A Cloud-Edge Collaborative Computing Method, System, Terminal and Storage Medium for Supervising Spatiotemporal Flow Data of Low-Altitude UAVs

Abstract

The present invention discloses a cloud-edge collaborative computing method, system, terminal and storage medium for low-altitude UAV supervision spatio-temporal flow data. The method includes: establishing a cloud-edge integrated collaborative mode for UAV supervision tasks based on a cloud-edge network structure, and converting the topological relationship of cloud-edge computing resources into a graph network structure; constructing a cloud-edge collaborative allocation strategy for cross-edge node global tasks adapted to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaborative mode, so as to realize the cloud-edge collaborative allocation of cross-edge node global tasks; establishing a cloud-edge resource scheduling adaptive optimization mechanism considering the low-latency cost of global memory, computing and transmission, and realizing the adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level. The present invention establishes a cloud-edge collaborative optimization scheduling mechanism for the calculation of large-scale UAV supervision spatio-temporal flow data in three-dimensional space to support the efficient scheduling requirements for real-time calculation of UAV supervision tasks in the cloud-edge collaborative mode.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) data processing, and particularly to a cloud-edge collaborative computing method, system, terminal and computer-readable storage medium for spatio-temporal flow data of low-altitude UAV supervision. Background Art

[0002] Low-altitude UAV supervision requires real-time acquisition of flight information such as the position, attitude, speed and heading of UAVs. This information constitutes spatio-temporal flow data in three-dimensional space with characteristics such as continuous arrival and infinite growth. How to perform real-time computational analysis on the spatio-temporal flow data generated during the flight of large-scale low-altitude UAVs to support real-time responses to tasks such as UAV anomaly detection and conflict warning has become a major requirement for ensuring the safe operation of large-scale UAVs.

[0003] Adopting a cloud-edge collaborative computing architecture for efficient computation of UAV supervision tasks is an important guarantee for real-time computation of various supervision tasks during the flight of large-scale UAVs. The cloud-edge collaborative computing architecture realizes the complementary advantages between cloud computing and edge computing. The cloud provides massive computing and storage resources, while the edge provides real-time task processing through widely distributed nodes. Under the cloud-edge collaborative computing architecture, reasonable task scheduling is carried out to coordinate available resources such as computing, storage, network and virtualization resources to achieve the best scheduling strategy, so as to efficiently complete computational tasks, which is the key to unleashing the performance of cloud-edge collaboration. For example, methods such as mathematical programming algorithms, heuristic algorithms, evolutionary algorithms, game algorithms, reinforcement learning algorithms, and machine learning algorithms are used to achieve optimal task scheduling.

[0004] In recent years, there have also been explorations of lighter scheduling algorithms and collaborative frameworks, such as a cloud-edge collaborative computing framework based on lightweight genetic algorithms and air-ground integration. Generally speaking, the existing research on task scheduling algorithms under cloud-edge collaboration provides theoretical support and practical support for giving full play to the performance of cloud-edge collaborative computing. However, most of the research does not consider problems such as the difficulty in dividing cloud-edge tasks for spatio-temporal flow data computation and the difficulty in collaborative resource scheduling caused by high spatio-temporal dependence of tasks.

[0005] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0006] The main purpose of the present invention is to provide a cloud-edge collaborative computing method, system, terminal and computer-readable storage medium for spatio-temporal flow data of low-altitude UAV supervision, aiming to solve the problems of difficult division of cloud-edge tasks for spatio-temporal flow data computation and difficult collaborative resource scheduling caused by high spatio-temporal dependence of tasks when using a cloud-edge collaborative computing architecture to compute UAV supervision tasks in the existing technology.

[0007] To achieve the above object, the present invention provides a cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data. The cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data includes the following steps:

[0008] Establish a cloud-edge integrated collaborative mode for UAV supervision tasks based on the cloud-edge network structure, and convert the cloud-edge computing resource topological relationship into a graph network structure;

[0009] Based on the node sequence characteristics of the cloud-edge integrated collaborative mode, construct a cloud-edge collaborative allocation strategy for cross-edge node global tasks adapted to the graph network structure to achieve cloud-edge collaborative allocation of cross-edge node global tasks;

[0010] Based on the cloud-edge collaborative allocation strategy for cross-edge node global tasks, establish a cloud-edge resource scheduling adaptive optimization mechanism considering the low-latency cost of global memory computing and transmission, and achieve adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level.

[0011] Optionally, in the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data, the establishment of a cloud-edge integrated collaborative mode for UAV supervision tasks based on the cloud-edge network structure and the conversion of the cloud-edge computing resource topological relationship into a graph network structure specifically includes:

[0012] Perform network mapping on the cloud-edge resource topological relationship of large-scale UAV supervision to construct a cloud-edge integrated network model. The cloud-edge integrated network model =(Uc, Ue, V), where Uc represents the cloud-edge structure central node, Ue represents the cloud-edge structure edge node, and V represents the homogeneous attribute edge set in the cloud-edge integrated network model;

[0013] Define the heterogeneous type point set (Uc, Ue), Uc={nc1, nc2,..., nc n}, representing the cloud center node and related attribute mapping in the cloud-edge structure. Among them, nc1 to nc n represent multiple attribute mappings in the central node, Ue={ne1, ne2,..., ne n}, representing the edge node and related attribute mapping. Among them, ne1 to ne n represent multiple attribute mappings in the edge node; The node attributes are expressed as:

[0014] ;

[0015] Among them, represents the node attributes, including the central node and the edge node, represents considering the influencing factors comprehensively, represents the node computing resource, represents the node storage resource, Indicates the node function type, Indicates the current load status or task deployment of the node;

[0016] Define the homogeneous attribute edge set V:

[0017] ;

