implemented on multi-core heterogeneous edge devices through the fine-grained task parallel execution model.

[0013] Optionally, the edge computing method for the real-time supervision task of low-altitude drones, wherein the preliminary allocation of edge computing resources based on priorities and constraints, and the dynamic allocation and adaptive optimization of edge computing resources, specifically include: Real-time monitoring and quantitative analysis of limited computing resources are carried out. Combined with the characteristics of spatiotemporal data processing tasks and historical execution status, machine learning algorithms are used to predict resource demand in the future and estimate the relationship between resource supply and demand. According to the urgency, timeliness and importance of the tasks, the priority of each fine-grained subtask to be executed is optimized. According to the current total amount of resources, the resource demand and priority of each task, fair scheduling, preemptive scheduling and on-demand allocation strategies are used to allocate initial computing resources to each fine-grained subtask on demand. After the initial allocation, a real-time feedback mechanism for edge resource utilization is established. When resource shortage, load imbalance or new tasks are detected, the dynamic resource reallocation process is triggered immediately. The allocated resources are adjusted through the load balancing algorithm. The relevant constraint functions are designed to balance the computing resource distribution status and computing task completion status within an edge cluster. Under the premise of satisfying the spatiotemporal dependency of tasks, an elastic resource allocation system performance model is constructed to dynamically adjust the edge resource allocation. Based on the evaluation of allocation effects based on task completion time, resource utilization and response speed, the resource allocation strategy is continuously iterated and optimized. In combination with the dynamic change trend of spatiotemporal flow data characteristics and future fluctuations in task requirements, the changing pattern of resource demand is predicted, and the resource allocation strategy is automatically adjusted and optimized based on the evaluation feedback information, forming a closed loop from monitoring, allocation, execution to feedback optimization.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also provides an edge computing system for real-time supervision of low-altitude UAVs, wherein the edge computing system for real-time supervision of low-altitude UAVs includes: A task evaluation module is used to quantitatively express the spatiotemporal dependency characteristics of the low-altitude UAV supervision task, and to quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result; A task decomposition module, used to perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks; A task execution module is used to construct a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment based on multiple fine-grained subtasks, and establish a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy; The resource allocation module is used to perform preliminary allocation of edge computing resources based on priorities and constraints, and to dynamically allocate and adaptively optimize edge computing resources.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an edge computing program for low-altitude UAV real-time supervision tasks stored on the memory and run on the processor, and when the edge computing program for low-altitude UAV real-time supervision tasks is executed by the processor, the steps of the edge computing method for low-altitude UAV real-time supervision tasks as described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an edge computing program for the real-time supervision task of low-altitude unmanned aerial vehicles, and when the edge computing program for the real-time supervision task of low-altitude unmanned aerial vehicles is executed by the processor, the steps of the edge computing method for the real-time supervision task of low-altitude unmanned aerial vehicles as described above are implemented.

