Resource scheduling method, system and device in edge cloud computing platform and medium

Abstract

The invention discloses a resource scheduling method, system and device in an edge cloud computing platform and a medium, and relates to the field of resource scheduling. In the method, a corresponding digital twinborn model is constructed for each edge node according to a resource state and an environmental parameter, a resource change condition in a future preset time is predicted according to the digital twinborn model, and a global resource state map is generated according to the resource change condition; obtaining a task demand of each task, and determining an execution position and a priority of each task according to the task demand and the global resource state map; generating a global allocation scheme through a federated learning algorithm according to the global resource state map, the execution position and the priority; and acquiring a resource allocation state in real time, determining the difference between the resource allocation state and the global allocation scheme, and correcting the global allocation scheme based on the difference through a hierarchical collaboration mechanism. By implementing the technical scheme provided by the invention, the effects of optimizing resource allocation efficiency, reducing delay and energy consumption and improving task processing performance are achieved.

Description

Technical Field

[0001] This application relates to the technical field of resource scheduling, and particularly to a resource scheduling method, system, device, and medium in an edge cloud computing platform. Background Art

[0002] As an emerging computing model, edge cloud computing has been widely used in recent years in fields such as the Internet of Things, intelligent transportation, and smart cities. Its core lies in deploying computing resources at edge nodes close to the data source, thereby reducing data transmission latency, improving response speed, and bandwidth utilization. This distributed architecture can not only effectively relieve the pressure on traditional cloud computing centers but also meet the requirements of application scenarios with high real-time requirements, greatly promoting the digital transformation and intelligent upgrading of all industries.

[0003] To solve the resource scheduling problem, existing technologies usually adopt resource allocation methods based on static rules, and allocate tasks through pre-set priority rules or fixed allocation strategies. However, when facing a large-scale and dynamically changing edge computing environment, the above methods often exhibit problems such as unbalanced resource allocation, low scheduling efficiency, and insufficient response to sudden tasks. Especially in the multi-node collaboration scenario, how to accurately predict resource changes and achieve efficient task scheduling has become a key technical problem to be solved urgently. Summary of the Invention

[0004] This application provides a resource scheduling method, system, device, and medium in an edge cloud computing platform, achieving the effects of optimizing resource allocation efficiency, reducing latency and energy consumption, improving task processing performance, and at the same time ensuring data privacy and load balancing, and is applicable to complex and changing edge computing scenarios.

[0005] In the first aspect of this application, a resource scheduling method in an edge cloud computing platform is provided, which is applied to an edge cloud computing platform. The method includes: Obtain the resource status and environmental parameters of each edge node, construct a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predict the resource change situation within a preset future time according to the digital twin model, and generate a global resource status map according to the resource change situation; Obtain the task requirements of each task, and determine the execution location and priority of each task according to the task requirements and the global resource status map; Generate a global allocation plan through a federated learning algorithm according to the global resource status map, the execution location, and the priority; Obtain the resource allocation status in real time, determine the difference between the resource allocation status and the global allocation plan, and correct the global allocation plan based on the difference through a hierarchical collaboration mechanism.

[0006] Optionally, generating a global resource status map according to the resource change situation includes: Encoding the resource change situation of each edge node into a multi-dimensional label vector, where the multi-dimensional label vector includes available resource labels, latency sensitivity labels, and energy consumption cost labels; Calculating node weights based on weighted calculation of the multi-dimensional label vector, determining edge weights from the network bandwidth and transmission latency between nodes, and constructing a global resource status map based on the topological relationship between edge nodes, the node weights, and the edge weights, where the topological relationship includes physical connection relationships, network hops, and geographical proximity.

[0007] Optionally, determining the execution location and priority of each task according to the task requirements and the global resource status map includes: Determining the task category of each task according to the task requirements, and extracting the key features of each task, where the key features include the computing resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the task; Determining the priority of each task according to the task category and the key features through predefined priority rules; Matching the most suitable execution location according to the priority, the key features, and the global resource status map.

[0008] Optionally, generating a global allocation plan through a federated learning algorithm according to the global resource status map, the execution location, and the priority includes: Calculating the weight of each node according to the total node resources and the historical task success rate, and weighted aggregating the resource allocation proposals of all nodes through the federated averaging algorithm to generate a preliminary global allocation plan; Binding target tasks with priorities higher than the threshold to target edge nodes to adjust the preliminary global allocation plan; Minimizing the total latency and energy consumption of the adjusted preliminary global allocation plan according to a preset multi-objective optimization function, and determining the global allocation plan using mixed integer linear programming.

[0009] Optionally, calculating the weight of each node according to the total node resources and the historical task success rate, and weighted aggregating the resource allocation proposals of all nodes through the federated averaging algorithm to generate a preliminary global allocation plan includes: Counting the resource data of each edge node, and performing weighted summation on the resource data to obtain the total node resources, where the resource data includes CPU, GPU, memory, storage, and network bandwidth, and performing weighted summation on the total node resources and the historical task success rate to obtain the node weights; Generate a resource allocation proposal based on the resource status and task requirements of each edge node, and use the federated averaging algorithm to perform weighted averaging on the resource allocation proposal according to the node weights to obtain a preliminary global allocation plan.

[0010] Optionally, the method further includes: For target tasks with a latency tolerance value lower than the threshold, skip the federated learning aggregation process, and directly match the execution location based on the local resource status map by the edge node; When multiple tasks compete for the same node resources, allocate the resources already allocated to the low-priority tasks to the high-priority tasks; Migrate the low-priority tasks to neighboring edge nodes or enter the waiting queue based on the consistent hashing algorithm.

