ITSS operation and maintenance resource configuration optimization method and system

Abstract

The invention discloses an ITSS operation and maintenance resource configuration optimization method and system. The method comprises the following steps: firstly, establishing a network node model of operation and maintenance resources to be allocated; constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; determining a to-be-allocated operation and maintenance resource configuration model based on the to-be-allocated operation and maintenance resource network node model and the to-be-allocated operation and maintenance resource constraint model; according to the to-be-allocated operation and maintenance resource configuration model, obtaining an initial configuration scheme of optimal configuration of to-be-allocated operation and maintenance resources and a resource configuration deviation matrix; and according to the initial configuration scheme and the resource configuration deviation matrix, obtaining a target configuration scheme for optimal configuration of the operation and maintenance resources to be allocated. Through the systematized and modeled operation and maintenance resource configuration optimization method, efficient, accurate and flexible configuration of the operation and maintenance resources is realized, and the method has remarkable effects of improving the operation and maintenance service quality, reducing the cost and enhancing the enterprise competitiveness.

Description

Technical Field

[0001] The present invention belongs to the field of information technology service management technology, and in particular to an ITSS operation and maintenance resource configuration optimization method and system. Background Art

[0002] With the rapid development of information technology, IT service management (ITSM) plays a vital role in enterprise operations. Among them, IT service support (ITSS) is an important part of ITSM. The effective configuration and optimization of operation and maintenance resources are directly related to the stability and efficiency of enterprise services. Traditional operation and maintenance resource configuration methods often rely on manual experience judgment or simple static allocation strategies, which are difficult to adapt to complex and changing business needs and service environments.

[0003] In actual applications, operation and maintenance resources cover hardware equipment, software tools, human resources, etc., and the allocation of these resources is subject to various constraints, such as cost budget, resource availability, service level agreement (SLA) requirements, etc. Therefore, how to achieve scientific configuration and dynamic optimization of operation and maintenance resources under limited resource conditions has become a key issue in improving IT service quality and operational efficiency.

[0004] Traditional resource allocation methods often have problems such as low configuration efficiency, uneven resource utilization, and difficulty in meeting immediate service needs when facing large-scale and highly complex operation and maintenance environments. Especially driven by emerging technologies such as cloud computing and big data, the IT service environment is becoming increasingly complex, and the configuration requirements of operation and maintenance resources are becoming more diverse and dynamic, which puts higher requirements on the intelligence and automation level of operation and maintenance resource configuration, which has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide an ITSS operation and maintenance resource configuration optimization method and system to address the deficiencies in the prior art. It achieves efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing corporate competitiveness.

[0006] An embodiment of the present application provides an ITSS operation and maintenance resource configuration optimization method, the method comprising: Establish a network node model of operation and maintenance resources to be allocated; According to the constraints of the operation and maintenance resources to be allocated, a constraint model of the operation and maintenance resources to be allocated is constructed; Determining a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; According to the allocation model of the operation and maintenance resources to be allocated, an initial allocation scheme for optimizing the allocation of the operation and maintenance resources to be allocated and a resource allocation deviation matrix are obtained; According to the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated is obtained.

[0007] Optionally, the establishing of a network node model of operation and maintenance resources to be allocated includes: Determine the network nodes of the operation and maintenance resources to be allocated; An adjacency matrix and a characteristic matrix are established according to the connection relationship between the nodes of the operation and maintenance resource network to be allocated; wherein the characteristic data of the characteristic matrix include the node resource status and the node performance index; Based on the adjacency matrix and the characteristic matrix, a network node model of the operation and maintenance resources to be allocated is generated.

[0008] Optionally, constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated includes: Based on minimizing cost and maximizing operation and maintenance resource utilization as allocation goals, limiting the maximum cost of each node and the maximum total cost as constraints, using the multi-objective optimization function as a linear programming function, and using the mixed integer programming method and constraint satisfaction solving algorithm, a constraint model of the operation and maintenance resources to be allocated is generated.

[0009] Optionally, determining a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated includes: Converting the network nodes and operation and maintenance resources in the network node model of the operation and maintenance resources to be allocated into quantitative indicator parameters; Based on the quantitative indicator parameters and with the optimization of node performance as the orientation, a configuration model of the operation and maintenance resources to be allocated is constructed in combination with the constraint model of the operation and maintenance resources to be allocated.

[0010] Optionally, the constructing of a configuration model for operation and maintenance resources to be allocated based on the quantitative indicator parameters and with the node performance being the best, combined with the constraint model for operation and maintenance resources to be allocated, includes: According to the quantitative indicator parameters, a mapping relationship between the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated is constructed; According to the mapping relationship, obtain resource configuration node parameters of the operation and maintenance resource configuration model to be allocated; According to the resource configuration node parameters, with the optimization of node performance as the goal, and in combination with the constraint model of the operation and maintenance resources to be allocated, a configuration model of the operation and maintenance resources to be allocated is generated.

