Edge-end collaborative computing power allocation method, device, equipment, medium and program product

Abstract

The embodiment of the present invention discloses a method, device, equipment, medium and program product for edge collaborative computing power allocation. It includes: obtaining the business logistics information of each terminal node in the current business cycle; inputting the business logistics information of each terminal node into the pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; determining the real-time computing power demand according to the real-time terminal node and the real-time computing power prediction value, and dividing the total computing power resources into the real-time business container area and the non-real-time business container area according to the real-time computing power demand; determining the non-real-time access node for each time slot corresponding to the next business cycle according to the non-real-time computing power resources of the non-real-time business container area and the node weight of each non-real-time terminal node, and allocating non-real-time computing power resources to each non-real-time access node in the non-real-time business container area. This enables the computing power resources of the edge node to be fully utilized, distributed services to be flexibly deployed, and improves the processing capability of the power Internet of Things network for services.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology, and in particular to a method, device, equipment, medium and program product for edge-end collaborative computing power allocation. Background Art

[0002] The Internet of Things is an expanded application and network extension of communication networks and the Internet. It uses sensing technology and intelligent equipment to perceive and identify the physical world, and performs calculations, processing and knowledge mining through network transmission and interconnection, to achieve information interaction and seamless connection between people and things, and between things, and to achieve the purpose of controlling, accurately managing and making scientific decisions about the physical world. It has been widely used in the power sector to form the power Internet of Things.

[0003] With the rapid development of the power Internet of Things (IoT), the explosive growth in data volume has far exceeded the capacity of network bandwidth. The emergence of a large number of new intelligent applications has placed higher demands on system latency. Edge computing, as a new computing model, enables timely and efficient data processing at the edge network near the source. In this edge computing model, terminals offload tasks to nearby edge computing devices for execution, effectively alleviating the network congestion and high latency caused by traditional cloud computing, which requires all data to be transmitted to the data center. However, edge computing still faces bottlenecks, mainly due to its limited computing, communication, and storage resources. As the number of terminals and the scale of data continue to increase, edge nodes tend to be saturated, which will lead to a serious decline in the service processing capabilities of edge-side converged terminals.

[0004] The scale of terminals, data, and services accessed within the current power Internet of Things (IoT) continues to grow. Massive amounts of power data require analysis and computation on edge computing devices to enable efficient and flexible business processing and decision-making. Due to the distributed nature of new power system services, the operating mode of edge computing nodes is gradually shifting from a single, independent operation mode to a distributed access and collaborative interaction mode between edge nodes and numerous terminal nodes. The current edge-side and terminal-side operating modes are no longer able to meet the growing demands of distributed business deployment, potentially preventing the timely execution of important services within the power network and reducing the business processing capabilities of the IoT. Summary of the Invention

[0005] The present invention provides a method, device, equipment, medium and program product for edge collaborative computing power allocation. Based on the different business types corresponding to the numerous terminal nodes connected to the edge node, computing power resources with different collaborative mechanisms are allocated to each terminal node in the edge node to ensure that more important businesses can be executed in a timely manner and the computing power resources of the edge node can be fully utilized, so that distributed businesses can be flexibly deployed, thereby improving the processing capabilities of the power Internet of Things network for business.

[0006] In a first aspect, an embodiment of the present invention provides a method for allocating edge collaborative computing power, including:

[0007] Obtain business logistics information of each terminal node in the current business cycle;

[0008] Input logistics information of various industries into the pre-trained business classification prediction model to determine the real-time terminal nodes and real-time computing power prediction values;

[0009] Determine the real-time computing power demand based on the real-time terminal nodes and the real-time computing power forecast value, and divide the total computing power resources into the real-time business container area and the non-real-time business container area based on the real-time computing power demand, so as to allocate the computing power resources corresponding to the real-time computing power demand to the real-time business container area;

[0010] Based on the non-real-time computing resources in the non-real-time service container area and the node weights of each non-real-time terminal node, the non-real-time access nodes for each time slot corresponding to the next service cycle are determined, and non-real-time computing resources are allocated to each non-real-time access node in the non-real-time service container area.

[0011] In a second aspect, an embodiment of the present invention further provides an edge-end collaborative computing power allocation device, comprising:

[0012] Information acquisition module, used to obtain business logistics information of each terminal node in the current business cycle;

[0013] The real-time computing power prediction module is used to input logistics information of various industries into the pre-trained business classification prediction model to determine the real-time terminal nodes and real-time computing power prediction values;

[0014] A business container partitioning module is used to determine the real-time computing power demand based on the real-time terminal nodes and the real-time computing power prediction value, and to divide the total computing power resources into a real-time business container area and a non-real-time business container area based on the real-time computing power demand, so as to allocate the computing power resources corresponding to the real-time computing power demand to the real-time business container area;

[0015] The non-real-time computing power allocation module is used to determine the non-real-time access nodes for each time slot corresponding to the next business cycle based on the non-real-time computing power resources in the non-real-time business container area and the node weights of each non-real-time terminal node, and to allocate non-real-time computing power resources to each non-real-time access node in the non-real-time business container area.

[0016] In a third aspect, an embodiment of the present invention further provides an edge collaborative computing power allocation device, the edge collaborative computing power allocation device comprising:

[0017] at least one processor; and a memory communicatively coupled to the at least one processor;

[0018] In which, the memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that at least one processor can implement the edge collaborative computing power allocation method of any embodiment of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the edge collaborative computing power allocation method of any embodiment of the present invention.

[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, is used to execute the edge collaborative computing power allocation method of any embodiment of the present invention.