[0011] Optionally, the migrating the low-priority tasks to neighboring edge nodes or entering the waiting queue based on the consistent hashing algorithm includes: Allocate multiple virtual nodes to each edge node to construct a hash ring. After the low-priority tasks are bound to the virtual nodes, locate the target node to be migrated clockwise according to the hash ring; Determine whether the low-priority task is a task restricted by data sovereignty. When the low-priority task is a task restricted by data sovereignty, determine a node with a load rate lower than the load threshold within a preset geographical area as the target node to be migrated; When there is no node with a load rate lower than the load threshold within the preset geographical area, put the low-priority task into the waiting queue.

[0012] In the second aspect of the present application, a resource scheduling system in an edge cloud computing platform is provided, including a collection module, a task module, an allocation module, and an adjustment module, where: The collection module is configured to obtain the resource status and environmental parameters of each edge node, construct a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predict the resource change situation within a preset future time according to the digital twin model, and generate a global resource status map according to the resource change situation; The task module is configured to obtain the task requirements of each task, and determine the execution location and priority of each task according to the task requirements and the global resource status map; The allocation module is configured to generate a global allocation plan through a federated learning algorithm according to the global resource status map, the execution location, and the priority; The adjustment module is configured to obtain the resource allocation status in real time, determine the difference between the resource allocation status and the global allocation plan, and correct the global allocation plan based on the difference through a hierarchical cooperation mechanism.

[0013] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the above.

[0014] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By constructing a digital twin model for each edge node, the resource status and environmental parameters of the node can be reflected in real time, and future resource changes can be predicted. This method enables resource scheduling to be based not only on the current state, but also to anticipate changes in resource requirements in advance, thus enabling more proactive resource allocation and scheduling; encoding the resource change situations of each node into a multi-dimensional label vector and constructing a global resource status map in combination with the topological relationship between nodes provides a comprehensive resource view, which helps to make more accurate resource scheduling decisions; 2. Determining the execution location and priority of a task according to the requirements of the task (such as computing resources, network bandwidth, latency tolerance, data privacy, etc.) can meet the diverse needs of different tasks and improve the flexibility of task execution; generating a global allocation scheme through a federated learning algorithm not only protects the privacy of nodes, but also can make full use of the resource information of each node to achieve more optimized resource allocation. Especially in a dynamic environment, this method can quickly adapt to changes in resources and tasks; 3. When generating a global allocation scheme, the minimization of total latency and energy consumption is considered, and the final scheme is determined through mixed-integer linear programming, ensuring the efficient use of resources while reducing the energy consumption of the system; monitoring the resource allocation status in real time, and when there is a difference between the actual allocation and the global scheme, it is corrected through a hierarchical cooperation mechanism. This mechanism can adjust resource allocation in a timely manner, avoid resource waste and task execution failures, and improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of a resource scheduling method in an edge cloud computing platform disclosed in an embodiment of the present application; Figure 2 is a schematic diagram of modules of a resource scheduling system in an edge cloud computing platform disclosed in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0017] Explanation of the reference numerals: 201, acquisition module; 202, task module; 203, allocation module; 204, adjustment module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0018] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0019] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0020] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0021] This embodiment discloses a resource scheduling method in an edge cloud computing platform, which is applied to the edge cloud computing platform. Figure 1 is a flow chart of a resource scheduling method in an edge cloud computing platform disclosed in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S101, obtaining the resource status and environmental parameters of each edge node, building a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predicting resource changes within a preset time in the future according to the digital twin model, and generating a global resource status map according to the resource changes; S102, obtaining the task requirements of each task, and determining the execution position and priority of each task according to the task requirements and the global resource status map; S103. Generate a global allocation plan through a federated learning algorithm based on the global resource status map, the execution location, and the priority; S104. Obtain the resource allocation status in real time, determine the difference between the resource allocation status and the global allocation plan, and correct the global allocation plan based on the difference through a hierarchical collaboration mechanism.