[0017] In the present invention, the spatiotemporal dependency characteristics of the low-altitude UAV supervision task are quantitatively expressed, and the low-altitude UAV supervision task is quantitatively evaluated according to the quantitative expression result to obtain a quantitative evaluation result; the edge-end spatiotemporal dependency task is fine-grained decomposed according to the quantitative evaluation result, and the low-altitude UAV supervision task is decomposed into multiple fine-grained subtasks; according to the multiple fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established; the edge-end computing resources are initially allocated based on priority and constraint conditions, and the edge-end computing resources are dynamically allocated and adaptively optimized. The present invention can significantly improve the edge computing real-time of large-scale low-altitude UAV supervision tasks and improve the utilization rate of edge computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a preferred embodiment of the edge computing method for real-time monitoring tasks of low-altitude UAVs of the present invention; Figure 2 It is a schematic diagram of the overall process in a preferred embodiment of the edge computing method for real-time monitoring tasks of low-altitude UAVs of the present invention; Figure 3 It is the overall technical roadmap of the preferred embodiment of the edge computing method for real-time monitoring tasks of low-altitude UAVs of the present invention; Figure 4 It is a schematic diagram of a spatiotemporal dependency graph model of a UAV computing task in a preferred embodiment of the edge computing method for real-time monitoring tasks of low-altitude UAVs of the present invention; Figure 5It is a schematic diagram of fine-grained decomposition of drone subtasks in a preferred embodiment of the edge computing method for real-time supervision of low-altitude drones of the present invention; Figure 6 It is a schematic diagram of a parallel execution strategy method for subtasks of edge-end drones in a preferred embodiment of the edge computing method for real-time supervision of low-altitude drones of the present invention; Figure 7 It is a flow chart of a dynamic allocation strategy of edge computing resources in a preferred embodiment of the edge computing method for real-time monitoring tasks of low-altitude UAVs of the present invention; Figure 8 It is a structural diagram of a preferred embodiment of the edge computing system for real-time monitoring tasks of low-altitude UAVs of the present invention; Fig. 9 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] The present invention is an edge computing method for real-time supervision tasks of low-altitude unmanned aerial vehicles. By quantitatively expressing the spatiotemporal dependence characteristics of supervision tasks such as unmanned aerial vehicle anomaly detection and conflict warning, the fine-grained decomposition of spatiotemporal dependence tasks on the edge is achieved, the complexity of task processing on the edge is reduced, and the balance and efficiency of task allocation are improved; a process-thread multi-granularity mixed edge fine-grained task parallel execution strategy is established, and an adaptive adjustment mechanism for edge granular task execution plans is established, which effectively improves the concurrent execution capability of the edge; according to the computing requirements and computing resource status of edge tasks, a dynamic allocation method of edge computing resources under limited resource conditions is studied, and an optimization mechanism for dynamic allocation of edge computing resources is established to ensure that computing tasks are reasonably and efficiently allocated at the edge.

[0021] The edge computing method for real-time monitoring of low-altitude drones described in the preferred embodiment of the present invention is as follows: Figure 1 , Figure 2 (Overall process diagram) and Figure 3 As shown in the (Technical route diagram), the edge computing method for the real-time supervision task of low-altitude drones includes the following steps: Step S10: quantitatively express the spatiotemporal dependence characteristics of the low-altitude UAV supervision task, and quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result.

[0022] Specifically, first, the timestamps, spatial location information, and dependencies between tasks in real-time computing tasks are comprehensively analyzed to construct a spatiotemporal graph model that expresses the spatiotemporal dependencies of tasks. The spatiotemporal graph model can express the spatiotemporal dependencies of tasks. The spatiotemporal graph model is used to construct task dependencies, quantify task execution constraints, and dynamically adjust task execution strategies. The weights of the edges in the spatiotemporal graph are defined and assigned based on factors such as the execution order of tasks, data dependencies (such as causality, synchronization, or asynchrony), and data transmission delays. These weight parameters can quantify key characteristics such as the strength of time constraints, data dependency, and delay tolerance between tasks, forming a multi-layer, multi-dimensional spatiotemporal dependency network.

[0023] Then, based on the changing characteristics of real-time computing tasks over time and space, we use time series prediction and other methods to model the changing trends of tasks, and combine historical data analysis to characterize the dynamic evolution of spatiotemporal dependency features. Spatiotemporal dependency features refer to feature dependencies in the time dimension, space dimension, and spatiotemporal interaction. Figure 4 The manifestation of spatiotemporal dependency features in computing tasks 1 and 2 in the task scheduling is also different. For example, the spatiotemporal dependency features in task scheduling are manifested in the time dimension as the execution order of tasks, and in the space dimension as the distribution of computing resources of tasks on different edge computing nodes. At the spatiotemporal interaction level, the execution efficiency of tasks depends not only on the current time state, but also on the spatial position of the drone. Finally, based on the feedback from actual operation, the edge weights are continuously adjusted and optimized to reflect changes in the system state. For example, the delay tolerance threshold is dynamically updated according to factors such as the current resource load status and changes in network conditions. The construction of the spatiotemporal graph model first establishes data dependencies according to the execution order, synchronization and asynchrony, transmission delay and other relationships between different computing tasks, and then converts these data dependencies into edge set relationships between different computing tasks according to the weights. The computing tasks are distributed to the graph network and connected with data dependencies to establish a spatiotemporal graph model (for example, including computing tasks 1-computing tasks 7). That is, the construction of the spatiotemporal graph model refers to Figure 4 .