[0018] Among them, Indicates the homogeneous attribute of the edge set between edge nodes, Indicates the transmission cost generated by node interaction, including the delay generated during communication or information transmission, Indicates the amount of computing tasks migrated during the node task scheduling process, Indicates the information transmission efficiency between nodes, Indicates the type of communication between nodes or the connectivity of the connected edges;

[0019] Cloud-edge integrated network model The expression is:

[0020] ;

[0021] Among them, Indicates the number of cloud nodes, Indicates the number of edge nodes, Indicates the cloud-cloud subnetwork, which is used to characterize the communication status between cloud nodes, Indicates the edge-edge subnetwork, which is used to characterize the cross-edge task calculation process, and Indicates the cloud-edge heterogeneous bipartite subnetwork with different numbers of nodes, which is used to characterize the communication process between the cloud and the edge;

[0022] Based on the cloud-edge integrated network model, establish a cloud-edge integrated collaboration mode for UAV supervision tasks. Using the structure walk and feature walk analysis methods of heterogeneous node networks, integrate the semantic associations of different node types into the network structure for network representation learning, and capture the structural similarity and attribute characteristics of the network.

[0023] Optionally, in the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data, the process of the cloud-edge collaborative allocation strategy includes: allocating local real-time computing tasks to the edge cluster and controlling global tasks through the cloud to achieve cloud-edge collaboration;

[0024] Construct a cloud-edge collaborative allocation strategy for cross-edge node global tasks that adapts to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaboration mode, so as to achieve cloud-edge collaborative allocation of cross-edge node global tasks, specifically including:

[0025] Allocate local real-time computing tasks to the nearby edge nodes for computing. If the computing resources of the nearby edge nodes cannot meet the requirements of low-latency processing, task coordination is performed through the cloud, and the local real-time computing tasks are offloaded to another nearby edge node or the cloud for takeover and processing, so as to achieve cross-edge communication and global collaborative optimization;

[0026] After the local real-time computing tasks transmitted to the edge nodes are processed, the edge nodes transmit real-time instructions and data to the terminals, and at the same time transmit spatio-temporal data to the cloud, and perform state perception and classification processing on the spatio-temporal data using the situation awareness model;

[0027] The cloud is used to store big data and train intelligent supervision models for processing various supervision tasks, and regularly distribute the trained intelligent supervision models to each edge node;

[0028] Dynamically estimate the cost consumption and efficiency formed in the cross-edge communication and computing process and transmit them to the cloud-edge integrated network, construct multiple independent real-time computing units with different granularities, and dynamically combine the multiple independent real-time computing units in the cloud-edge collaborative scheduling service chain to construct a scheduling service chain for cloud-edge scheduling;

[0029] Automatically execute the scheduling service chain within a preset time window based on the workflow, and intelligently allocate and orchestrate computing task units according to the resource status, network conditions, and task priorities of each node within each time window to ensure that the nodes execute efficiently and in parallel within each time window.

[0030] Optionally, in the cloud-edge collaborative computing method for spatio-temporal flow data of low-altitude UAV supervision, the establishment of a cloud-edge resource scheduling adaptive optimization mechanism considering the global memory-computation-transmission low-latency cost specifically includes:

[0031] Establish a cloud-edge resource balanced allocation strategy considering the global memory-computation-transmission low-latency cost;

[0032] Establish a global task cloud-edge resource dynamic allocation mechanism based on the network structure;

[0033] Establish a dynamically iterative cloud-edge resource scheduling adaptive optimization mechanism.

[0034] Optionally, in the cloud-edge collaborative computing method for spatio-temporal flow data of low-altitude UAV supervision, the establishment of a cloud-edge resource balanced allocation strategy considering the global memory-computation-transmission low-latency cost specifically includes:

[0035] Formalize the relevance of spatio-temporal stream real-time computing tasks based on computing task units formed by information at different time points and different interaction modes. Map the dependencies of computing tasks in different time windows on cloud-edge computing resources and the latency costs of storage, computing, and transmission generated under the current task to nodes and edges in the network, and perform dynamic calculations of element attributes.

[0036] Construct a load balancing allocation algorithm based on network structure driven by latency cost. Measure the resource allocation and system load status of the entire network through balance evaluation indicators. Simulate cloud-edge computing task allocation and execution through dynamic configuration of node attributes and edge attributes, and converge the task allocation process to the optimal state with the minimum latency cost of a single task and maximized resource utilization as the objective function.

[0037] Among them, the processing process of the load balancing allocation algorithm based on network structure includes:

[0038] Obtain the computing resources of edge nodes and the required amount of computing tasks they carry. Coordinate and define the current load status of nodes through the difference in the required amount of computing tasks. The load status includes high load, low load, and balanced state.

[0039] Evaluate the cost of communication and information transmission between nodes, obtain the edge weights used to assist algorithm decision-making in the network, and define the state of edges.

[0040] Based on node attributes and edge attributes, use the maximum weight and matching to perform optimal matching of cloud-edge resources and latency costs in the network, and obtain an equilibrium allocation scheme that satisfies minimum latency and maximized resource utilization.

[0041] Optionally, in the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal stream data, the establishment of a global task cloud-edge resource dynamic allocation mechanism based on network structure specifically includes:

[0042] From the global perspective of the cloud-edge integrated network, the cloud center systematically collects and organizes the target key information of all global tasks to be executed. The target key information includes the computing intensity, data volume, latency cost, and dependencies between tasks of the tasks.

[0043] Use the mapping relationship and node attributes to obtain all edge nodes with available resources. Comprehensively consider the hardware configuration, network conditions, and service capabilities of each edge node, perform real-time evaluation of the resource volume and computing efficiency, and save the global tasks in the corresponding edge clusters, and locate the most suitable edge node for each global task to carry the global task.