[0022] Resource status: including computing power (CPU / GPU utilization), memory occupancy, network bandwidth, storage capacity, etc. Environmental parameters: including node geographical location, mobile device trajectory, wireless channel quality, etc. A lightweight digital twin model is constructed for each edge node, and the input includes real-time resource status parameters, dynamic environmental parameters, and historical resource fluctuation data. The digital twin model is trained using a long short-term memory network to predict the resource change trend within a preset future time window. The time window is dynamically adjusted according to the node load, with a 10-minute window for low load and shortened to 2 minutes for high load. The prediction error is corrected through a sliding window mechanism. When the deviation between the actual resource utilization and the predicted value exceeds the threshold, incremental learning and updating of the digital twin model are triggered. The predicted resource status of each edge node is encoded as a multi-dimensional label vector. Based on the topological relationship between edge nodes (physical connection relationship, network hop count, geographical proximity) and the multi-dimensional label vector, a global resource status map is constructed. The triggering conditions for map updates include periodic updates (default interval of 5 minutes), sudden changes in node resources (such as CPU utilization fluctuation > 20% for 1 minute), and task offloading decisions triggering local updates of the map. Tasks are classified into critical, compute-intensive, privacy-sensitive, etc. according to the QoS (Quality of Service) requirements of the tasks (such as latency ceiling, computational complexity). The computing resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the tasks are extracted. According to the task category and key features, the priority of each task is determined through predefined priority rules. For example, critical tasks have the highest priority, compute-intensive tasks determine their priority based on computing resource requirements and expected execution time, and privacy-sensitive tasks determine their priority considering the data privacy protection level. The most suitable execution location is matched according to the task priority, key features, and global resource status map. For high-priority tasks, edge nodes with sufficient resources, low latency, and reasonable energy consumption costs are preferentially selected; for privacy-sensitive tasks, edge nodes with a high security level or local execution are selected; for compute-intensive tasks, the computing power and network bandwidth of the nodes are comprehensively considered. The resources such as CPU, GPU, memory, storage, and network bandwidth of each node are counted to form a total resource metric. The success rate data of past task executions on the nodes is collected to reflect the stability and reliability of the nodes. Through weighted summation, the total resource metric and historical task success rate are converted into node weights. Each node generates a resource allocation proposal based on its own resource status and task requirements. Using the federated averaging algorithm, the proposals of each node are weighted and averaged according to the node weights to obtain a preliminary global allocation plan. The preliminary global allocation plan is optimized to ensure that the plan meets the execution location and priority constraints of the tasks, while optimizing the resource allocation ratio to improve resource utilization. The resource allocation status is obtained in real time, including metrics such as task execution progress, CPU / GPU utilization, memory occupancy, network bandwidth usage, etc. The difference between the actual resource allocation status and the global allocation plan is determined, and the reasons for the difference are analyzed.For minor differences, local adjustments are made by the edge autonomy layer, such as migrating some containers, adjusting resource quotas, etc. For major differences, global coordination is carried out by the cloud coordination center, such as reallocating tasks to other available nodes. The adjusted data is fed back to the digital twin model for model update and optimization, forming a closed-loop optimization link of prediction - allocation - feedback.

[0023] Optionally, the generating the global resource status map according to the resource change situation includes: Encoding the resource change situations of each edge node into a multi-dimensional label vector, the multi-dimensional label vector including available resource labels, latency sensitivity labels, and energy consumption cost labels; Calculating the node weights by weighted calculation according to the multi-dimensional label vector, determining the edge weights from the inter-node network bandwidth and transmission latency, and constructing a global resource status map based on the topological relationship between edge nodes, the node weights, and the edge weights, the topological relationship including physical connection relationships, network hop counts, and geographical proximity.

[0024] Available resource label: Reflects the resource capabilities that a node can provide for new tasks, including remaining computing power (number of CPU cores / GPU video memory), allocable bandwidth, storage capacity, etc. Latency sensitivity label: Reflects the support capabilities of a node for latency-sensitive tasks, including the average round-trip latency (RTT) from the node to the user device, the maximum latency tolerance threshold, etc. Energy consumption cost label: Measures the energy consumption overhead of executing tasks on this node, including the energy consumption per unit computing power of the node (watts / core), network transmission energy consumption (joules / MB), etc. Obtain the resource change situation of each edge node in real time, including resource utilization rate, task execution situation, etc. Extract the eigenvalue of the above three types of labels from the collected data. Integrate the extracted eigenvalues into a multi-dimensional vector, with each dimension corresponding to a label. According to the system design and actual requirements, set weight factors for the available resource label, latency sensitivity label, and energy consumption cost label respectively to reflect the importance of each label in the node weight calculation. Multiply each label value by its corresponding weight factor and sum them to obtain the comprehensive weight of the node. For example: Node weight = w1 × available resource label + w2 × latency sensitivity label + w3 × energy consumption cost label, where w1, w2, and w3 are weight factors, and w1 + w2 + w3 = 1. Network bandwidth: Reflects the data transmission ability between nodes. The higher the bandwidth, the faster the data transmission and the greater the edge weight. Transmission delay: Reflects the data transmission delay between nodes. The lower the delay, the more timely the data transmission and the greater the edge weight. Combine the network bandwidth and transmission delay to calculate the edge weight. For example, the following formula can be used: Edge weight = α × network bandwidth + β × (1 / transmission delay), where α and β are weight coefficients used to balance the influence of network bandwidth and transmission delay. Represent each edge node as a vertex in the graph, and the attributes of the vertex include the node weight and the multi-dimensional label vector. Represent the connection relationship between nodes as an edge in the graph, and the attributes of the edge include the edge weight. Combine topological information such as physical connection relationship, network hop count, and geographical proximity to construct a complete graph structure. According to the preset update trigger conditions, such as periodic update, node resource mutation, task offloading decision, etc., update the node weight, edge weight, and topological relationship in the graph in a timely manner to ensure the real-time and accuracy of the graph.

[0025] By encoding the resource change situations of each edge node into a multi-dimensional label vector, including available resource labels, latency sensitivity labels, and energy consumption cost labels, the resource status of each node can be comprehensively and accurately reflected. This way of quantification and abstraction makes the complex resource status intuitive and easy to compare and calculate, providing a solid quantitative basis for subsequent resource scheduling decisions. The node weight is calculated by weighted calculation based on the multi-dimensional label vector, which can comprehensively consider multiple factors such as the resource availability, latency characteristics, and energy consumption efficiency of the node. This weight calculation method enables a reasonable evaluation of the node's resource status and provides a scientific reference for subsequent resource allocation. The edge weight is determined by the network bandwidth and transmission latency between nodes, and a global resource status map is constructed based on the topological relationship between edge nodes, fully considering the network connection characteristics between nodes. This helps to select nodes with better network conditions during resource scheduling, reduce data transmission latency, and improve the overall performance of the system. The update trigger conditions of the map include periodic updates, sudden changes in node resources, and local updates triggered by task offloading decisions, which can timely reflect the changes in resource status, ensure the real-time and accuracy of the global resource status map, and enable resource scheduling to make decisions based on the latest resource information.