[0024] Finally, based on the quantitative expression of spatiotemporal dependency features, we obtain computable modes for different tasks. Based on the computable modes, we conduct a quantitative evaluation of the low-altitude UAV supervision task under the condition of limited resources at the edge, and obtain a quantitative evaluation result. That is, by quantitatively expressing the spatiotemporal dependency features of different tasks, we transform the abstract tasks into specific computable data forms, and then use these computable quantitative expression results to conduct a quantitative evaluation of the task computation amount.

[0025] Since edge devices are usually limited by multiple factors such as computing power, storage capacity, and battery life, computational workload evaluation needs to consider factors such as CPU usage, memory usage, network transmission overhead, and energy budget. By constructing an objective function that can reflect the requirements of spatiotemporal flow computing tasks and is suitable for optimization, the above resource consumption indicators are integrated into a unified computational workload evaluation model (referring to the model used for quantitative evaluation of computing tasks at the edge segment). For large-scale UAV spatiotemporal flow computing tasks, computational workload estimation is also affected by factors such as the number of UAVs and the complexity of UAV operation. The number of UAVs directly affects the task parallelism, collaboration complexity, and overall resource requirements. The flight trajectory planning, data collection, and processing strategies of each UAV will affect the computational workload of the entire system. Therefore, the computational workload will be reflected as the time complexity of the spatial computing operator, which is determined by the specific process of the algorithm, the number of UAVs, and the complexity of UAV operation. The computational workload evaluation model is defined as: ; in, represents the computational workload evaluation model, represents the task load function, Indicates the number of drones, Indicates the total number of turning points in the running trajectory, representing the complexity of the trajectory, Function representing the time complexity calculation of the drone supervision algorithm.

[0026] The quantitative evaluation results include functional requirements, input and output requirements, performance indicators and key performance indicators.

[0027] Step S20: Perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks.

[0028] Specifically, we first conduct a comprehensive quantitative evaluation of the supervision task, clarify its functional requirements, input and output requirements, performance indicators (such as response time, throughput, accuracy, etc.), and determine the key performance indicators. The drone supervision task contains many types of computing tasks, and the spatiotemporal dependency characteristics of each task (such as Figure 5The spatiotemporal dependency feature 1, spatiotemporal dependency feature 2, and spatiotemporal dependency feature n) in the task are all different. It is necessary to quantify the spatiotemporal dependency features of these tasks before conducting a quantitative evaluation of the computational amount. A comprehensive quantitative evaluation refers to the evaluation of all different computing tasks. Draw a detailed spatiotemporal dependency graph, including data flow, control flow, and resource dependency, to visually display the relationships between the various parts of the task to ensure that these relationships are fully retained in the subsequent decomposition process. Select appropriate modeling tools based on the characteristics of the task in order to more intuitively represent the task structure and its spatiotemporal characteristics. Build a comprehensive state space for the task, including all task states and the conversion rules between them.

[0029] Then, the large task is subdivided into multiple small-scale and logically independent fine-grained subtasks according to the functional logic of the task (e.g. Figure 5 Task 1, Task 2, Task n), while defining clear input and output interfaces to ensure loose coupling between fine-grained subtasks; using dynamic programming algorithms to construct state transition equations, the state transition equations are used to describe how the execution state of fine-grained subtasks changes with time and resources, ensuring that the task decomposition scheme meets the time and space dependency constraints while optimizing the overall scheduling strategy, and finding the optimal decomposition scheme that meets the time and space dependency constraints. This process should consider the balance between time complexity and space complexity; setting specific time windows, resource constraints and other necessary conditions for each fine-grained subtask to guide the order and method of interaction between fine-grained subtasks, ensuring that the decomposed fine-grained subtasks can effectively collaborate under the specified conditions, and the fine-grained decomposition of drone subtasks is referenced. Figure 5 .

[0030] Step S30: Based on the multiple fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established.