[0021] The embodiments of the present invention provide an edge collaborative computing power allocation method, device, equipment, medium and program product, which obtains the business logistics information of each terminal node in the current business cycle; inputs the business logistics information into a pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; determines the real-time computing power demand based on the real-time terminal node and the real-time computing power prediction value, and divides the total computing power resources into a real-time business container area and a non-real-time business container area according to the real-time computing power demand, so as to allocate the computing power resources corresponding to the real-time computing power demand to the real-time business container area; determines the non-real-time access nodes for each time slot corresponding to the next business cycle based on the non-real-time computing power resources of the non-real-time business container area and the node weight of each non-real-time terminal node, and allocates non-real-time computing power resources to each non-real-time access node in the non-real-time business container area. By adopting the above technical solution, different terminal nodes within the access edge nodes are divided into real-time terminal nodes and non-real-time terminal nodes based on the type of services they need to implement. Real-time computing power is then predicted for real-time terminal nodes, and total computing power resources are divided into a real-time service container zone, which reserves and allocates full computing power to real-time terminal nodes, and a non-real-time service container zone, which provides partial computing power to non-real-time terminal nodes. This utilizes a reservation-based coordination mechanism for real-time services. For non-real-time services, the available non-real-time access nodes for computing power provision in different time slots within the next service cycle are determined based on the available non-real-time computing power resources within the non-real-time service container zone and the node weights of different non-real-time terminal nodes. This utilizes a competitive coordination mechanism for non-real-time services, providing computing power resources to the most important non-real-time services within the same time period. This ensures that more important services can be executed promptly and the computing power resources of edge nodes are fully utilized. This allows for the flexible deployment of distributed services and enhances the service processing capabilities of the Power Internet of Things network.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a method for allocating edge-to-edge collaborative computing power provided in Example 1 of the present invention;

[0025] Figure 2 This is a flow chart of a method for allocating edge-to-edge collaborative computing power provided in Example 2 of the present invention;

[0026] Figure 3 This is a structural example diagram of a GRU unit provided in Example 2 of the present invention;

[0027] Figure 4 A schematic diagram of the structure of an edge-to-edge collaborative computing power allocation device provided in Example 3 of the present invention;

[0028] Figure 5 A structural diagram of an edge collaborative computing power allocation device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a method for allocating computing power to an edge provided by the first embodiment of the present invention. The embodiment of the present invention is applicable to the situation where computing power resources are divided for edge computing nodes of the electric power Internet of Things. The method can be executed by an edge collaborative computing power allocation device, which can be implemented by software and / or hardware. The edge collaborative computing power allocation device can be configured in an edge collaborative computing power allocation device. Optionally, the edge collaborative computing power allocation device can be an electronic device, which can be a notebook, desktop computer, smart tablet, and edge computing device in the electric power Internet of Things, etc. The embodiment of the present invention does not limit this.

[0033] like Figure 1 As shown, an embodiment of the present invention provides a method for allocating edge collaborative computing power, which specifically includes the following steps:

[0034] S101. Obtain business logistics information of each terminal node in the current business cycle.

[0035] In this embodiment, a business cycle can be specifically understood as the time period required for a business to start and end. The current business cycle can be specifically understood as the business cycle at the current moment. A terminal node can be specifically understood as a power terminal device connected to the same edge computing device as an edge computing node in the edge network corresponding to the power Internet of Things. It can be understood that each terminal node can perform different businesses. Business logistics information can be specifically understood as business feature information related to the business executed by the terminal node.

[0036] Specifically, to fully utilize the computing resources of the edge computing device during the edge computing process, the edge computing device can be accessed based on the current business cycle, and the computing resources within the edge computing device can be pre-allocated for the business executed using the computing resources of the edge computing device for the next business cycle. To achieve the above purpose, when it is necessary to pre-allocate the computing resources of the edge computing device for the next business cycle, the edge computing device can obtain the business flow information of the business flows transmitted to each terminal node connected to it in the current business cycle through the southbound communication interface provided therein.

[0037] S102: Input the logistics information of each industry into the pre-trained business classification prediction model to determine the real-time terminal node and real-time computing power prediction value.

[0038] In this embodiment, the business classification prediction model can be specifically understood as a neural network model used to classify the business types corresponding to the input business logistics information based on the business characteristics contained in the business logistics information, and to predict the computing power requirements for real-time business types. A real-time terminal node can be specifically understood as a terminal node executing real-time business types. The real-time computing power prediction value can be specifically understood as the predicted amount of computing power resources required for real-time business types during the next business cycle, as obtained through computing power prediction for real-time business types.

[0039] Specifically, the business flow information corresponding to each terminal node is input into a pre-trained business classification prediction model. The business flow information is first feature extracted through the business classification prediction model. Based on the extracted features, it is determined whether the business flow information containing each feature belongs to the real-time business type or the non-real-time business type. The terminal node corresponding to the business flow information belonging to the real-time business type is then determined as a real-time terminal node. Once the business flow information is determined to be real-time business type, computing power resource prediction is performed using the corresponding features to obtain a predicted value of the real-time computing power that the real-time terminal node may require in the next business cycle.

[0040] S103. Determine the real-time computing power demand based on the real-time terminal nodes and the real-time computing power prediction value, and divide the total computing power resources into a real-time business container area and a non-real-time business container area based on the real-time computing power demand, so as to allocate computing power resources corresponding to the real-time computing power demand to the real-time business container area.

[0041] In this embodiment, the real-time computing power demand can be specifically understood as the amount of computing power resources required to execute real-time business in the next business cycle. The real-time business container area can be specifically understood as a container for executing real-time business type services, which includes computing power resources for executing real-time business type services. The non-real-time business container area can be specifically understood as a container for executing non-real-time business type services, which includes computing power resources for executing non-real-time business type services. It can be understood that computing power resources may include central processing unit (CPU) resources, container resources and memory resources, etc., and the embodiment of the present invention does not limit this.