[0026] Optionally, the determining the execution location and priority of each task according to the task requirements and the global resource status map includes: Determine the task category of each task according to the task requirements, and extract the key features of each task, where the key features include the computing resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the task; Determine the priority of each task according to the task category and the key features through predefined priority rules; Match the most suitable execution location according to the priority, the key features, and the global resource status map.

[0027] Tasks are classified into different categories according to the QoS requirements of the tasks (such as upper limit of latency, computational complexity), such as critical type, computationally intensive type, privacy-sensitive type, etc. For example, industrial control tasks belong to the critical type, video analysis tasks belong to the computationally intensive type, and medical data processing tasks belong to the privacy-sensitive type. Extract the key features of each task, including the computational resource requirements of the task (such as the number of CPU / GPU cores, memory size), network bandwidth requirements, expected execution time, and data privacy level. These features will be used for subsequent priority evaluation and execution location matching. According to the task category and key features, determine the priority of each task through predefined priority rules. For example: Critical tasks are given the highest priority because of their extremely high requirements for real-time performance and reliability; Computationally intensive tasks determine their priorities according to their computational resource requirements and expected execution time, and the higher the requirements and the longer the execution time, the higher the priority; Privacy-sensitive tasks determine their priorities based on considering the data privacy protection level, and the higher the privacy level of the task, the higher the priority. Combine the task category and key features, and evaluate each task according to the predefined priority rules, and assign corresponding priority values. For example, the priority value ranges from 1 to 100, and the higher the value, the higher the priority. According to the priority, key features of the task and the global resource status map, match the most suitable execution location. When matching, mainly consider resource matching degree, latency requirements, privacy protection, and energy consumption cost. Resource matching degree: Whether the computational resource requirements and network bandwidth requirements of the task match the available resources of the node. Latency requirements: Whether the upper limit of the task's latency can be met by the node, especially for latency-sensitive tasks. Privacy protection: For privacy-sensitive tasks, ensure that the execution node has sufficient data security measures. Energy consumption cost: On the premise of meeting the task requirements, select a node with a lower energy consumption cost to save energy. In the global resource status map, traverse each edge node, and find the optimal execution location for each task according to the above matching criteria. For example, for high-priority critical tasks, select edge nodes with sufficient resources, low latency, and reasonable energy consumption cost; for privacy-sensitive tasks, select edge nodes with a high security level or local execution; for computationally intensive tasks, comprehensively consider the computational power and network bandwidth of the node, and select the most suitable node.

[0028] The tasks are divided into different categories (such as critical, computationally intensive, privacy-sensitive) according to the QoS requirements of the tasks (such as upper limit of latency, computational complexity), which provides a basis for subsequent priority determination and execution location matching. Key features such as the computational resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the tasks are extracted to comprehensively capture the resource requirements and execution constraints of the tasks, providing detailed information for accurate resource matching. The most suitable execution location is matched according to the task priority, key features, and the global resource status map. For example, high-priority tasks preferentially select edge nodes with sufficient resources, low latency, and reasonable energy consumption costs; privacy-sensitive tasks select edge nodes with a high security level or local execution; computationally intensive tasks comprehensively consider the computing power and network bandwidth of the nodes. This matching method can ensure that tasks are assigned to the most suitable nodes for execution, improving the success rate and efficiency of task execution. Using information such as multi-dimensional label vectors and node weights in the global resource status map, comprehensively considering the resource status and network topology relationship of the nodes, the optimal execution location for the tasks is found, avoiding task execution failures caused by insufficient local resources or poor network conditions. Through refined task classification and feature extraction, the diverse requirements of different tasks can be met, improving the flexibility of resource scheduling. Whether it is real-time tasks sensitive to latency, data processing tasks with high requirements for computing resources, or tasks with strict requirements for data privacy, reasonable resource allocation and scheduling can be obtained.

[0029] Optionally, the generating of the global allocation scheme through the federated learning algorithm according to the global resource status map, the execution location, and the priority includes: Calculating the weight of each node according to the total node resources and the historical task success rate, and weighted aggregating the resource allocation proposals of all nodes through the federated averaging algorithm to generate a preliminary global allocation scheme; Binding target tasks with priorities higher than the threshold to target edge nodes to adjust the preliminary global allocation scheme; Minimizing the total latency and energy consumption of the adjusted preliminary global allocation scheme according to a preset multi-objective optimization function, and determining the global allocation scheme by using mixed integer linear programming.