[0044] According to the optimal scheduling strategy, global tasks are allocated to the cloud center and related edge nodes one by one for processing, so as to complete the full-link management from global task feature analysis, edge node adaptation, edge node screening to specific task allocation.

[0045] Optionally, in the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data, the establishment of a dynamic iterative cloud-edge resource scheduling adaptive optimization mechanism specifically includes:

[0046] Capture and update the target core information within the global scope of the cloud-edge integrated network in real time, and track and record the status of all global computing tasks in real time, including the progress of task startup, execution, and completion, as well as the interdependence between tasks;

[0047] Based on the cloud-edge network topology and task characteristics, establish a delay cost prediction model, calculate the execution cost of tasks on different edge nodes, and minimize the global delay cost as the main optimization goal;

[0048] Based on a process-driven dynamic iterative scheduling algorithm, perform adaptive adjustment and optimization according to the real-time changing task requirements and system resource status, and adaptively adjust the task scheduling plan according to the real-time requirements of different tasks, network transmission delays, and computational resource tensions.

[0049] In addition, to achieve the above object, the present invention also provides a cloud-edge collaborative computing system for low-altitude UAV supervision spatio-temporal flow data, wherein the cloud-edge collaborative computing system for low-altitude UAV supervision spatio-temporal flow data includes:

[0050] A cloud-edge integrated collaborative mode establishment module for establishing a cloud-edge integrated collaborative mode for UAV supervision tasks based on the cloud-edge network structure, and converting the cloud-edge computing resource topological relationship into a graph network structure;

[0051] A cloud-edge collaborative allocation strategy construction module for constructing a cloud-edge collaborative allocation strategy for global tasks across edge nodes adapted to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaborative mode, so as to achieve cloud-edge collaborative allocation of global tasks across edge nodes;

[0052] A cloud-edge resource scheduling optimization module for establishing a cloud-edge resource scheduling adaptive optimization mechanism considering low delay costs of global memory, computing, and transmission based on the cloud-edge collaborative allocation strategy for global tasks across edge nodes, and realizing adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level.

[0053] In addition, to achieve the above object, the present invention further provides a terminal, wherein the terminal includes: a memory, a processor, and a cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data stored on the memory and operable on the processor. When the cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data is executed by the processor, the steps of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data as described above are implemented.

[0054] In addition, to achieve the above object, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data. When the cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data is executed by a processor, the steps of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data as described above are implemented.

[0055] In the present invention, a cloud-edge integrated collaborative mode for UAV supervision tasks based on a cloud-edge network structure is established, and the topological relationship of cloud-edge computing resources is converted into a graph network structure; based on the node sequence characteristics of the cloud-edge integrated collaborative mode, a cloud-edge collaborative allocation strategy for cross-edge node global tasks adapted to the graph network structure is constructed to achieve cloud-edge collaborative allocation of cross-edge node global tasks; based on the cloud-edge collaborative allocation strategy for cross-edge node global tasks, a cloud-edge resource scheduling adaptive optimization mechanism considering global memory-computation-data transfer low-latency costs is established to achieve adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level. The present invention establishes a cloud-edge collaborative optimization scheduling mechanism for large-scale UAV supervision spatio-temporal flow data calculation in three-dimensional space to support the efficient scheduling requirements for real-time calculation of UAV supervision tasks in the cloud-edge collaborative mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flowchart of a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0057] Figure 2 is a schematic diagram of the overall process in a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0058] Figure 3 is a schematic diagram of the overall technical route in a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0059] Figure 4 is a schematic diagram of the cloud-edge integrated network model structure in a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0060] Figure 5It is a schematic diagram of the collaborative scheduling of computing tasks across edge segments in a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0061] Figure 6 It is a schematic diagram of the self-adaptive optimization mechanism for global cloud-edge resource scheduling in a preferred embodiment of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0062] Figure 7 It is a structural diagram of a preferred embodiment of the cloud-edge collaborative computing system for low-altitude UAV supervision spatio-temporal flow data of the present invention;

[0063] Figure 8 It is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed implementation manners

[0064] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] The present invention is a cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data, which performs real-time and efficient calculations on UAV real-time tasks and reasonably allocates computing resources based on cloud-edge collaborative technology. The cloud-edge-end computing resources are abstracted into a graph network relationship according to topological relationships and computing capabilities, etc., a graph network model is established, and the computing resources are quantified to provide support for subsequent task allocation; subsequently, a cross-edge-node global task cloud-edge collaborative allocation strategy adapted to the graph network structure is constructed to realize the inclination of UAV computing tasks at high-load edge ends to computing tasks at low-load edge ends, and ensure the load balance of each edge end; finally, a self-adaptive optimization mechanism for cloud-edge resource scheduling that comprehensively considers storage, computing, and low-latency costs of transmission is established, and a network load balancing algorithm for global storage, computing, and transmission is realized from the UAV task queue, data stream transmission to computing resource allocation, and the load balancing algorithm is dynamically optimized through UAV feedback.

[0066] The cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data described in the preferred embodiment of the present invention, as Figure 1 、 Figure 2 (overall process schematic diagram) and Figure 3 (technical route schematic diagram) shows, the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data includes the following steps:

[0067] Step S10, establish a cloud-edge integrated collaborative mode for UAV supervision tasks based on the cloud-edge network structure, and convert the topological relationship of cloud-edge computing resources into a graph network structure.