[0042] Specifically, based on the determined real-time terminal nodes and the predicted real-time computing power values, the maximum average amount of computing power resources required to execute real-time business-type services at each moment in the next business cycle is determined, and the determined computing power resource amount is used as the real-time computing power demand. Since real-time business-type services are highly important in the power system and their timely execution must be guaranteed, if the total computing power resources available in the edge computing device can meet the real-time computing power demand, the computing power resources corresponding to the real-time computing power demand in the total computing power resources can be divided into a real-time business container area, so that the real-time business of each real-time terminal node in the next business cycle can be executed using the computing power resources in the real-time business container area. At the same time, the computing power resources in the total computing power resources other than the real-time computing power demand are divided into a container area as a non-real-time business container area, so that the business of the remaining terminal nodes except the real-time terminal nodes in the next business cycle can be executed in the non-real-time business container area.

[0043] In an embodiment of the present invention, access to different types of services in the power Internet of Things edge network is divided by container division, ensuring sufficient computing resources for more important real-time business types. By adjusting the container, the computing resources of the edge node can be fully utilized and flexibly deployed, thereby improving the processing capability of the power Internet of Things network for business.

[0044] S104. Determine the non-real-time access nodes for each time slot corresponding to the next service cycle based on the non-real-time computing power resources of the non-real-time service container area and the node weight of each non-real-time terminal node, and allocate non-real-time computing power resources to each non-real-time access node in the non-real-time service container area.

[0045] In this embodiment, non-real-time computing resources can be specifically understood as computing resources allocated by edge computing devices to the non-real-time service container area and available for use by non-real-time services. Node weights can be specifically understood as weights that indicate the importance of services executed at different times within non-real-time terminal nodes. Non-real-time access nodes can be specifically understood as terminal nodes that require service access for a specific time slot.

[0046] Specifically, based on the non-real-time computing power resources that can be allocated to the non-real-time business container area in the next business cycle, the concurrency upper limit and processing upper limit for non-real-time business type services at the same time can be determined, and then the next business cycle can be divided into multiple time slots. Based on the node weight of each non-real-time terminal node, the weight of each non-real-time terminal node accessing the edge computing device in different time slots is determined. Based on each weight, the non-real-time terminal node that is most suitable for accessing the time slot to perform non-real-time business type services is selected as the non-real-time access node for each time slot in the next business cycle. Then, within the maximum allowable range of the non-real-time computing power resources of the non-real-time business container area, in each time slot in the next business cycle, the computing power resources required by the non-real-time access node accessing the time slot are provided to complete the allocation of non-real-time computing power resources. It can be understood that for a time slot in the next business cycle, if the non-real-time computing power resources are greater than the computing power resources required by the non-real-time access node in the time slot, then only the computing power resources required by the non-real-time access node need to be provided; if the non-real-time computing power resources are less than the computing power resources required by the non-real-time access node in the time slot, then all the non-real-time computing power resources can be provided to the non-real-time access node. Although the non-real-time access node is still unable to execute its corresponding non-real-time business type at the optimal speed at this time, since the importance of non-real-time business type is lower than that of real-time business type, it can still meet the business execution requirements in the power Internet of Things network. The embodiment of the present invention adopts this computing power resource division method to provide relatively sufficient computing power resources for non-real-time business type business to the greatest extent on the basis of ensuring that real-time business type business can be executed in a timely and sufficient manner, so that the computing power resources of the edge computing device can be fully utilized.

[0047] The technical solution of this embodiment obtains the business logistics information of each terminal node in the current business cycle; inputs the business logistics information into a pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; determines the real-time computing power demand based on the real-time terminal node and the real-time computing power prediction value, and divides the total computing power resources into a real-time business container area and a non-real-time business container area according to the real-time computing power demand, so as to allocate the computing power resources corresponding to the real-time computing power demand to the real-time business container area; determines the non-real-time access node for each time slot corresponding to the next business cycle based on the non-real-time computing power resources of the non-real-time business container area and the node weight of each non-real-time terminal node, and allocates non-real-time computing power resources to each non-real-time access node in the non-real-time business container area. By adopting the above technical solution, different terminal nodes within the access edge nodes are divided into real-time terminal nodes and non-real-time terminal nodes based on the type of services they need to implement. Real-time computing power is then predicted for real-time terminal nodes, and total computing power resources are divided into a real-time service container zone, which reserves and allocates full computing power to real-time terminal nodes, and a non-real-time service container zone, which provides partial computing power to non-real-time terminal nodes. This utilizes a reservation-based coordination mechanism for real-time services. For non-real-time services, the available non-real-time access nodes for computing power provision in different time slots within the next service cycle are determined based on the available non-real-time computing power resources within the non-real-time service container zone and the node weights of different non-real-time terminal nodes. This utilizes a competitive coordination mechanism for non-real-time services, providing computing power resources to the most important non-real-time services within the same time period. This ensures that more important services can be executed promptly and the computing power resources of edge nodes are fully utilized. This allows for the flexible deployment of distributed services and enhances the service processing capabilities of the Power Internet of Things network.