[0030] The weight of each node is calculated based on its total resource amount and historical task success rate. The total resource amount includes resources such as CPU, GPU, memory, storage, and network bandwidth, and the total resource amount indicator is obtained through weighted summation. The historical task success rate reflects the stability and reliability of the node. Each node generates a resource allocation proposal based on its own resource status and task requirements, and the proposal contains resource allocation suggestions for each task. Using the Federated Averaging algorithm (FedAvg), the proposals of each node are weighted and averaged according to the node weights to obtain a preliminary global allocation plan. The proposals of nodes with high weights have a greater impact on the global plan. Optimize the preliminary global allocation plan to ensure that the plan meets the execution location and priority constraints of the tasks, and at the same time optimize the resource allocation ratio to improve resource utilization. According to the task priorities, filter out the target tasks with priorities higher than the threshold. For example, the priority threshold is set to 80, and all tasks with priorities higher than 80 are filtered out. In the global resource status graph, select the most suitable target edge node for each high-priority task and bind the task to the node. When selecting, comprehensively consider the resource status of the node, task requirements, and network topology relationship. For example, for the high-priority task T1, select node A with sufficient resources, low latency, and reasonable energy consumption cost for binding. According to the binding relationship between tasks and nodes, adjust the preliminary global allocation plan to ensure that high-priority tasks obtain sufficient resource allocation to meet their QoS requirements. The adjustment process may involve operations such as reallocating resources and adjusting the task execution order. Define a multi-objective optimization function that comprehensively considers two objectives: total latency and energy consumption. For example, the multi-objective optimization function can be expressed as: ; where N represents the total number of tasks, T i and P i represent the latency and energy consumption of task i respectively, and λ is the weight coefficient used to balance latency and energy consumption. Adopt the Mixed Integer Linear Programming (MILP) method to optimize resource allocation under the condition of meeting the task execution location and priority constraints. The specific steps include: define the task allocation variable X ij indicating whether task i is allocated to node j (1 means allocated, 0 means not allocated); ensure that each task can only be allocated to one node and the node resources do not exceed its capacity; minimize the weighted sum of total latency and energy consumption; use the MILP solver to solve the optimization problem to obtain the final global allocation plan.

[0031] When calculating the node weights, resource data such as CPU, GPU, memory, storage, and network bandwidth are statistically analyzed and weighted and summed to obtain the total node resources, which comprehensively reflects the resource status of the nodes. Combining the historical task success rate, the node weights are obtained through weighted summation, taking into account both the richness of resources and the stability of task execution, and accurately reflecting the value of the nodes. The federated averaging algorithm is used to perform weighted averaging on the resource allocation proposals generated by each node, without uploading the original data, protecting data privacy, avoiding single-point failures, and improving the robustness of the system. The resource allocation proposals are weighted and averaged according to the node weights to generate a reasonable and efficient preliminary global allocation plan, improving the efficiency and effectiveness of resource allocation. Target tasks with priorities higher than the threshold are screened, bound to the target edge nodes, and the preliminary plan is adjusted to ensure that critical tasks obtain sufficient resource allocation to meet QoS requirements. A multi-objective optimization function is defined, considering the total latency and energy consumption, and solved through mixed-integer linear programming to obtain the final global allocation plan, achieving a balance between performance and energy consumption. Optimize task allocation, reduce resource waste, and improve resource utilization and task execution success rate. Combining with the real-time update of the global resource status map, the preliminary global allocation plan is adjusted in a timely manner to adapt to the dynamic changes of the resource environment and ensure the stable operation of the system. Reasonably allocate resources, reduce resource waiting and data transmission latency, improve task execution efficiency, and enhance the overall performance of the system and user experience.

[0032] Optionally, calculating the weight of each node according to the total node resources and the historical task success rate, and weighted aggregating the resource allocation proposals of all nodes through the federated averaging algorithm according to the weight to generate a preliminary global allocation plan includes: Statistically analyze the resource data of each edge node, and perform weighted summation on the resource data to obtain the total node resources. The resource data includes CPU, GPU, memory, storage, and network bandwidth. Perform weighted summation on the total node resources and the historical task success rate to obtain the node weights; Generate resource allocation proposals according to the resource status and task requirements of each edge node itself, and use the federated averaging algorithm to perform weighted averaging on the resource allocation proposals according to the node weights to obtain a preliminary global allocation plan.

[0033] Collect the resource data of each edge node, including CPU, GPU, memory, storage, network bandwidth, etc. These data can be obtained through the node's resource monitoring tools or management interfaces. Perform a weighted sum on the collected resource data to obtain the total resource of each node. Collect the success rate data of past task executions on each node, which reflects the stability and reliability of the node. Perform a weighted sum on the total node resources and the historical task success rate to obtain the node weight. Each edge node generates a resource allocation proposal based on its own resource status and task requirements. The proposal includes the resource allocation suggestions for each task on the node, such as the number of CPU cores allocated, the memory size, the network bandwidth, etc. Federated Averaging algorithm (FedAvg): FedAvg is a commonly used federated learning algorithm for aggregating model parameters among multiple nodes. Here, FedAvg is used to perform a weighted average on the resource allocation proposals of each node. Perform a weighted average on the resource allocation proposals of each node according to the node weight to obtain a preliminary global allocation plan. For example, assume that the weights of node A and node B are wA and wB respectively, and their proposals are PA and PB respectively. Then the preliminary global allocation plan P_preliminary can be expressed as: P_preliminary = (wA × PA + wB × PB) / (wA + wB). This preliminary global allocation plan comprehensively considers the resource status and task requirements of each node, and at the same time performs a reasonable weighting according to the node weight, ensuring the rationality and fairness of resource allocation.