[0068] Specifically, map the cloud-edge resource topology relationship of large-scale UAV supervision to the network, construct a cloud-edge integrated network model, conduct resource exploration and performance evaluation on the cloud and edge sides of large-scale UAV supervision, including key parameters such as computing power, storage capacity, communication bandwidth, and response latency, and use relevant theories such as graph theory to construct a cloud-edge integrated network model. In the cloud-edge integrated network model, the cloud is the core node with powerful processing capabilities and massive storage space, while the edge sides distributed in different geographical locations and close to the UAV flight area serve as the intermediate layer connecting the cloud and terminal devices (UAVs). Each edge side is reflected in the network model in the form of a node according to its physical location, service capabilities, and the characteristics of the communication links with the cloud and UAVs.

[0069] Cloud-edge integrated network model =(Uc, Ue, V) is mainly formed through two key definitions, mainly including the point set definition and the edge set definition. Among them, Uc represents the central node of the cloud-edge structure, Ue represents the edge node of the cloud-edge structure, and V represents the homogeneous attribute edge set in the cloud-edge integrated network model; First, define the heterogeneous type point set (Uc, Ue), Uc = {nc1, nc2,..., nc n}, which represents the mapping of the cloud center node and related attributes in the cloud-edge structure. Among them, nc1 to nc n represent multiple attribute mappings in the central node, Ue = {ne1, ne2,..., ne n}, which represents the mapping of the edge node and related attributes. Among them, ne1 to ne n represent multiple attribute mappings in the edge node; The node attributes are expressed as:

[0070] ;

[0071] Among them, represents the node attributes, including the central node and the edge node, represents considering the influencing factors comprehensively, represents the node computing resources, represents the node storage resources, represents the node function type, represents the current load status or task deployment of the node.

[0072] At the same time, define the homogeneous attribute edge set V, whose weight or attribute represents the connection status and information transmission process between the cloud and the edge, and between the edges. Its specific definition is expressed as:

[0073] ;

[0074] Among them, represents the homogeneous attribute of the edge set between the edge nodes, Represents the transmission cost generated by the interaction of nodes, including the time delay generated during communication or information transmission. Represents the amount of computational tasks migrated during the task scheduling process of nodes. Represents the information transmission efficiency between nodes. Represents the type of communication or the connectivity of the edges between nodes, which can be subdivided into connectionless, cloud-edge connection, and edge-edge connection.

[0075] Cloud-edge integrated network model The structure is expressed through an adjacency matrix, including the "cloud-cloud" sub-network composed of the cloud node set Uc and its corresponding edge set , which is used to characterize the communication status between cloud nodes; the "edge-edge" sub-network composed of the edge node set Ue and its corresponding edge set , which is used to characterize the cross-edge task calculation process; the "cloud-edge" heterogeneous bipartite sub-network composed of the cloud node set Uc and the edge node set Ue Or , which is used to characterize the communication process between the cloud and the edge. Therefore, the cloud-edge integrated network model The expression can be:

[0076] ;

[0077] Among them, Represents the number of cloud nodes, Represents the number of edge nodes, Represents the cloud-cloud sub-network, which is used to characterize the communication status between cloud nodes, Represents the edge-edge sub-network, which is used to characterize the cross-edge task calculation process, And Represents the cloud-edge heterogeneous bipartite sub-network with different numbers of nodes, which is used to characterize the communication process between the cloud and the edge. The schematic diagram of the cloud-edge integrated network model structure refers to Figure 4 , Figure 4 In which, the cloud Uc respectively interacts with the edge nodes Ue1, edge node Ue2, edge node Ue3, edge node Ue4, and edge node Uen for information.

[0078] Then, based on the cloud-edge integrated network model, a cloud-edge integrated collaboration mode for UAV supervision tasks is established. Using the structure walk and feature walk analysis methods of heterogeneous node networks, the semantic associations of different node types are incorporated into the network structure for network representation learning to capture the structural similarity and attribute characteristics of the network.

[0079] Through the node sequence (ne i , nc j , ne i+1 ,…) of the corresponding path to define the "cloud-edge" interaction (ncj , ne i ), "edge-edge" interaction (ne i , ne j ), "edge-cloud-edge" interaction (ne i , nc j , ne i+1 ), "edge-cloud-edge-edge" interaction (ne i , nc j , ne i+1 , ne i+2 ), etc. There are multiple cooperation modes. Among them, ne represents different node sequences (i represents nodes), and nc represents different edge sequences (j represents edges). The information sequence containing node states and their attribute information is represented as various basic cooperation modes and their combined forms within the edge-cloud system, thereby supporting the design and implementation of cooperative optimization scheduling and adaptive resource allocation strategies for edge-cloud computing tasks.

[0080] Step S20: Based on the node sequence characteristics of the edge-cloud integrated cooperation mode, construct an edge-cloud cooperative allocation strategy for the global tasks of cross-edge nodes that adapts to the graph network structure, so as to achieve the edge-cloud cooperative allocation of the global tasks of cross-edge nodes.

[0081] Specifically, first, based on the node sequence representation of the edge-cloud integrated cooperation mode, where the node sequence representation is the attribute information represented by different nodes, construct an edge-cloud cooperative allocation strategy for the global tasks of cross-edge nodes that adapts to the network structure. The process of the edge-cloud cooperative allocation strategy includes: allocating local real-time computing tasks to the edge-end cluster and controlling the global tasks through the cloud to achieve edge-cloud cooperation.