[0011] Optionally, obtaining an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix based on the configuration model of the operation and maintenance resources to be allocated includes: Based on the allocation model of the operation and maintenance resources to be allocated, a nonlinear programming method is combined with a genetic algorithm to solve an initial configuration scheme for the optimal configuration of the operation and maintenance resources to be allocated under preset conditions; Construct an operation and maintenance resource data prediction model based on CNN-LSTM; The initial configuration plan is adjusted according to the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

[0012] Optionally, obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix includes: According to the initial configuration scheme and the resource configuration deviation matrix, an optimization target value in the operation and maintenance resource configuration scheme set is obtained; According to the optimization target value, the operation and maintenance resource configuration scheme set is analyzed using an improved ant colony algorithm to obtain a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated.

[0013] Another embodiment of the present application provides an ITSS operation and maintenance resource configuration optimization system, the system comprising: Establish a module for establishing a network node model of operation and maintenance resources to be allocated; A construction module, used to construct a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; A determination module, configured to determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; An acquisition module, used to obtain an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix according to the configuration model of the operation and maintenance resources to be allocated; The module is used to obtain a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

[0014] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to implement any of the above methods when executed.

[0015] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement any of the above methods.

[0016] Compared with the prior art, the present invention first establishes a network node model of operation and maintenance resources to be allocated; constructs a constraint model of operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated; determines the configuration model of operation and maintenance resources to be allocated based on the network node model of operation and maintenance resources to be allocated and the constraint model of operation and maintenance resources to be allocated; obtains the initial configuration scheme and resource configuration deviation matrix of the optimal configuration of operation and maintenance resources to be allocated based on the configuration model of operation and maintenance resources to be allocated; obtains the target configuration scheme of the optimal configuration of operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. It realizes the efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing the competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a computer terminal for an ITSS operation and maintenance resource configuration optimization method provided by an embodiment of the present invention; Figure 2 A flowchart of an ITSS operation and maintenance resource configuration optimization method provided by an embodiment of the present invention; Figure 3 A structural diagram of an ITSS operation and maintenance resource configuration optimization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0019] The embodiment of the present invention firstly provides an ITSS operation and maintenance resource configuration optimization method, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers, quantum computers, etc.

[0020] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of an ITSS operation and maintenance resource configuration optimization method provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0021] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the ITSS operation and maintenance resource configuration optimization method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0022] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0023] ITSS (Information Technology Service Standards) is a systematic and comprehensive information technology service standard library that comprehensively standardizes information technology service products and their components. It is used to guide the implementation of standardized information technology services. It is a summary and improvement of the best practices in the information technology service industry, and it also consolidates the independent innovation results of various organizations engaged in information technology service research and development, supply, promotion and application.

[0024] ITSS-based operation and maintenance resource allocation is a key link to ensure efficient and stable operation of information technology services. Enterprises need to reasonably allocate operation and maintenance personnel, technology and resources according to business needs and technical needs, and improve service efficiency and quality through process optimization and continuous monitoring and optimization. This will help enhance the competitiveness of enterprises and achieve sustainable business development.

[0025] See also Figure 2 , Figure 2 A flowchart of an ITSS operation and maintenance resource configuration optimization method provided by an embodiment of the present invention may include the following steps: S201: Establish a network node model of operation and maintenance resources to be allocated.

[0026] Specifically, the establishment of a network node model of operation and maintenance resources to be allocated may include: 1. Determine the network nodes of the operation and maintenance resources to be allocated; 2. Establish an adjacency matrix and a feature matrix according to the connection relationship between the nodes of the operation and maintenance resource network to be allocated; wherein the feature data of the feature matrix includes the node resource status and the node performance index; 3. Based on the adjacency matrix and the feature matrix, a network node model of the operation and maintenance resources to be allocated is generated.

[0027] First, determine the network nodes to be allocated for operation and maintenance resources. It is necessary to clarify which resources or entities will be considered as nodes in the network. These nodes may represent different operation and maintenance teams, devices, servers, or other resources that need to be managed and maintained. The process of determining nodes is based on the actual demand and distribution of operation and maintenance resources, and ensures that all key resources are included in the model.

[0028] An adjacency matrix is ​​a matrix that represents the connection relationship between nodes in a graph. In the operation and maintenance resource network, each row and column of the adjacency matrix corresponds to a node, and the elements in the matrix represent the connection between nodes. If there is a direct connection between two nodes, for example, there is a collaborative relationship between two teams, or there is a physical connection between two devices, then the element value at the corresponding position is 1 or a non-zero value, otherwise it is 0.