[0048] Example 2

[0049] Figure 2A flowchart of an edge collaborative computing power allocation method provided in Example 2 of the present invention. The embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions. A business classification prediction model including a business classification sub-model and a computing power prediction sub-model is used to directly complete the classification and prediction processing of business flow data provided by each terminal node, thereby reducing the process complexity of data processing. At the same time, the real-time computing power demand is pre-determined based on the business arrival volume of each real-time terminal node, the average business data volume of each real-time terminal node, and the real-time computing power prediction value, and the computing power resources of the real-time computing power demand are divided into the real-time business container area on the edge computing device to realize computing power reservation for real-time business. The computing power resources in the edge computing device other than the real-time computing power demand are used as the non-real-time computing power resources of the non-real-time business container area, and the next business cycle is divided into multiple time slots. Each non-real-time terminal node adopts a competitive access mechanism. According to their respective node weights and the uplink transmission limit and concurrent processing limit of the non-real-time terminal node to the edge computing device, the non-real-time access node to be accessed in each time slot is determined. The non-real-time computing power resources that can be allocated to the non-real-time access node are determined based on the historical computing power resources, predicted computing power resources of each non-real-time access node, or by random allocation. This ensures that more important businesses can be executed in a timely manner and the computing power resources of the edge nodes can be fully utilized, so that distributed businesses can be flexibly deployed, thereby improving the business processing capabilities of the power Internet of Things network.

[0050] like Figure 2 As shown, an embodiment of the present invention provides a method for allocating edge collaborative computing power, which specifically includes the following steps:

[0051] S201. Obtain the business logistics information of each terminal node in the current business cycle.

[0052] S202: For each piece of business logistics information, input the business logistics information into the business classification sub-model in the pre-trained business classification prediction model to determine the business type of the terminal node corresponding to the business logistics information.

[0053] In this embodiment, the business classification sub-model can be specifically understood as a neural network sub-model in the business classification prediction model, which is used to extract and classify the business logistics information input therein, and determine whether the business type corresponding to the business logistics information is a real-time business type or a non-real-time business type based on the classification results. Optionally, the business classification sub-model can be a probabilistic classification model based on Bayes' theorem.

[0054] For example, assume that a service flow information has n features, namely {u1,u2,...,u n}, and by classifying each logistics feature, it can be divided into real-time business x1 and non-real-time business x2. Then, using the business classification sub-model to classify it, the probability of the logistics information belonging to real-time business can be determined. and the probability of belonging to non-real-time business The business type with a higher probability between the two is taken as the business type corresponding to the logistics information.

[0055] Specifically, for each business logistics information, the business logistics information is input into the business classification sub-model in the pre-trained business classification prediction model, and the business classification sub-model is used to extract features of the business flow information to obtain multiple business features, and each business feature is classified to determine whether it is a business feature of real-time business or a business feature of non-real-time business. After the type of all business features is determined, the number of real-time business features and the number of total business features can be determined as the probability that the business logistics information belongs to real-time business, and the number of non-real-time business features and the number of total business features can be determined as the probability that the business logistics information belongs to non-real-time business. The two probabilities can be compared, and the business type with a larger probability can be determined as the business type to which the business flow information belongs.

[0056] S203. When the business type of the terminal node is a real-time business type, the terminal node corresponding to the business logistics information is determined as a real-time terminal node, and the business logistics information is input into the computing power prediction sub-model in the pre-trained business classification prediction model to determine the real-time computing power prediction value of the real-time terminal node.

[0057] In this embodiment, the computing power prediction sub-model can be specifically understood as a neural network sub-model within the business classification prediction model that iteratively predicts the business features input therein to determine the computing power requirements corresponding to the business. Alternatively, the computing power prediction sub-model can be a GRU model based on time series prediction, which is not limited in this embodiment of the present invention.

[0058] For example, taking the computing power prediction sub-model as the GRU model, Figure 3 This is a structural example diagram of a GRU unit provided in the second embodiment of the present invention, and its state equation can be expressed as:

[0059]

[0060] Among them, y t-1 is the hidden layer output of the previous GRU unit, x t is the business feature of the current round input, σ is the sigmoid function, z t is the update gate of the GRU unit, y t ' is the updated intermediate state, y t is the output of the GRU unit in this round, U zis the weight of the update gate, W z is the weight of the intermediate state, b z is the offset.

[0061] Specifically, after the business classification sub-model classifies the business flow information input therein, if it is determined that the business type of the terminal node corresponding to the business flow information is a non-real-time business type, it can be considered that there is no need to predict the computing power resources required for the terminal node in the next business cycle. At this time, there is no need to input the business features extracted from the business flow information into the computing power prediction sub-model in the pre-trained business classification prediction model. When the business type of the terminal node is a real-time business type, the terminal node corresponding to the business flow information can first be determined as a real-time terminal node, and the business features extracted from the business flow information can be input into the computing power prediction sub-model in the pre-trained business classification prediction model to perform real-time computing power prediction, and the output of the computing power prediction sub-model is determined as the real-time computing power prediction value of the real-time terminal node.

[0062] S204: Determine the business computing power requirement based on the business arrival volume of each real-time terminal node and the average business data volume of each real-time terminal node.

[0063] In this embodiment, the service arrival volume can be specifically understood as the number of real-time services that can reach the edge computing device from real-time terminal nodes within a service cycle. The average service data volume can be specifically understood as the average amount of data transmitted from real-time services of each real-time terminal node to the edge computing device. The service computing power requirement can be specifically understood as the amount of computing power resources required by the edge computing device to process the real-time services of each real-time terminal node within a service cycle.

[0064] Specifically, based on the business arrival volume of each real-time terminal node and the average business data volume of each real-time terminal node, the amount of real-time business data that the edge computing device will receive from each real-time terminal node at each moment in a business cycle is determined, and it is determined as the business computing power demand.

[0065] For example, the business computing power demand can be expressed by the following formula:

[0066]

[0067] Among them, T is the next business cycle, Y t is the business arrival volume at time t, D t is the average business data volume at time t, and η is the proportional factor between the business data volume and the computing power demand.

[0068] S205. Determine a maximum real-time computing power prediction error based on the real-time computing power prediction value and the prediction error corresponding to the real-time computing power prediction value.