[0082] The local real-time computing tasks are quickly computed by being allocated to the nearby edge-end. If the computing resources of this edge-end are difficult to meet the requirements of low-latency processing, then task coordination is carried out through the cloud, and the tasks are offloaded to another nearby edge-end or the cloud for takeover and processing, realizing cross-edge communication and global cooperative optimization. When the computing tasks transmitted to the edge-end are processed, the edge-end transmits real-time instructions and data to the terminal, and at the same time transmits the data required by applications with low real-time requirements to the cloud, and uses the situation awareness model to perform state awareness and classification processing of the data state for these spatio-temporal data, providing data support for early warning. The cloud has a global control function, is responsible for the storage of big data and the training of intelligent supervision models for processing various supervision tasks, and regularly distributes the trained models to each edge node, further shortening the computing delay of the edge-end. Through the above method, an edge-cloud cooperative scheduling optimization strategy for parallel computing tasks of cross-edge nodes driven by the task volume is realized to ensure the low-latency operation of spatio-temporal flow data. The schematic diagram of cross-edge-end cooperative scheduling is referred to Figure 5 , Figure 5In this, t1 and t2 represent different sub-task schedules, Ue represents an edge computing node (i.e., the edge node of the cloud-edge structure), Uc represents a central computing node (i.e., the central node of the cloud-edge structure), and the meanings of the formulas for t1 and t2 are the scheduling calculation relationships of sub-tasks in the edge sets between different computing nodes.

[0083] Then, a series of independent real-time computing units with different granularities are constructed and dynamically combined in the cloud-edge collaborative scheduling service chain. The cost consumption and efficiency formed during the cross-edge communication and computing process are dynamically estimated and transmitted to the cloud-edge integrated network. A series of independent real-time computing units with different granularities are constructed, and these independent real-time computing units can be dynamically combined in the cloud-edge collaborative scheduling service chain to construct the scheduling service chain for cloud-edge scheduling.

[0084] For the computing job J, it can be decomposed into a series of parallel computing tasks , where P represents the computing task, represents multiple different computing units. Assuming that the communication cost between tasks is C, if the th computing unit and the th computing unit satisfy , represents the empty set, and the communication cost between two computing units satisfies when the computing job J can be expressed as , represents the communication cost between two computing units, then the computing job J is called a dynamic scheduling service task chain for independent parallel real-time computing. In the cloud-edge collaborative scheduling service chain, the above real-time computing units will be intelligently combined and dynamically adjusted in a modular manner. According to the current task allocation strategy and factors such as the network condition and device load status obtained in real time, the system will automatically select the most suitable computing units and their execution order.

[0085] Finally, based on the workflow, the scheduling service chain within the preset time window is automatically executed. According to factors such as the resource status, network conditions, and task priorities of each node within each time window, the computing task units are intelligently allocated and orchestrated to ensure that each node can execute efficiently in parallel within its own time window, thereby maximizing the use of the concurrent processing capabilities of the distributed system and reducing the overall response time. The fine partitioning and optimized combination of the service chain within the time window achieve seamless cooperation and efficient completion of computing tasks in the cloud-edge multi-node environment.

[0086] Step S30: Based on the cloud-edge collaborative allocation strategy for the cross-edge node global task, establish an adaptive optimization mechanism for cloud-edge resource scheduling that takes into account the low-latency costs of global memory, computing, and transmission, and achieve the adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level.

[0087] Specifically, first, establish a cloud-edge resource balanced allocation strategy that takes into account the low-latency costs of global memory, computing, and transmission. Formalize the relevance of spatio-temporal flow real-time computing tasks based on the computing task units formed by different time-point information and different interaction modes. On this basis, map the dependency relationships of computing tasks in different time windows on cloud-edge computing resources and the latency costs of storage, computing, and transmission generated under the current task to the nodes and edges in the network, and perform dynamic calculations of element attributes; construct a load balancing allocation algorithm based on the network structure driven by latency costs, measure the global resource allocation and system load status of the network through balance evaluation indicators, simulate the allocation and execution of cloud-edge computing tasks through the dynamic configuration of node attributes and edge attributes, and converge the task allocation process to the optimal state with the minimum latency cost of a single task and maximized resource utilization as the objective function.

[0088] The load balancing allocation algorithm based on the network structure can be split into the following processes:

[0089] (1). Obtain the computing resources of the edge nodes and the required amount of computing tasks they carry, and coordinately define the current load status of the nodes through the difference between the two, including high load, low load, and balanced state;

[0090] (2). Evaluate the costs of communication and information transmission between nodes, and obtain the edge weights used to assist algorithm decision-making in the network and define the state of the edges;

[0091] (3). Based on node attributes and edge attributes, use the maximum weight sum matching to perform optimal matching of cloud-edge resources and latency costs in the network, and obtain a balanced allocation plan that meets the minimum latency and maximized resource utilization.

[0092] Then, establish a global task cloud-edge resource dynamic allocation mechanism based on the network structure. From the global perspective of the cloud-edge integrated network, the cloud center systematically collects and collates the key information of all global tasks to be executed, including key parameters such as the computational intensity, data volume, latency cost, and dependencies between tasks of the tasks. Subsequently, use the mapping relationship and node attributes to obtain all edge nodes with available resources, comprehensively consider various aspects such as the hardware configuration, network status, and service capabilities of each edge node, and through real-time evaluation of the resource volume and computational efficiency and save the global tasks in the corresponding edge clusters, locate the most suitable edge node for each global task to carry the task, and achieve an accurate match between task requirements and edge computing resources. At the same time, conduct real-time analysis and scientific evaluation of the total resources and actual computational efficiency of these edge nodes to ensure that tasks can be successfully deployed and resources can be optimized. Finally, according to the optimal scheduling strategy, allocate global tasks to the cloud center and relevant edge nodes for processing one by one, complete the full-link management from global task feature analysis, edge node adaptation, edge node screening to specific task allocation, and effectively realize the optimal allocation of resources for global computing tasks in the cloud-edge collaborative environment.