[0034] Statistical resource data such as CPU, GPU, memory, storage, and network bandwidth of edge nodes are collected, and weighted summation is performed to obtain the total node resources. This multi-dimensional consideration method comprehensively reflects the resource status of nodes, avoiding the one-sidedness that may be caused by a single resource indicator. The total node resources and the historical task success rate are weighted and summed to obtain the node weight, which takes into account both the resource richness of the node and the task execution stability of the node. This comprehensive evaluation method enables the node weight to more accurately reflect the value of the node in resource scheduling. The federated averaging algorithm is used to perform weighted averaging on the resource allocation proposals generated by each node without uploading the original data of the node to the central server, effectively protecting data privacy. At the same time, this distributed cooperation method avoids single-point failures and improves the robustness of the system. The resource allocation proposals are weighted and averaged according to the node weights, making the resource allocation scheme more reasonable. Nodes with high weights have a greater impact on the global scheme, and their proposals can better reflect the allocation strategies of nodes with rich resources, thus improving the efficiency and effectiveness of resource allocation. After generating the preliminary global allocation scheme, optimization is also carried out to meet the execution location and priority constraints of tasks, and the resource allocation ratio is optimized, further improving resource utilization and the success rate of task execution. Combining with the real-time update mechanism of the global resource status map, it can respond to changes in resource status in a timely manner and dynamically adjust the preliminary global allocation scheme. This dynamic adaptability enables resource scheduling to better cope with the dynamic changes of resources in the edge cloud computing platform and ensures the stable operation of the system.

[0035] Optionally, the method further includes: For target tasks with a latency tolerance value lower than the threshold, skip the federated learning aggregation process, and directly match the execution location based on the local resource status map by the edge node; When multiple tasks compete for the same node resources, allocate the resources already allocated to low-priority tasks to high-priority tasks; Migrate the low-priority tasks to adjacent edge nodes or enter the waiting queue based on the consistent hashing algorithm.

[0036] For target tasks with a latency tolerance value lower than the threshold, there is no need to go through the federated learning aggregation process. Instead, the edge node directly matches the execution location based on the local resource status map. This can reduce latency and improve the response speed of the task. The edge node quickly finds a suitable execution location for the task according to the local resource status map, ensuring that the task can be executed in a timely manner. When multiple tasks compete for the same node resources, the resources already allocated to low-priority tasks are assigned to high-priority tasks. This can ensure that high-priority tasks can obtain sufficient resources to meet their QoS requirements. After the resources of low-priority tasks are preempted, they are migrated to neighboring edge nodes or enter the waiting queue based on the consistent hashing algorithm. Multiple virtual nodes are assigned to each edge node to construct a hash ring. When a low-priority task is bound to a virtual node, the target node to be migrated is located clockwise according to the hash ring. Determine whether the low-priority task is a task restricted by data sovereignty. If so, a node with a load rate lower than the load threshold within the preset geographical area is determined as the target node to be migrated. If there is no suitable node within the preset geographical area, the low-priority task is placed in the waiting queue.

[0037] For target tasks with a latency tolerance value lower than the threshold, skip the federated learning aggregation process to reduce scheduling latency. The edge node directly matches the execution location based on the local resource status map to improve scheduling efficiency, which is suitable for tasks with high real-time requirements. When multiple tasks compete for the same node resources, the resources already allocated to low-priority tasks are assigned to high-priority tasks to ensure that critical tasks obtain sufficient resources and improve the overall efficiency of the system. By reallocating resources, resource idle is avoided and resource utilization is improved. Based on the consistent hashing algorithm, low-priority tasks are migrated to neighboring edge nodes or enter the waiting queue to reduce system oscillation during task migration and improve system fault tolerance. The consistent hashing algorithm enables the system to quickly reallocate tasks when nodes are added or removed, improving scalability and meeting the requirements of large-scale edge cloud computing platforms. Through mechanisms such as skipping the federated learning aggregation process, resource reallocation, and task migration, the system can flexibly respond to different types of task requirements and dynamic resource environments. These mechanisms work together to enable the system to quickly adjust and maintain stable and efficient operation in the face of task priority changes, resource competition, and node dynamic changes.

[0038] Optionally, the migrating the low-priority task to a neighboring edge node or entering the waiting queue based on the consistent hashing algorithm includes: Assigning multiple virtual nodes to each edge node to construct a hash ring, and when the low-priority task is bound to a virtual node, locating the target node to be migrated clockwise according to the hash ring; Determine whether the low-priority task is a task restricted by data sovereignty. When the low-priority task is a task restricted by data sovereignty, determine a node with a load rate lower than the load threshold within a preset geographical area as the target node to be migrated. When there is no node with a load rate lower than the load threshold within the preset geographical area, put the low-priority task into the waiting queue.

[0039] Allocate multiple virtual nodes to each edge node, and these virtual nodes are evenly distributed on the hash ring. The number of virtual nodes can be adjusted according to actual needs to improve the granularity and flexibility of the hash ring. Construct a hash ring and arrange all virtual nodes in a clockwise order on the ring according to their hash values. The hash function can use common hash algorithms such as SHA-1, MD5, etc. When a low-priority task needs to be migrated, bind it to a virtual node. The binding process can calculate the hash value by hashing the identifier or characteristics of the task, and then map the hash value to a virtual node on the hash ring. According to the hash ring, start from the bound virtual node and find the first actually existing edge node in the clockwise direction as the target node to be migrated. This clockwise search method ensures the certainty and efficiency of task migration. Determine whether the low-priority task is a task restricted by data sovereignty. Data sovereignty restriction means that the data of the task can only be processed in a specific geographical area or on specific nodes to meet the requirements of data privacy and security. If the task is restricted by data sovereignty, then find a node with a load rate lower than the load threshold within the preset geographical area as the target node to be migrated. The preset geographical area can be defined according to the data privacy policy or the requirements of the task. Within the preset geographical area, check whether the load rate of each node is lower than the load threshold. The load rate can be expressed as the ratio of the currently allocated resources of the node to the total resources. If a node with a load rate lower than the threshold is found, migrate the low-priority task to this node; if no suitable node is found within the preset geographical area, put the task into the waiting queue and wait for subsequent resource release or node load reduction.