[0069] For example, assume that the real-time computing power prediction value is y t , ε t is the prediction error of the real-time computing power prediction value at time t within period T, then the maximum real-time computing power prediction error can be expressed as

[0070] S206. Determine the sum of the business computing power demand and the maximum real-time computing power prediction error as the real-time computing power demand.

[0071] S207: Use the computing power resources in the total computing power resources that correspond to the real-time computing power demand as real-time computing power resources, and construct a real-time business container area with the resource amount of real-time computing power resources.

[0072] Specifically, to ensure that real-time services can be executed in a timely manner within the next service cycle, when the total computing power resources are sufficient, the computing power resources corresponding to the real-time computing power demand can be used as real-time computing power resources, and a real-time business container area can be divided in the edge computing device, and the determined real-time computing power resources can be used as the computing power resources of the real-time business container area.

[0073] It is understandable that when the total computing power resources are insufficient to support the real-time computing power demand, all the total computing power resources can be used as real-time computing power resources; or a maximum resource ratio that can be provided to real-time services can be divided from the total computing power resources of the edge computing device, and all computing power resources within this resource ratio can be used as real-time computing power resources. The method for determining real-time computing power resources can also be set according to other actual needs, and the embodiments of the present invention do not limit this.

[0074] S208: The computing resources other than the real-time computing resources in the total computing resources are used as non-real-time computing resources, and a non-real-time business container area with a resource amount of non-real-time computing resources is constructed.

[0075] Specifically, after meeting the needs of real-time business, in order to make full use of the computing power resources in the edge computing device, the computing power resources other than real-time computing power resources in the total computing power resources can be used as non-real-time computing power resources, and a non-real-time business container area with a resource amount of non-real-time computing power resources can be constructed to carry the operation of non-real-time business in the next business cycle through the non-real-time business container area.

[0076] S209: Determine the uplink transmission upper limit and the concurrent processing upper limit based on the average bandwidth demand and average computing power demand of each non-real-time terminal node, the non-real-time computing power resources and the total bandwidth of the non-real-time service container area.

[0077] In this embodiment, the average bandwidth requirement can be specifically understood as the average bandwidth required by each non-real-time terminal node to transmit business logistics information to the edge computing device. The average computing power requirement can be specifically understood as the average computing power resources required to process non-real-time services in each non-real-time terminal node. The uplink transmission limit can be specifically understood as the upper limit of the number of concurrent access services for uplink data transmission from the terminal node. The concurrent processing limit can be specifically understood as the upper limit of the number of services that can be concurrently processed by the computing power of the edge computing device.

[0078] For example, suppose B total is the total bandwidth, B avg is the average bandwidth requirement, C total is the non-real-time computing resource, ηD avg is the average computing power requirement, then the upper limit of uplink transmission M1 can be expressed as:

[0079] The concurrent processing upper limit M2 can be expressed as:

[0080] It is understandable that after determining the uplink transmission upper limit and the concurrent processing upper limit, the process further includes: broadcasting the uplink transmission upper limit and the concurrent processing upper limit to each non-real-time terminal node.

[0081] Specifically, since each non-real-time terminal node also needs to determine the access time slot required for itself in the next business cycle, after the edge computing device clearly defines the uplink transmission upper limit and the concurrent processing upper limit, the above two types of information need to be broadcast to each non-real-time terminal node so that each non-real-time terminal node can calculate and determine the access time slot required for itself based on the obtained uplink transmission upper limit and concurrent processing upper limit.

[0082] S210. For each non-real-time terminal node, determine the probability of contention access of the non-real-time terminal node in each time slot in the next service cycle based on the uplink transmission upper limit, the concurrent processing upper limit, the total number of time slots corresponding to the next service cycle, and the node weight of the non-real-time terminal node.

[0083] Continuing with the above example, assuming that the next service cycle T can be divided into N time slots, then when the uplink transmission limit M1 and the concurrent processing limit M2 are known, an intermediate parameter can be determined: Then, for each non-real-time terminal node, its contention access probability P in the i-th time slot can be calculated: i for:

[0084]

[0085] Among them, i∈[1,N], ω i is the node weight of the non-real-time terminal node in the i-th time slot.

[0086] It can be understood that after the calculation of S210, the edge computing node can clearly determine the contention access probability of each non-real-time terminal node in each time slot in the next business cycle. That is, assuming there are m non-real-time terminal nodes, the edge computing node can calculate m*N contention access probabilities.

[0087] S211: Determine the non-real-time terminal node as the non-real-time access node in the time slot corresponding to the maximum contention access probability.

[0088] Specifically, since the edge computing node can determine the contention access probability of each non-real-time terminal node in different time slots, it can be considered that the time slot corresponding to the maximum contention access probability is the time slot that the non-real-time terminal node most needs to access in the next business cycle. At this time, the non-real-time terminal node can be determined as a non-real-time access node in the time slot corresponding to its maximum contention access probability.

[0089] It can be understood that since the node weights of different non-real-time terminal nodes are different, the time slots corresponding to the maximum contention access probability of each non-real-time terminal node should be different, that is, the maximum contention access probability can be used to determine a unique non-real-time access node for different time slots in the next service cycle.

[0090] It is understandable that each non-real-time terminal node itself also needs to calculate the contention access probability of all time slots in the next business cycle, and access the edge computing device as a non-real-time access node when the time slot corresponding to the maximum contention access probability is reached.

[0091] S212: Allocate non-real-time computing resources to each non-real-time access node in the non-real-time service container area.

[0092] Optionally, a method for allocating non-real-time computing resources to each non-real-time access node in the non-real-time service container area includes at least one of the following:

[0093] 1) Determine the non-real-time computing power requirements corresponding to each non-real-time access node based on the historical computing power resources of each non-real-time access node, and allocate non-real-time computing power resources to each non-real-time access node based on the non-real-time computing power requirements;

[0094] 2) Forecasting the computing power demand of each non-real-time access node, determining the non-real-time computing power demand corresponding to each non-real-time access node, and allocating non-real-time computing power resources to each non-real-time access node based on the non-real-time computing power demand;

[0095] 3) Randomly allocate non-real-time computing resources to each non-real-time access node.