[0093] Finally, establish a dynamic iterative cloud-edge resource scheduling adaptive optimization mechanism. By real-time capturing and updating various core information within the global scope of the cloud-edge integrated network, including real-time tracking and recording the status of all global computing tasks, including the progress of task startup, execution, and completion, and the interdependencies between tasks, accurately grasp the changes in resource requirements for drones to perform tasks at different times and spatial positions, anticipate and prepare resources in advance to cope with possible future resource demand peaks. Then, based on the cloud-edge network topology and task characteristics, establish a latency cost prediction model, calculate the execution costs of tasks on different edge nodes, and minimize the global latency cost as the main optimization goal. Subsequently, based on the process-driven dynamic iterative scheduling algorithm, make adaptive adjustments and optimizations according to the real-time changing task requirements and system resource status, and adaptively adjust the task scheduling plan according to factors such as the real-time requirements of different tasks, network transmission delays, and computational resource tensions, striving to minimize the overall latency cost of task execution to the greatest extent, so as to achieve the adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level. A schematic diagram of the cloud-edge resource scheduling adaptive optimization mechanism considering low latency costs for global memory, computing, and transmission is referred to Figure 6 。

[0094] The present invention proposes a task calculation method for a cloud-edge-end graph network structure. By mapping the topological relationship of cloud-edge resources for large-scale unmanned aerial vehicle (UAV) supervision tasks into a network, a cloud-edge integrated network model structure is constructed, and the computing resources are quantified to provide data support for subsequent collaborative optimization. Subsequently, based on the cloud-edge integrated network model, a cloud-edge integrated collaborative mode for UAV supervision tasks is established, thereby achieving fast calculation of spatio-temporal flow data for UAV supervision.

[0095] The present invention proposes a cross-edge node collaborative allocation strategy based on a network structure. The real-time calculation tasks of UAVs are allocated to edge nodes, and the global tasks are controlled by the cloud. Based on the network structure of computing resources, the loads of each edge-end are analyzed. Through cloud coordination, the computing tasks of high-load nodes are offloaded to low-load edge-ends to achieve load balancing of edge nodes. Subsequently, efficient task calculation is achieved through the dynamic combination of real-time calculation units with different granularities at the edge-end.

[0096] The present invention proposes a cloud-edge resource scheduling adaptive optimization mechanism that takes into account the global low-latency cost. From the UAV task queue, data stream transmission, to the calculation task allocation based on the cloud-edge-end graph network model, and finally to the dynamic scheduling of computing resources to achieve a cloud-edge resource adaptive optimization mechanism with a global low-latency cost for memory, calculation, and transmission. And through the feedback of UAVs, the adaptive dynamic optimization of the cloud-edge resource scheduling algorithm is realized, further improving the resource allocation and calculation efficiency.

[0097] Further, as Figure 7 shown, based on the above cloud-edge collaborative calculation method for spatio-temporal flow data of low-altitude UAV supervision, the present invention also correspondingly provides a cloud-edge collaborative calculation system for spatio-temporal flow data of low-altitude UAV supervision. Among them, the cloud-edge collaborative calculation system for spatio-temporal flow data of low-altitude UAV supervision includes:

[0098] A cloud-edge integrated collaborative mode establishment module 51, which is used to establish a cloud-edge integrated collaborative mode for UAV supervision tasks based on the cloud-edge network structure, and convert the topological relationship of cloud-edge computing resources into a graph network structure;

[0099] A cloud-edge collaborative allocation strategy construction module 52, which is used to construct a cloud-edge collaborative allocation strategy for global tasks across edge nodes that adapts to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaborative mode, so as to achieve cloud-edge collaborative allocation of global tasks across edge nodes;

[0100] A cloud-edge resource scheduling optimization module 53, which is used to establish a cloud-edge resource scheduling adaptive optimization mechanism that takes into account the global low-latency cost for memory, calculation, and transmission based on the cloud-edge collaborative allocation strategy for global tasks across edge nodes, and realize the adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow calculation tasks at the global level.

[0101] Further, as Figure 8As shown, based on the above cloud-edge collaborative computing method and system for low-altitude UAV supervision spatio-temporal flow data, the present invention also correspondingly provides a terminal, which includes a processor 10, a memory 20, and a display 30. Figure 8 Only some components of the terminal are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0102] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as the 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 Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal. Further, the memory 20 may also include both the internal storage unit and the external storage device of the terminal. The memory 20 is used to store application software installed on the terminal and various types of data, such as the program code for installing the terminal. The memory 20 may also be used to temporarily store data that has been output or will be output. In one embodiment, a cloud-edge collaborative computing program 40 for low-altitude UAV supervision spatio-temporal flow data is stored on the memory 20, and this cloud-edge collaborative computing program 40 for low-altitude UAV supervision spatio-temporal flow data can be executed by the processor 10, thereby implementing the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data in this application.

[0103] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chips, and is used to run the program code stored in the memory 20 or process data, such as executing the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data, etc.

[0104] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, 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 through a system bus.

[0105] In one embodiment, when the processor 10 executes the cloud-edge collaborative computing program 40 for low-altitude UAV supervision spatio-temporal flow data in the memory 20, the steps of the above-mentioned cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data are implemented.

[0106] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data, and when the cloud-edge collaborative computing program for low-altitude UAV supervision spatio-temporal flow data is executed by a processor, the steps of the cloud-edge collaborative computing method for low-altitude UAV supervision spatio-temporal flow data as described above are implemented.

[0107] In summary, the present invention provides a cloud-edge collaborative computing method, system, terminal and computer-readable storage medium for low-altitude UAV supervision spatio-temporal flow data. The method includes: establishing a cloud-edge integrated collaborative mode for UAV supervision tasks based on a cloud-edge network structure, and converting the topological relationship of cloud-edge computing resources into a graph network structure; constructing a cloud-edge collaborative allocation strategy for cross-edge node global tasks adapted to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaborative mode to achieve cloud-edge collaborative allocation of cross-edge node global tasks; establishing a cloud-edge resource scheduling adaptive optimization mechanism considering the low-latency cost of global memory, computing and transmission based on the cloud-edge collaborative allocation strategy for cross-edge node global tasks, and realizing adaptive optimization of cloud-edge resource scheduling for spatio-temporal flow computing tasks at the global level. The present invention establishes a cloud-edge collaborative optimization scheduling mechanism for calculating large-scale UAV supervision spatio-temporal flow data in three-dimensional space to support the efficient scheduling requirements for real-time computing of UAV supervision tasks in the cloud-edge collaborative mode.