[0040] Multiple virtual nodes are assigned to each edge node to construct a hash ring. When a low-priority task is bound to a virtual node, the target node to be migrated is located clockwise according to the hash ring. This mechanism ensures that tasks can be migrated to appropriate nodes quickly and efficiently, reducing the complexity and overhead of task migration. Determine whether the low-priority task is a task restricted by data sovereignty. If it is restricted, determine the node with a load rate lower than the load threshold within the preset geographical area as the target node to be migrated. This ensures data security and compliance while ensuring that tasks can be executed on appropriate nodes. The consistent hashing algorithm ensures that when nodes are added or removed, only some tasks need to be migrated, and the mapping relationships of the remaining tasks remain unchanged. This avoids large-scale task reallocation caused by node changes and improves the stability of the system. By introducing virtual nodes, the distribution of nodes on the hash ring is more uniform, avoiding the situation where some nodes are overloaded while others are idle, achieving load balancing of the system and enhancing the fault tolerance of the system. Low-priority tasks are migrated to adjacent edge nodes or enter the waiting queue, enabling more efficient use of resources and avoiding waste of resources.

[0041] This embodiment also discloses a resource scheduling system in an edge cloud computing platform. Figure 2 It is a schematic diagram of the modules of the resource scheduling system in the edge cloud computing platform disclosed in the embodiments of the present application. As Figure 2 shown, the system includes a collection module 201, a task module 202, an allocation module 203, and an adjustment module 204, where: The collection module 201 is configured to obtain the resource status and environmental parameters of each edge node, construct a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predict the resource change situation within a preset future time according to the digital twin model, and generate a global resource status map according to the resource change situation; The task module 202 is configured to obtain the task requirements of each task, and determine the execution location and priority of each task according to the task requirements and the global resource status map; The allocation module 203 is configured to generate a global allocation plan through a federated learning algorithm according to the global resource status map, the execution location, and the priority; The adjustment module 204 is configured to obtain the resource allocation status in real time, determine the difference between the resource allocation status and the global allocation plan, and correct the global allocation plan based on the difference through a hierarchical collaboration mechanism.

[0042] Optionally, the collection module 201 is configured to: Encode the resource change situations of each edge node into a multi-dimensional label vector, where the multi-dimensional label vector includes an available resource label, a latency sensitivity label, and an energy consumption cost label; The node weights are calculated by weighted calculation based on the multi-dimensional label vectors, the edge weights are determined by the network bandwidth and transmission delay between nodes, and a global resource status map is constructed based on the topological relationship between edge nodes, the node weights, and the edge weights. The topological relationship includes physical connection relationship, network hop count, and geographical proximity.

[0043] Optionally, the task module 202 is configured to: Determine the task category of each task according to the task requirements, and extract the key features of each task. The key features include the computing resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the task; Determine the priority of each task according to the task category and the key features through predefined priority rules; Match the most suitable execution location according to the priority, the key features, and the global resource status map.

[0044] Optionally, the allocation module 203 is configured to: Calculate the weight of each node according to the total node resources and the historical task success rate, and weighted aggregate the resource allocation proposals of all nodes through the federated averaging algorithm to generate a preliminary global allocation plan; Bind the target tasks with priorities higher than the threshold to the target edge nodes to adjust the preliminary global allocation plan; Minimize the total delay and energy consumption of the adjusted preliminary global allocation plan according to a preset multi-objective optimization function, and use mixed integer linear programming to determine the global allocation plan.

[0045] Optionally, the allocation module 203 is configured to: Statistically analyze the resource data of each edge node, and perform weighted summation on the resource data to obtain the total node resources. The resource data includes CPU, GPU, memory, storage, and network bandwidth, and perform weighted summation on the total node resources and the historical task success rate to obtain the node weights; Generate resource allocation proposals according to the resource status and task requirements of each edge node, and use the federated averaging algorithm to perform weighted averaging on the resource allocation proposals according to the node weights to obtain a preliminary global allocation plan.

[0046] Optionally, the system further includes a migration module, which is configured to: For target tasks with a delay tolerance value lower than the threshold, skip the federated learning aggregation process, and directly match the execution location by the edge node based on the local resource status map; When multiple tasks compete for the same node resources, allocate the resources already allocated to the low-priority tasks to the high-priority tasks; Migrate the low-priority task to an adjacent edge node or into the waiting queue based on the consistent hashing algorithm.

[0047] Optionally, the migration module is further configured to: Allocate multiple virtual nodes to each edge node to construct a hash ring. After the low-priority task is bound to a virtual node, locate the target node to be migrated clockwise according to the hash ring; Determine whether the low-priority task is a task restricted by data sovereignty. When the low-priority task is a task restricted by data sovereignty, determine a node with a load rate lower than the load threshold within a preset geographical area as the target node to be migrated; When there is no node with a load rate lower than the load threshold within the preset geographical area, put the low-priority task into the waiting queue.

[0048] It should be noted that when the device provided in the above embodiment implements its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0049] This embodiment also discloses an electronic device. Refer to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0050] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0051] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0052] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0053] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0054] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As Figure 3 shown, the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for the resource scheduling method in the edge cloud computing platform.