[0096] Specifically, any of the three methods described above can be used to determine the non-real-time computing resources allocated to the corresponding non-real-time access node by the non-real-time service container area when it arrives at the corresponding non-real-time access node access time slot. If the first method is used, then at each time slot, based on the historical computing resources of the non-real-time access nodes that access that time slot, the historical computing resources can be averaged or the historical computing resources with the highest frequency of occurrence can be taken according to a normal distribution as the non-real-time computing power demand. If the second method is used, at each time slot, the historical computing resources of the non-real-time access nodes that access that time slot can be input into a pre-trained computing resource prediction model for prediction, and the resulting output can be determined as the non-real-time computing power demand of the corresponding non-real-time access node. If the third method is used, a value within the allowable range of non-real-time computing resources can be randomly determined at each time slot as the non-real-time computing power demand of the corresponding non-real-time access node. When the non-real-time computing power demand is met, non-real-time computing resources are allocated to the corresponding non-real-time access node.

[0097] The technical solution of this embodiment directly completes the classification and prediction processing of business flow data provided by each terminal node through a business classification prediction model including a business classification sub-model and a computing power prediction sub-model, thereby reducing the process complexity of data processing. At the same time, the real-time computing power demand is pre-determined based on the business arrival volume of each real-time terminal node, the average business data volume of each real-time terminal node and the real-time computing power prediction value, and the computing power resources of the real-time computing power demand are divided into the real-time business container area on the edge computing device to realize computing power reservation for real-time business. The computing power resources in the edge computing device other than the real-time computing power demand are used as the non-real-time computing power resources of the non-real-time business container area, and the next business cycle is divided into multiple time slots. Each non-real-time terminal node adopts a competitive access mechanism. According to their respective node weights and the uplink transmission limit and concurrent processing limit of the non-real-time terminal node to the edge computing device, the non-real-time access node to be accessed in each time slot is determined. The non-real-time computing power resources that can be allocated to the non-real-time access node are determined based on the historical computing power resources, predicted computing power resources of each non-real-time access node, or by random allocation. This ensures that more important businesses can be executed in a timely manner and the computing power resources of the edge nodes can be fully utilized, so that distributed businesses can be flexibly deployed, thereby improving the business processing capabilities of the power Internet of Things network.

[0098] Example 3

[0099] Figure 4 This is a structural diagram of an edge-end collaborative computing power allocation device provided in Example 3 of the present invention, such as Figure 4 As shown, the edge collaborative computing power allocation device includes an information acquisition module 31, a real-time computing power prediction module 32, a business container division module 33 and a non-real-time computing power allocation module 34.

[0100] Among them, the information acquisition module 31 is used to obtain the business logistics information of each terminal node in the current business cycle; the real-time computing power prediction module 32 is used to input the business logistics information of each terminal node into the pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; the business container division module 33 is used to determine the real-time computing power demand based on the real-time terminal node and the real-time computing power prediction value, and divide the total computing power resources into real-time business container area and non-real-time business container area according to the real-time computing power demand, so as to allocate the computing power resources corresponding to the real-time computing power demand to the real-time business container area; the non-real-time computing power allocation module 34 is used to determine the non-real-time access nodes for each time slot corresponding to the next business cycle based on the non-real-time computing power resources of the non-real-time business container area and the node weight of each non-real-time terminal node, and allocate non-real-time computing power resources to each non-real-time access node in the non-real-time business container area.

[0101] The technical solution of the embodiments of the present invention divides different terminal nodes in the access edge nodes into real-time terminal nodes and non-real-time terminal nodes based on the type of services required by the terminal nodes. Real-time computing power is then predicted for the real-time terminal nodes, and the total computing power resources are divided into a real-time service container area that reserves and allocates full computing power resources to real-time terminal nodes, and a non-real-time service container area that provides partial computing power resources to non-real-time terminal nodes. This means that a reservation-based coordination mechanism is adopted for real-time services. For non-real-time services, the non-real-time access nodes that can provide computing power in different time slots within the next service cycle are determined based on the non-real-time computing power resources available to non-real-time services within the non-real-time service container area and the node weights of different non-real-time terminal nodes. This means that a competitive coordination mechanism is adopted for non-real-time services, providing computing power resources to the most important non-real-time services within the same time period. This ensures that more important services can be executed in a timely manner and that the computing power resources of the edge nodes are fully utilized. This allows for the flexible deployment of distributed services and improves the service processing capabilities of the power Internet of Things network.

[0102] Optionally, the business classification prediction model includes a business classification sub-model and a computing power prediction sub-model; the real-time computing power prediction module 32 is specifically used to:

[0103] For each business logistics information, input the business logistics information into the business classification sub-model in the pre-trained business classification prediction model to determine the business type of the terminal node corresponding to the business logistics information;

[0104] When the business type of the terminal node is a real-time business type, the terminal node corresponding to the business logistics information is determined as the real-time terminal node, and the business logistics information is input into the computing power prediction sub-model in the pre-trained business classification prediction model to determine the real-time computing power prediction value of the real-time terminal node.