[0029] The feature matrix is ​​used to describe the specific attributes and status of each node. In the operation and maintenance resource network, the feature matrix can contain a variety of feature data, including but not limited to: Node resource status, such as the current load, remaining capacity, availability, etc. of the node, which reflects the resource usage of the node.

[0030] Node performance indicators, such as response time, failure rate, stability, etc., reflect the operating efficiency and reliability of the node.

[0031] Each row of the feature matrix corresponds to a node, and each column corresponds to a feature data. Through the feature matrix, we can have a more comprehensive understanding of the status and capabilities of each node.

[0032] Finally, after establishing the adjacency matrix and the characteristic matrix, these matrix data can be used to generate the network node model of the operation and maintenance resources to be allocated. The network node model of the operation and maintenance resources to be allocated is a complex network structure that integrates node connection relationships and node characteristics. In practical applications, the network node model of the operation and maintenance resources to be allocated can be used for a variety of operation and maintenance resource management tasks, such as resource allocation, load balancing, fault prediction, etc. By analyzing the node connection relationships and characteristic data in the model, more effective operation and maintenance strategies can be formulated to improve operation and maintenance efficiency and resource utilization.

[0033] S202: Constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated.

[0034] Specifically, constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated may include: Based on minimizing cost and maximizing operation and maintenance resource utilization as allocation goals, limiting the maximum cost of each node and the maximum total cost as constraints, using the multi-objective optimization function as a linear programming function, and using the mixed integer programming method and constraint satisfaction solving algorithm, a constraint model of the operation and maintenance resources to be allocated is generated.

[0035] Among them, in order to achieve the goals of minimizing costs and maximizing the utilization of operation and maintenance resources, while considering the maximum cost of each node and the total cost limit as constraints, a multi-objective optimization function is used for linear programming. A mixed integer programming method and a constraint satisfaction solving algorithm can be used to generate a constraint model for the operation and maintenance resources to be allocated.

[0036] Exemplarily, minimizing the total cost can be expressed as follows: ; Where N represents the total number of operation and maintenance resources, M represents the total number of nodes, and c ij represents the cost of allocating operation and maintenance resource i to node j, x ij Indicates whether operation and maintenance resource i is allocated to node j, x ij It is a binary variable, such as 1 for allocation and 0 for non-allocation.

[0037] Maximizing the utilization of operation and maintenance resources can be expressed by the following formula: ; Among them, u ij It represents the utilization rate of operation and maintenance resource i allocated to node j.

[0038] The maximum cost of each node and the maximum total cost are restricted as constraints, and the multi-objective optimization function is used as a linear programming function. Through the mixed integer programming method and the constraint satisfaction solving algorithm, the constraint model of the operation and maintenance resources to be allocated is generated and regarded as a multi-objective optimization problem. For example, it can be converted into a single-objective optimization problem through transformation methods such as the weighted sum method and the ε-constraint method.

[0039] Exemplarily, the weighted sum method is used to limit the maximum cost of each node and the maximum total cost as constraints, and the two objective functions are weightedly added, that is: ; Among them, u demand,j represents the total demand of node j for operation and maintenance resources, and β represents the weight parameter, which is used to balance the cost and resource utilization.

[0040] Among them, the cost constraint of each node satisfies: , indicating that the cost of each node cannot exceed its budget, C max,j represents the maximum cost constraint for node j. The total cost constraint satisfies: , C total,max represents the maximum limit of the total cost. Binary variable constraints satisfy: x ij ∈{0,1}, ∀i∈{1,...,N}, ∀j∈{,...,M}; the resource constraints of each node satisfy: , R i where represents the total amount of the i-th resource.

[0041] S203: Determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated.

[0042] Specifically, when determining the configuration model of the operation and maintenance resources to be allocated, the previously constructed network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated can be comprehensively considered. These two models respectively provide the structural information of the network nodes and the constraint conditions of resource allocation.

[0043] First, the nodes, connection relationships, and feature data in the network node model of the operation and maintenance resources to be allocated are integrated into a unified framework. This includes the resource status of the nodes, performance indicators, and the connection relationships between them. Then, the constraints in the constraint model of the operation and maintenance resources to be allocated, such as the maximum cost limit of each node, the maximum total cost limit, etc., and the objective functions, such as minimizing costs and maximizing resource utilization, are also integrated into this framework. Based on the integrated model, a configuration strategy for operation and maintenance resources is formulated. This can include determining how many resources should be allocated to each node, the type of resources, etc., and the order in which resources should be allocated.