[0055] In Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; while the processor 301 can be used to call the application program stored in the memory 305 that stores the resource scheduling method in the edge cloud computing platform. When executed by one or more processors 301, the electronic device is caused to execute the method in one or more of the above embodiments.

[0056] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0057] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0058] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0059] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0061] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0062] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other embodiments of the present disclosure after considering the disclosure of the specification. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A resource scheduling method in an edge cloud computing platform, characterized in that: Applied to an edge cloud computing platform, the method includes: Obtain the resource status and environmental parameters of each edge node, build a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predict resource changes within a preset time in the future according to the digital twin model, and generate a global resource status map according to the resource changes; Obtaining the task requirements of each task, and determining the execution location and priority of each task according to the task requirements and the global resource status map; Generate a global allocation plan through a federated learning algorithm according to the global resource state map, the execution location and the priority; The resource allocation state is acquired in real time, the difference between the resource allocation state and the global allocation scheme is determined, and the global allocation scheme is modified based on the difference through a hierarchical coordination mechanism.

2. The resource scheduling method in the edge cloud computing platform according to claim 1 is characterized in that: Generating a global resource status map according to the resource change situation includes: Encoding the resource change of each edge node into a multidimensional label vector, wherein the multidimensional label vector includes an available resource label, a delay sensitivity label, and an energy consumption cost label; The node weight is obtained by weighted calculation according to the multidimensional label vector, the edge weight is determined by the network bandwidth and transmission delay between nodes, and a global resource status map is constructed based on the topological relationship between edge nodes, the node weight and the edge weight, wherein the topological relationship includes physical connection relationship, network hop number and geographical location proximity.

3. The resource scheduling method in the edge cloud computing platform according to claim 1, characterized in that: Determining the execution position and priority of each task according to the task requirements and the global resource status map includes: Determine a task category of each task according to the task requirements, and extract key features of each task, wherein the key features include computing resource requirements, network bandwidth requirements, expected execution time, and data privacy level of the task; Determining the priority of each task by a predefined priority rule according to the task category and the key characteristics; The most suitable execution location is matched according to the priority, the key characteristics and the global resource status map.

4. The resource scheduling method in the edge cloud computing platform according to claim 1, characterized in that: Generating a global allocation scheme through a federated learning algorithm according to the global resource state map, the execution location and the priority includes: The weight of each node is calculated based on the total amount of node resources and the historical task success rate. The resource allocation proposals of all nodes are weighted and aggregated through the federated averaging algorithm based on the weight to generate a preliminary global allocation plan. Binding target tasks with a priority higher than a threshold to target edge nodes to adjust the preliminary global allocation scheme; The total delay and energy consumption of the adjusted preliminary global allocation scheme are minimized according to the preset multi-objective optimization function, and the global allocation scheme is determined by mixed integer linear programming.

5. The resource scheduling method in the edge cloud computing platform according to claim 4 is characterized in that: The weight of each node is calculated based on the total node resources and the historical task success rate, and the resource allocation proposals of all nodes are weighted and aggregated through the federated average algorithm according to the weight to generate a preliminary global allocation plan, including: The resource data of each edge node is counted, and the resource data is weighted and summed to obtain the total node resources, wherein the resource data includes CPU, GPU, memory, storage and network bandwidth, and the total node resources and the historical task success rate are weighted and summed to obtain the node weight; A resource allocation proposal is generated according to the resource status and task requirements of each edge node. The resource allocation proposal is weighted averaged according to the node weight using the federated averaging algorithm to obtain a preliminary global allocation plan.

6. The resource scheduling method in the edge cloud computing platform according to claim 1, characterized in that: The method further comprises: For target tasks whose latency tolerance is lower than the threshold, the federated learning aggregation process is skipped, and the edge node directly matches the execution location based on the local resource status graph; When multiple tasks compete for the same node resources, the resources allocated to the low-priority task are allocated to the high-priority task; The low priority task is migrated to an adjacent edge node or enters a waiting queue based on a consistent hashing algorithm.

7. The resource scheduling method in the edge cloud computing platform according to claim 6, characterized in that: The migrating of the low priority task to an adjacent edge node or into a waiting queue based on a consistent hashing algorithm comprises: Allocate multiple virtual nodes to each edge node to build a hash ring. After the low-priority task is bound to the virtual node, locate the target node to be migrated clockwise according to the hash ring. Determining whether the low-priority task is a task subject to data sovereignty restrictions, and when the low-priority task is a task subject to data sovereignty restrictions, determining a node with a load rate lower than a load threshold in a preset geographical area as a target node to be migrated; When there is no node with a load rate lower than a load threshold in the preset geographical area, the low priority task is put into a waiting queue.

8. A resource scheduling system in an edge cloud computing platform, characterized in that: It includes collection module, task module, allocation module and adjustment module, among which: An acquisition module is configured to obtain the resource status and environmental parameters of each edge node, build a corresponding digital twin model for each edge node according to the resource status and the environmental parameters, predict resource changes within a preset time in the future according to the digital twin model, and generate a global resource status map according to the resource changes; A task module configured to obtain task requirements of each task, and determine the execution location and priority of each task according to the task requirements and the global resource status map; An allocation module configured to generate a global allocation plan through a federated learning algorithm according to the global resource state map, the execution location and the priority; The adjustment module is configured to obtain the resource allocation status in real time, determine the difference between the resource allocation status and the global allocation scheme, and modify the global allocation scheme based on the difference through a hierarchical coordination mechanism.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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