[0105] Optionally, the service container division module 33 is specifically configured to:

[0106] Determine the business computing power requirement based on the business arrival volume of each real-time terminal node and the average business data volume of each real-time terminal node;

[0107] Determine a maximum real-time computing power prediction error based on the real-time computing power prediction value and the prediction error corresponding to the real-time computing power prediction value;

[0108] The sum of the business computing power demand and the maximum real-time computing power prediction error is determined as the real-time computing power demand;

[0109] The computing power resources in the total computing power resources that correspond to the real-time computing power demand are used as real-time computing power resources, and a real-time business container area with resources equal to the real-time computing power resources is constructed;

[0110] The computing resources other than real-time computing resources in the total computing resources are regarded as non-real-time computing resources, and a non-real-time business container area with the resource volume of non-real-time computing resources is constructed.

[0111] Optionally, the non-real-time computing power allocation module 34 is specifically configured to:

[0112] Determine the upper limit for uplink transmission and concurrent processing based on the average bandwidth and computing power requirements of each non-real-time terminal node, the non-real-time computing power resources, and the total bandwidth of the non-real-time service container area.

[0113] For each non-real-time terminal node, the probability of contention access for each time slot in the next service cycle is determined based on the uplink transmission limit, the concurrent processing limit, the total number of time slots corresponding to the next service cycle, and the node weight of the non-real-time terminal node.

[0114] The non-real-time terminal node is determined as the non-real-time access node of the time slot corresponding to the maximum contention access probability.

[0115] Optionally, after determining the upper limit of uplink transmission and the upper limit of concurrent processing, the following steps are also included:

[0116] The uplink transmission limit and concurrent processing limit are broadcast to each non-real-time terminal node.

[0117] Optionally, allocating non-real-time computing resources to each non-real-time access node in the non-real-time service container area includes at least one of the following:

[0118] Determine the non-real-time computing power requirements corresponding to each non-real-time access node based on the historical computing power resources of each non-real-time access node, and allocate non-real-time computing power resources to each non-real-time access node based on the non-real-time computing power requirements;

[0119] Predicting the computing power demand of each non-real-time access node, determining the non-real-time computing power demand corresponding to each non-real-time access node, and allocating non-real-time computing power resources to each non-real-time access node based on the non-real-time computing power demand;

[0120] Randomly allocate non-real-time computing resources to each non-real-time access node.

[0121] The edge collaborative computing power allocation device provided in an embodiment of the present invention can execute the edge collaborative computing power allocation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0122] Example 4

[0123] Figure 5 A structural diagram of an edge collaborative computing power distribution device provided for embodiment four of the present invention. The edge collaborative computing power distribution device 40 can be intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The edge collaborative computing power distribution device 40 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0124] like Figure 5 As shown, the edge collaborative computing power distribution device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. Various programs and data required for the operation of the edge collaborative computing power distribution device 40 can also be stored in the RAM 43. The processor 41, ROM 42 and RAM 43 are connected to each other via a bus 44. The input / output (I / O) interface 45 is also connected to the bus 44.

[0125] Multiple components in the edge collaborative computing power distribution device 40 are connected to an I / O interface 45, including: an input unit 46, such as a keyboard and mouse; an output unit 47, such as various types of displays and speakers; a storage unit 48, such as a disk and optical disk; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the edge collaborative computing power distribution device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0126] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the edge-to-edge collaborative computing power allocation method.

[0127] In some embodiments, the edge collaborative computing power allocation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the computing power allocation device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the edge collaborative computing power allocation method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to execute the edge collaborative computing power allocation method in any other appropriate manner (for example, by means of firmware).

[0128] Optionally, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the edge collaborative computing power allocation method provided in any embodiment of the present invention.

[0129] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0131] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computing power distribution device, which has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball), through which the user can provide input to the computing power distribution device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0134] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0135] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0136] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for allocating edge collaborative computing power, characterized in that: include: Obtain business logistics information of each terminal node in the current business cycle; Input the logistics information of each business into the pre-trained business classification prediction model to determine the real-time terminal node and real-time computing power prediction value; Determining a real-time computing power requirement based on the real-time terminal node and the real-time computing power prediction value, and dividing total computing power resources into a real-time business container area and a non-real-time business container area based on the real-time computing power requirement, so as to allocate computing power resources corresponding to the real-time computing power requirement to the real-time business container area; Determine, based on the non-real-time computing resources of the non-real-time service container area and the node weight of each non-real-time terminal node, the non-real-time access node for each time slot corresponding to the next service cycle, and allocate non-real-time computing resources to each non-real-time access node in the non-real-time service container area; The step of determining the non-real-time access node for each time slot corresponding to the next service cycle based on the non-real-time computing resources of the non-real-time service container area and the node weight of each non-real-time terminal node includes: Determine the uplink transmission limit and concurrent processing limit based on the average bandwidth demand and average computing power demand of each non-real-time terminal node, the non-real-time computing power resources and total bandwidth of the non-real-time service container area; For each non-real-time terminal node, determine, based on the uplink transmission upper limit, the concurrent processing upper limit, the total number of time slots corresponding to the next service cycle, and the node weight of the non-real-time terminal node, a contention access probability of the non-real-time terminal node in each time slot in the next service cycle; Determining the non-real-time terminal node as a non-real-time access node in a time slot corresponding to a maximum contention access probability; The allocating non-real-time computing resources to each of the non-real-time access nodes in the non-real-time service container area includes at least one of the following: Determining, based on historical computing resources of each of the non-real-time access nodes, a non-real-time computing power requirement corresponding to each of the non-real-time access nodes, and allocating non-real-time computing power resources to each of the non-real-time access nodes based on the non-real-time computing power requirement; Performing a computing power demand forecast on each of the non-real-time access nodes, determining a non-real-time computing power demand corresponding to each of the non-real-time access nodes, and allocating non-real-time computing power resources to each of the non-real-time access nodes based on the non-real-time computing power demand; Randomly allocate non-real-time computing resources to each of the non-real-time access nodes.