[0108] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or terminal including the element.

[0109] Certainly, those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium readable by a computer. When the program is executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disc, etc.

[0110] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A cloud-edge collaborative computing method for low-altitude UAV monitoring spatiotemporal flow data, characterized in that: The cloud-edge collaborative computing method for low-altitude UAV supervision spatiotemporal flow data includes: Establish a cloud-edge integrated collaboration model for drone monitoring tasks based on a cloud-edge network structure, and convert the topological relationship of cloud-edge computing resources into a graph network structure; Based on the node sequence characteristics of the cloud-edge integrated collaboration mode, a cloud-edge collaborative allocation strategy for global tasks across edge nodes that adapts to the graph network structure is constructed to achieve cloud-edge collaborative allocation of global tasks across edge nodes; Based on the cloud-edge collaborative allocation strategy of global tasks across edge nodes, an adaptive optimization mechanism for cloud-edge resource scheduling that takes into account the global storage, computing and transmission low latency cost is established to achieve adaptive optimization of cloud-edge resource scheduling for spatiotemporal stream computing tasks at the global level. The establishment of a cloud-edge resource scheduling adaptive optimization mechanism that takes into account the global storage, computing and transmission low latency cost specifically includes: Establish a balanced cloud-edge resource allocation strategy that takes into account the global storage, computing, and transmission low latency costs; Establish a global task cloud-edge resource dynamic allocation mechanism based on network structure; Establish a dynamic and iterative cloud-edge resource scheduling adaptive optimization mechanism; The above-mentioned cloud-edge resource balanced allocation strategy that takes into account the global storage, computing and transmission low latency cost specifically includes: The correlation of real-time computing tasks of spatiotemporal streams is formally described based on computing task units formed by information at different time points and different interaction modes. The dependency of computing tasks in different time windows on cloud-edge computing resources and the storage, computing, and transmission delay costs generated under the current task are mapped to nodes and edges in the network, and dynamic calculation of feature attributes is performed. Construct a network-structure-based load balancing allocation algorithm driven by latency cost, measure the global resource allocation and system load status of the network through balance evaluation indicators, simulate cloud-edge computing task allocation and execution with dynamic configuration of node attributes and edge attributes, and converge the task allocation process to the optimal state with the minimum latency cost of a single task and maximum resource utilization as the objective function; The process of load balancing based on network structure includes: Obtain computing resources of edge nodes and computing task requirements carried by them, and coordinate and define the current load state of the nodes through the difference in computing task requirements, wherein the load state includes high load, low load and balanced state; Evaluate the cost of communication and information transmission between nodes, obtain edge weights in the network to assist algorithm decision-making, and define edge states; Based on node attributes and edge attributes, maximum weight matching is used to optimally match cloud-edge resources and delay costs in the network to obtain a balanced allocation solution that satisfies minimum delay and maximum resource utilization.

2. The cloud-edge collaborative computing method for low-altitude UAV monitoring spatiotemporal flow data according to claim 1 is characterized in that: The cloud-edge integrated collaborative mode for drone monitoring tasks based on the cloud-edge network structure is established, and the topological relationship of cloud-edge computing resources is converted into a graph network structure, specifically including: Map the topological relationship of cloud-edge resources for large-scale drone supervision and build a cloud-edge integrated network model. = (Uc, Ue, V), where Uc represents the central node of the cloud-edge structure, Ue represents the edge node of the cloud-edge structure, and V represents the homogeneous attribute edge set in the cloud-edge integrated network model; Define a set of heterogeneous points (Uc, Ue), Uc = {nc1, nc2, ..., nc n }, represents the cloud center node and related attribute mapping in the cloud edge structure, where nc1 to nc n Represents multiple attribute mappings in the central node, Ue={ne1, ne2, ..., ne n }, indicating edge nodes and related attribute mapping, where ne1 to ne n Represents multiple attribute mappings in edge nodes; node attributes are represented as: ； in, Represents node attributes, including central nodes and edge nodes. Considering the influencing factors comprehensively, Indicates node computing resources, Represents node storage resources, Indicates the node function type, Indicates the current load status or task deployment of the node; Define a homogeneous attribute edge set V: ； in, represents the homogeneity property of the edge set between edge nodes, It represents the transmission cost incurred by the nodes to interact, including the delay in the communication or information transmission process. Indicates the amount of computing tasks migrated by the node during task scheduling. represents the efficiency of information transmission between nodes, Indicates the type of communication or edge connectivity between nodes; Cloud-edge integrated network model The expression is: ； in, Indicates the number of cloud nodes, Expressed as the number of edge nodes, Represents the cloud-cloud subnetwork, which is used to describe the communication status between cloud nodes. represents the edge-edge subnetwork, which is used to characterize the cross-edge task computing process. and Representing cloud-edge heterogeneous bipartite networks with different numbers of nodes, used to characterize the communication process between cloud and edge; Based on the cloud-edge integrated network model, a cloud-edge integrated collaborative mode for drone supervision tasks is established. The structural walk and feature walk analysis methods of heterogeneous node networks are used to integrate the semantic associations of different node types into the network structure for network representation learning, thereby capturing the structural similarity and attribute characteristics of the network.