[0031] Specifically, first, for the fine-grained subtasks after fine-grained decomposition, we first prioritize them according to factors such as the time urgency, data importance, and computational size of each fine-grained subtask, and then evaluate the resource requirements of each fine-grained subtask. Then, combined with the real-time performance indicators of the edge device, we build a time window planning model. Through the time window planning model, we reasonably arrange the execution order and start and end time of each fine-grained subtask to ensure that all fine-grained subtasks are completed within the specified time window, while avoiding resource contention and conflict. Based on the above steps, we build a fine-grained task execution plan that dynamically adapts to the resource constraints of the edge environment and the real-time requirements of computing. The execution plan needs to ensure the maximization of resource utilization efficiency and the efficient completion of tasks according to the preset time window.

[0032] Then, according to the characteristics of different types of fine-grained subtasks, a fine-grained task parallel execution model with multi-granularity hybrid of process and thread is established. Through the fine-grained task parallel execution model, hybrid-level parallel computing is implemented on multi-core heterogeneous edge devices to make full use of concurrency mechanisms at different granularity levels. For fine-grained subtasks with high computing intensity or large memory consumption, independent processes are created to avoid global resource competition. At the same time, synchronization mechanisms such as mutexes and semaphores between processes are used to ensure the correct and orderly execution of related operations. For lightweight fine-grained subtasks with high response speed requirements, multiple threads are used in a single process to quickly switch execution, reduce context switching overhead, and improve the real-time response capability of the system. Between the processes of each step, a directed acyclic graph is constructed using memory iteration. This method can avoid huge hard disk I / O overhead and additional calculations. In thread-level parallel management, the amount of tasks to be processed by the process to which the thread belongs is determined. If the amount of tasks built is greater than the set threshold, the drone anomaly detection and conflict warning are divided into multiple fine-grained subtasks. The calculation chain sequence is further segmented according to the complexity of the geometric object and entrusted to the sub-threads for processing, so as to make full use of idle computing resources with finer parallel granularity. The multi-granularity hybrid parallel execution strategy formed in this way can flexibly respond to various types of task requirements and can be dynamically adjusted according to the real-time environment. For example, when the edge device resources are sufficient, the number of processes is increased, and when resources are tight, more threads are used, thereby improving the overall computing performance. For example, Figure 6 As shown, in each computing node (such as node 0, node 1, node 2), the spatiotemporal stream data is first processed using the stream data processing process. After basic cleaning, addition, deletion, and screening of the data (data preprocessing), large batches of data are divided into different threads (such as thread 0-thread i) for synchronous processing. After the data partitioning algorithm is processed, different computing tasks are then assigned to different threads (such as thread 0-thread i) in the real-time monitoring process to obtain the required computing data, realize the synchronous execution of different algorithms, and finally feed back to the drone. Reference for the process-thread multi-granularity hybrid drone task parallel execution strategy Figure 6 .

[0033] Step S40: Perform preliminary allocation of edge computing resources based on priorities and constraints, and dynamically allocate and adaptively optimize edge computing resources.

[0034] Specifically, first, through real-time monitoring and quantitative analysis of limited computing resources, combined with the characteristics of spatiotemporal stream data processing tasks and historical execution status, machine learning algorithms are used to predict resource requirements in the future to accurately estimate the relationship between resource supply and demand. According to factors such as the urgency, timeliness, and importance of the task, the priority of each fine-grained subtask to be executed is optimized. According to the current total amount of resources, the resource requirements and priority of each task, fair scheduling, preemptive scheduling, and on-demand allocation strategies are used to perform initial computing resource allocation on demand for each fine-grained subtask to ensure that critical tasks can obtain necessary resources in a timely manner. For the edge computing resource allocation strategy flow chart, please refer to Figure 7 , which is divided into computing task (such as computing task A, computing task B, computing task C) resource quantification, resource demand prediction and dynamic allocation optimization of edge computing resources.