2. The method according to claim 1, characterized in that The business classification prediction model includes a business classification sub-model and a computing power prediction sub-model; Inputting the logistics information of each business into the pre-trained business classification prediction model to determine the real-time terminal node and real-time computing power prediction value includes: For each of the business logistics information, input the business logistics information into the business classification sub-model in the pre-trained business classification prediction model to determine the business type of the terminal node corresponding to the business logistics information; When the business type of the terminal node is a real-time business type, the terminal node corresponding to the business logistics information is determined as a real-time terminal node, and the business logistics information is input into the computing power prediction sub-model in the pre-trained business classification prediction model to determine the real-time computing power prediction value of the real-time terminal node.

3. The method according to claim 1, characterized in that The determining of the real-time computing power requirement according to the real-time terminal node and the real-time computing power prediction value includes: Determining the business computing power requirement based on the business arrival volume of each of the real-time terminal nodes and the average business data volume of each of the real-time terminal nodes; Determining a maximum real-time computing power prediction error based on the real-time computing power prediction value and a prediction error corresponding to the real-time computing power prediction value; The sum of the business computing power demand and the maximum real-time computing power prediction error is determined as the real-time computing power demand.

4. The method according to claim 3, characterized in that The dividing of the total computing power resources into a real-time business container area and a non-real-time business container area according to the real-time computing power demand includes: The computing power resources corresponding to the real-time computing power demand in the total computing power resources are used as real-time computing power resources, and a real-time business container area with a resource amount equal to the real-time computing power resources is constructed; The computing power resources other than the real-time computing power resources in the total computing power resources are used as non-real-time computing power resources, and a non-real-time business container area with a resource amount equal to the non-real-time computing power resources is constructed.

5. The method according to claim 1, wherein After determining the uplink transmission upper limit and the concurrent processing upper limit, the method further includes: The uplink transmission upper limit and the concurrent processing upper limit are broadcasted to each of the non-real-time terminal nodes.

6. A device for allocating computing power to an edge device, characterized in that: include: Information acquisition module, used to obtain business logistics information of each terminal node in the current business cycle; A real-time computing power prediction module is used to input the logistics information of each business into a pre-trained business classification prediction model to determine the real-time terminal node and the real-time computing power prediction value; a business container partitioning module, configured to determine a real-time computing power requirement based on the real-time terminal node and the real-time computing power prediction value, and to partition total computing power resources into a real-time business container area and a non-real-time business container area based on the real-time computing power requirement, so as to allocate computing power resources corresponding to the real-time computing power requirement to the real-time business container area; a non-real-time computing power allocation module, configured to determine the non-real-time access nodes for each time slot corresponding to the next service cycle based on the non-real-time computing power resources of the non-real-time service container area and the node weights of each non-real-time terminal node, and to allocate non-real-time computing power resources to each of the non-real-time access nodes within the non-real-time service container area; The non-real-time computing power allocation module is specifically used to: Determine the uplink transmission limit and concurrent processing limit based on the average bandwidth demand and average computing power demand of each non-real-time terminal node, the non-real-time computing power resources and total bandwidth of the non-real-time service container area; For each non-real-time terminal node, determine, based on the uplink transmission upper limit, the concurrent processing upper limit, the total number of time slots corresponding to the next service cycle, and the node weight of the non-real-time terminal node, a contention access probability of the non-real-time terminal node in each time slot in the next service cycle; Determining the non-real-time terminal node as a non-real-time access node in a time slot corresponding to a maximum contention access probability; The allocating non-real-time computing resources to each of the non-real-time access nodes in the non-real-time service container area includes at least one of the following: Determining, based on historical computing resources of each of the non-real-time access nodes, a non-real-time computing power requirement corresponding to each of the non-real-time access nodes, and allocating non-real-time computing power resources to each of the non-real-time access nodes based on the non-real-time computing power requirement; Performing a computing power demand forecast on each of the non-real-time access nodes, determining a non-real-time computing power demand corresponding to each of the non-real-time access nodes, and allocating non-real-time computing power resources to each of the non-real-time access nodes based on the non-real-time computing power demand; Randomly allocate non-real-time computing resources to each of the non-real-time access nodes.

7. The device according to claim 6, characterized in that The business classification prediction model includes a business classification sub-model and a computing power prediction sub-model; the real-time computing power prediction module is specifically used to: For each of the business logistics information, input the business logistics information into the business classification sub-model in the pre-trained business classification prediction model to determine the business type of the terminal node corresponding to the business logistics information; When the business type of the terminal node is a real-time business type, the terminal node corresponding to the business logistics information is determined as a real-time terminal node, and the business logistics information is input into the computing power prediction sub-model in the pre-trained business classification prediction model to determine the real-time computing power prediction value of the real-time terminal node.

8. The device according to claim 6, characterized in that The service container division module is specifically used to: Determining the business computing power requirement based on the business arrival volume of each of the real-time terminal nodes and the average business data volume of each of the real-time terminal nodes; Determining a maximum real-time computing power prediction error based on the real-time computing power prediction value and a prediction error corresponding to the real-time computing power prediction value; Determine the sum of the business computing power requirement and the maximum real-time computing power prediction error as the real-time computing power requirement; The computing power resources corresponding to the real-time computing power demand in the total computing power resources are used as real-time computing power resources, and a real-time business container area with a resource amount equal to the real-time computing power resources is constructed; The computing power resources other than the real-time computing power resources in the total computing power resources are used as non-real-time computing power resources, and a non-real-time business container area with a resource amount equal to the non-real-time computing power resources is constructed.

9. A device for allocating computing power to the edge, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; In which, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the edge collaborative computing power allocation method described in any one of claims 1-5.

10. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to execute the edge collaborative computing power allocation method as described in any one of claims 1 to 5.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the edge collaborative computing power allocation method as described in any one of claims 1 to 5.

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