3. The cloud-edge collaborative computing method for low-altitude UAV monitoring spatiotemporal flow data according to claim 1 is characterized in that: The process of the cloud-edge collaborative allocation strategy includes: allocating local real-time computing tasks to edge clusters and controlling global tasks through the cloud to achieve cloud-edge collaboration; The node sequence characteristics of the cloud-edge integrated collaborative mode are used to construct a cloud-edge collaborative allocation strategy for global tasks across edge nodes that adapts to the graph network structure to achieve cloud-edge collaborative allocation of global tasks across edge nodes, specifically including: Assign local real-time computing tasks to the nearby edge for computing. If the computing resources of the nearby edge cannot meet the requirements of low-latency processing, the cloud will be used to coordinate the tasks and offload the local real-time computing tasks to another nearby edge or cloud for takeover and processing, so as to achieve cross-edge communication and global collaborative optimization. When the local real-time computing tasks transmitted to the edge are processed, the edge transmits the real-time instructions and data to the terminal, and transmits the spatiotemporal data to the cloud, and uses the situation awareness model to perform state awareness and classification processing on the spatiotemporal data; The cloud is used to store big data and train intelligent supervision models for processing various supervision tasks, and regularly distribute the trained intelligent supervision models to each edge node; The cost loss and efficiency generated in the cross-edge communication and computing process are dynamically estimated and transmitted to the cloud-edge integrated network, multiple independent real-time computing units of different granularities are constructed, and multiple independent real-time computing units are dynamically combined in the cloud-edge collaborative scheduling service chain to build a scheduling service chain for cloud-edge scheduling; Based on the workflow, the scheduling service chain within the preset time window is automatically executed. According to the resource status, network conditions and task priority of each node in each time window, the computing task units are intelligently allocated and orchestrated to ensure that the nodes are efficiently executed in parallel within each time window.

4. The cloud-edge collaborative computing method for low-altitude UAV monitoring spatiotemporal flow data according to claim 1 is characterized in that: The establishment of a global task cloud-edge resource dynamic allocation mechanism based on network structure specifically includes: Based on the global perspective of the cloud-edge integrated network, the cloud center systematically collects and organizes the target key information of all global tasks to be executed, including the computing intensity of the task, the amount of data, the delay cost, and the dependencies between tasks; Use mapping relationships and node attributes to obtain all edge nodes with available resources, comprehensively consider the hardware configuration, network status and service capabilities of each edge node, conduct real-time evaluation of resource volume and computing efficiency, save global tasks in the corresponding edge cluster, and locate the most suitable edge node for each global task; Based on the optimal scheduling strategy, global tasks are assigned to the cloud center and related edge nodes for processing one by one, so as to complete the full-link management from global task feature analysis, edge node adaptation, edge node screening to specific task allocation.

5. The cloud-edge collaborative computing method for low-altitude UAV monitoring spatiotemporal flow data according to claim 1 is characterized in that: The establishment of a dynamic iterative cloud-edge resource scheduling adaptive optimization mechanism specifically includes: Capture and update the target core information in the global scope of the cloud-edge integrated network in real time, and track and record the status of all global computing tasks in real time, including the progress of task initiation, execution, and completion, as well as the interdependencies between tasks; Based on the cloud-edge network topology and task characteristics, a delay cost prediction model is established to calculate the execution cost of tasks on different edge nodes, and the minimization of global delay cost is taken as the main optimization goal; Based on the process-driven dynamic iterative scheduling algorithm, it makes adaptive adjustments and optimizations according to the real-time changing task requirements and system resource status, and adaptively adjusts the task scheduling plan according to the real-time requirements of different tasks, network transmission delays and computing resource constraints.

6. A cloud-edge collaborative computing system for low-altitude UAV monitoring spatiotemporal flow data, characterized in that: The cloud-edge collaborative computing system for low-altitude UAV supervision spatiotemporal stream data is applied to the cloud-edge collaborative computing method for low-altitude UAV supervision spatiotemporal stream data according to any one of claims 1 to 5, and the cloud-edge collaborative computing system for low-altitude UAV supervision spatiotemporal stream data includes: The cloud-edge integrated collaborative model establishment module is used to establish a cloud-edge integrated collaborative model for drone supervision tasks based on the cloud-edge network structure, and convert the topological relationship of cloud-edge computing resources into a graph network structure; A cloud-edge collaborative allocation strategy construction module is used to construct a cloud-edge collaborative allocation strategy for global tasks across edge nodes that is adapted to the graph network structure based on the node sequence characteristics of the cloud-edge integrated collaborative mode, so as to realize cloud-edge collaborative allocation of global tasks across edge nodes; The cloud-edge resource scheduling optimization module is used to establish a cloud-edge resource scheduling adaptive optimization mechanism that takes into account the global storage, computing and transmission low latency costs based on the cloud-edge collaborative allocation strategy of the global tasks across edge nodes, and realize the cloud-edge resource scheduling adaptive optimization of spatiotemporal stream computing tasks at the global level.

7. A terminal, characterized in that: The terminal includes: a memory, a processor, and a cloud-edge collaborative computing program for low-altitude UAV supervision spatiotemporal stream data stored in the memory and runnable on the processor. When the cloud-edge collaborative computing program for low-altitude UAV supervision spatiotemporal stream data is executed by the processor, the steps of the cloud-edge collaborative computing method for low-altitude UAV supervision spatiotemporal stream data as described in any one of claims 1-5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a cloud-edge collaborative computing program for low-altitude UAV supervision spatiotemporal flow data. When the cloud-edge collaborative computing program for low-altitude UAV supervision spatiotemporal flow data is executed by a processor, it implements the steps of the cloud-edge collaborative computing method for low-altitude UAV supervision spatiotemporal flow data as described in any one of claims 1-5.

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