[0035] Then, after the initial allocation, a real-time feedback mechanism for edge resource utilization is established. Once resource shortage, load imbalance or new tasks are detected, the dynamic resource reallocation process is triggered immediately. The allocated resources are adjusted through load balancing algorithms (such as round-robin scheduling algorithm, minimum connection scheduling algorithm, etc.), such as releasing resources occupied by completed tasks and reallocating idle resources. According to the key indicators such as collected processing delay, resource utilization, data processing speed, and relevant constraint functions (such as tilt willingness function, rate willingness function, etc.) are designed to balance the distribution state of computing resources and the completion state of computing tasks in an edge cluster. Under the premise of meeting the spatiotemporal dependency of tasks, an elastic resource allocation system performance model is constructed to dynamically adjust the edge resource allocation, minimize the degree of resource fragmentation, and improve resource utilization. When the resources of a single edge device are not enough to meet all task requirements, cloud-edge coordination can be used to expand to multi-edge collaboration, build a distributed resource pool, and further optimize the resource allocation effect through cross-device task migration and resource sharing.

[0036] Finally, based on the evaluation of allocation effects such as task completion time, resource utilization, and response speed, the resource allocation strategy is continuously iterated and optimized. Combined with the dynamic change trend of spatiotemporal flow data characteristics and future fluctuations in task demand, the changing pattern of resource demand is predicted, and the resource allocation strategy is automatically adjusted and optimized based on the evaluation feedback information, forming a closed loop from monitoring, allocation, execution to feedback optimization, so that edge computing resources can always be dynamically and efficiently managed and allocated under conditions of limited resources.

[0037] The fine-grained task decomposition method based on quantitative modeling of UAV supervision tasks proposed in the present invention quantitatively expresses the spatiotemporal dependency characteristics of tasks according to the task execution order and the dependency relationship between data, and establishes a spatiotemporal graph data model, thereby providing model quantification support for the subsequent fine-grained decomposition of spatiotemporal tasks, achieving a reasonable balance between the execution time and space requirements of subtasks, and improving the efficiency and timeliness of the calculation of large-scale UAV supervision tasks.

[0038] The present invention proposes a method for predicting and allocating parallel computing resources for edge tasks. Based on the parallel execution of the fine-grained subtasks of the above-mentioned drone, this method uses a deep learning method to achieve pre-predictive allocation of computing resources, ensure the execution priority of key subtasks, and combine it with a dynamic allocation optimization strategy of computing resources to further optimize the resource allocation effect and achieve the computing efficiency of drone supervision tasks under limited computing resources.

[0039] Furthermore, if Figure 8 As shown, based on the above-mentioned edge computing method for real-time supervision of low-altitude UAVs, the present invention also provides an edge computing system for real-time supervision of low-altitude UAVs, wherein the edge computing system for real-time supervision of low-altitude UAVs includes: The task evaluation module 51 is used to quantitatively express the spatiotemporal dependency characteristics of the low-altitude UAV supervision task, and quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result; A task decomposition module 52 is used to perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks; The task execution module 53 is used to construct a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment according to the multiple fine-grained subtasks, and establish a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy; The resource allocation module 54 is used to perform preliminary allocation of edge computing resources based on priorities and constraints, and to dynamically allocate and adaptively optimize edge computing resources.

[0040] Furthermore, if Fig. 9 As shown, based on the above-mentioned edge computing method and system for real-time supervision tasks of low-altitude UAVs, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Fig. 9 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0041] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Furthermore, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an edge computing program 40 for the real-time supervision task of a low-altitude drone, and the edge computing program 40 for the real-time supervision task of a low-altitude drone can be executed by the processor 10, thereby realizing the edge computing method for the real-time supervision task of a low-altitude drone in the present application.

[0042] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as an edge computing method for executing the real-time supervision task of the low-altitude drone.

[0043] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The processor 10, the memory 20, and the display 30 of the terminal communicate with each other via a system bus.

[0044] In one embodiment, when the processor 10 executes the edge computing program 40 for the low-altitude UAV real-time supervision task in the memory 20, the steps of the edge computing method for the low-altitude UAV real-time supervision task as described above are implemented.

[0045] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an edge computing program for low-altitude UAV real-time supervision tasks, and when the edge computing program for low-altitude UAV real-time supervision tasks is executed by a processor, the steps of the edge computing method for low-altitude UAV real-time supervision tasks as described above are implemented.

[0046] In summary, the present invention provides an edge computing method, system, terminal and computer-readable storage medium for real-time supervision tasks of low-altitude UAVs, the method comprising: quantitatively expressing the spatiotemporal dependency characteristics of the low-altitude UAV supervision tasks, and quantitatively evaluating the low-altitude UAV supervision tasks according to the quantitative expression results to obtain quantitative evaluation results; fine-grained decomposition of the spatiotemporal dependency tasks at the edge end according to the quantitative evaluation results, decomposing the low-altitude UAV supervision tasks into multiple fine-grained subtasks; based on the multiple fine-grained subtasks, constructing a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment, and establishing a process-thread multi-granularity mixed edge end fine-grained task parallel execution strategy; performing preliminary allocation of edge end computing resources based on priority and constraint conditions, and dynamically allocating and adaptively optimizing edge end computing resources. The present invention can significantly improve the edge computing real-time performance of large-scale low-altitude UAV supervision tasks and improve edge computing resource utilization.

[0047] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or terminal including the element.

[0048] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0049] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. An edge computing method for real-time monitoring of low-altitude UAVs, characterized in that: The edge computing method for the real-time supervision task of low-altitude drones includes: Quantitatively express the spatiotemporal dependency characteristics of the low-altitude UAV supervision task, and quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result; Perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks; According to the multiple fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established; Initial allocation of edge computing resources is performed based on priorities and constraints, and edge computing resources are dynamically allocated and adaptively optimized.

2. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 1 is characterized in that: The temporal and spatial dependence characteristics of the low-altitude UAV supervision task are quantitatively expressed, and the low-altitude UAV supervision task is quantitatively evaluated according to the quantitative expression result to obtain a quantitative evaluation result, which specifically includes: Comprehensively analyze the timestamps, spatial location information, and dependencies between tasks in real-time computing tasks, and build a spatiotemporal graph model that expresses the spatiotemporal dependencies of tasks. The spatiotemporal graph model is used to build task dependencies, quantify task execution constraints, and dynamically adjust task execution strategies. Based on the changing characteristics of real-time computing tasks over time and space, the time series prediction method is used to model the task change trend, and combined with historical data analysis, the dynamic evolution law of time and space dependency characteristics is depicted; Based on the quantitative expression of spatiotemporal dependency features, computable patterns of different tasks are obtained. According to the computable patterns, the low-altitude UAV supervision task is quantitatively evaluated under the condition of limited resources at the edge to obtain quantitative evaluation results.

3. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 2 is characterized in that: The quantitative evaluation of the low-altitude UAV supervision task under the limited resource conditions at the edge includes: Construct an objective function that reflects the requirements of spatiotemporal flow computing tasks and is suitable for optimization, and integrate resource consumption indicators into a unified computing workload evaluation model. The resource consumption indicators include CPU usage, memory usage, network transmission overhead, and energy consumption budget. Define the computational evaluation model: ； in, represents the computational workload evaluation model, represents the task load function, Indicates the number of drones, Indicates the total number of turning points in the running trajectory, representing the complexity of the trajectory, Function representing the time complexity calculation of the drone supervision algorithm.

4. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 1 is characterized in that: The quantitative evaluation results include functional requirements, input and output requirements, performance indicators and key performance indicators.

5. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 4 is characterized in that: The fine-grained decomposition of the edge-end spatiotemporal dependent tasks is performed according to the quantitative evaluation results, and the low-altitude UAV supervision task is decomposed into multiple fine-grained subtasks, specifically including: Draw detailed spatiotemporal dependency graphs based on functional requirements, input and output requirements, performance indicators, and key performance indicators, including data flow, control flow, and resource dependencies, to visually display the relationships between the various parts of the task; Select modeling tools based on task characteristics, intuitively represent task structure and temporal and spatial characteristics, and build a comprehensive state space for the task, including all task states and transition rules between task states; According to the functional logic of the task, the large task is subdivided into multiple small-scale and logically independent fine-grained subtasks, clear input and output interfaces are defined, and the state transfer equation is constructed using the dynamic programming algorithm to find the optimal decomposition solution that meets the time and space dependency constraints.

6. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 1 is characterized in that: According to the plurality of the fine-grained subtasks, a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment is constructed, and a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy is established, which specifically includes: For the fine-grained subtasks after fine-grained decomposition, the priorities are divided according to the time urgency, data importance, and computing amount of each fine-grained subtask, and the resource demand of each fine-grained subtask is evaluated. Combined with the real-time performance indicators of the edge device, a time window planning model is constructed. The execution order and start and end time of each fine-grained subtask are arranged through the time window planning model to ensure that all fine-grained subtasks are completed within the specified time window; According to the characteristics of different types of fine-grained subtasks, a process-thread multi-granularity hybrid fine-grained task parallel execution model is established, and hybrid-level parallel computing is implemented on multi-core heterogeneous edge devices through the fine-grained task parallel execution model.

7. The edge computing method for real-time monitoring of low-altitude UAVs according to claim 1 is characterized in that: The preliminary allocation of edge computing resources based on priorities and constraints, and dynamic allocation and adaptive optimization of edge computing resources, specifically include: Real-time monitoring and quantitative analysis of limited computing resources are carried out. Combined with the characteristics of spatiotemporal data processing tasks and historical execution status, machine learning algorithms are used to predict resource demand in the future and estimate the relationship between resource supply and demand. According to the urgency, timeliness and importance of the tasks, the priority of each fine-grained subtask to be executed is optimized. According to the current total amount of resources, the resource demand and priority of each task, fair scheduling, preemptive scheduling and on-demand allocation strategies are used to allocate initial computing resources to each fine-grained subtask on demand. After the initial allocation, a real-time feedback mechanism for edge resource utilization is established. When resource shortage, load imbalance or new tasks are detected, the dynamic resource reallocation process is triggered immediately. The allocated resources are adjusted through the load balancing algorithm. The relevant constraint functions are designed to balance the computing resource distribution status and computing task completion status within an edge cluster. Under the premise of satisfying the spatiotemporal dependency of tasks, an elastic resource allocation system performance model is constructed to dynamically adjust the edge resource allocation. Based on the evaluation of allocation effects based on task completion time, resource utilization and response speed, the resource allocation strategy is continuously iterated and optimized. In combination with the dynamic change trend of spatiotemporal flow data characteristics and future fluctuations in task requirements, the changing pattern of resource demand is predicted, and the resource allocation strategy is automatically adjusted and optimized based on the evaluation feedback information, forming a closed loop from monitoring, allocation, execution to feedback optimization.

8. An edge computing system for real-time monitoring of low-altitude drones, characterized in that: The edge computing system for the real-time supervision task of low-altitude drones includes: A task evaluation module is used to quantitatively express the spatiotemporal dependency characteristics of the low-altitude UAV supervision task, and to quantitatively evaluate the low-altitude UAV supervision task according to the quantitative expression result to obtain a quantitative evaluation result; A task decomposition module, used to perform fine-grained decomposition of the edge-end spatiotemporal dependent tasks according to the quantitative evaluation results, and decompose the low-altitude UAV supervision task into multiple fine-grained subtasks; A task execution module is used to construct a fine-grained task execution plan that dynamically adapts to resource constraints and computing real-time in the edge environment based on multiple fine-grained subtasks, and establish a process-thread multi-granularity mixed edge-end fine-grained task parallel execution strategy; The resource allocation module is used to perform preliminary allocation of edge computing resources based on priorities and constraints, and to dynamically allocate and adaptively optimize edge computing resources.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and an edge computing program for low-altitude UAV real-time supervision tasks stored in the memory and run on the processor. When the edge computing program for low-altitude UAV real-time supervision tasks is executed by the processor, the steps of the edge computing method for low-altitude UAV real-time supervision tasks as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an edge computing program for the real-time supervision task of low-altitude drones. When the edge computing program for the real-time supervision task of low-altitude drones is executed by the processor, the steps of the edge computing method for the real-time supervision task of low-altitude drones as described in any one of claims 1-7 are implemented.

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