[0044] Verify the initially formulated operation and maintenance resource configuration model to ensure that it meets all constraints and can achieve the optimization of the objective function. After verification and optimization, generate the final operation and maintenance resource configuration model to be allocated. This model should be a complete solution that integrates the network node structure, resource requirements and constraints, and can guide the actual operation and maintenance resource allocation work. In the future, the generated operation and maintenance resource configuration model can also be applied to the actual operation and maintenance work, and dynamically adjusted and optimized according to the actual situation. At the same time, establish a monitoring mechanism to monitor and evaluate the use of resources in real time to ensure the effective use of resources and the smooth progress of operation and maintenance work.

[0045] In an optional implementation, the determining the configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated may include: Step 1: converting the network nodes and operation and maintenance resources in the network node model of the operation and maintenance resources to be allocated into quantitative indicator parameters; Step 2: Based on the quantitative indicator parameters and with the optimal node performance as the orientation, combined with the constraint model of the operation and maintenance resources to be allocated, a configuration model of the operation and maintenance resources to be allocated is constructed.

[0046] First, it is necessary to quantify each network node and operation and maintenance resource in the network node model to be allocated operation and maintenance resources, and convert them into quantitative indicator parameters that can be calculated and compared. These parameters should be able to fully reflect the resource status, performance indicators and connection relationship between the nodes. For example, the resource status of the node can be quantified into indicators such as remaining capacity and availability, and the performance indicators can be quantified into response time, failure rate, etc.

[0047] When constructing the operation and maintenance resource configuration model, the optimal node performance is taken as the direction, so that the performance indicators of all nodes reach the optimal or near-optimal state. In order to achieve this goal, the resource requirements, performance indicators and mutual influence of the nodes can be comprehensively considered to ensure the rationality and effectiveness of resource allocation. In addition, the constraints in the constraint model of the operation and maintenance resources to be allocated need to be fully considered. These constraints include the maximum cost limit of each node, the maximum total cost limit, etc., which have an important impact on the selection of resource allocation schemes. Therefore, when constructing the model, these constraints need to be taken into consideration to ensure that the resulting resource allocation scheme meets both the requirements of optimal performance and the constraints.

[0048] Based on the above steps, we can start to build a model for the operation and maintenance resource configuration to be allocated. This model should be a complex system that comprehensively considers quantitative indicator parameters, node performance optimization orientation, and constraints. When building the model, various optimization algorithms and mathematical tools, such as linear programming, integer programming, dynamic programming, etc., can be used to solve the optimal or suboptimal resource allocation plan.

[0049] Finally, the constructed operation and maintenance resource configuration model needs to be verified and optimized. This includes checking whether the model meets all constraints, whether it can achieve the goal of optimal node performance, and whether it is feasible for practical application. If it is found that the model is insufficient or cannot fully meet the requirements, further optimization and adjustment are required to ensure the accuracy and practicality of the model.

[0050] The step of constructing a configuration model for operation and maintenance resources to be allocated based on the quantitative indicator parameters and taking the optimal node performance as a guide and combining the constraint model for operation and maintenance resources to be allocated may include: a. According to the quantitative indicator parameters, construct a mapping relationship between the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; b. According to the mapping relationship, obtain the resource configuration node parameters of the operation and maintenance resource configuration model to be allocated; c. Based on the resource configuration node parameters, with the node performance optimization as the goal, combined with the constraint model of the operation and maintenance resources to be allocated, a configuration model of the operation and maintenance resources to be allocated is generated.

[0051] In the process of building the allocation model of the operation and maintenance resources to be allocated, we need to establish a mapping relationship between the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated based on the quantitative indicator parameters, and obtain the resource configuration node parameters based on this mapping relationship, and finally generate the operation and maintenance resource allocation model in combination with the guidance and constraint conditions of the optimal node performance. The following is a detailed description of the steps: First, the mapping relationship between the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated can be constructed based on the quantitative indicator parameters. This mapping relationship should be able to accurately reflect the corresponding relationship between the resource status and performance indicators of each node in the network node model and the constraints in the constraint model. For example, the node resource status in the network node model, such as remaining capacity, availability and performance indicators, such as response time and failure rate, can be used as input to establish a mapping with the constraints such as cost constraints and resource utilization in the constraint model.

[0052] After the mapping relationship is established, the resource configuration node parameters of the operation and maintenance resource configuration model to be allocated can be obtained based on this relationship. These parameters should be able to fully reflect the resource requirements and performance indicators of each node, as well as their location and connection relationship in the network. Through the mapping relationship, the nodes in the network node model can be converted into resource configuration nodes in the resource configuration model, and the corresponding resource requirements and performance indicator parameters can be allocated to each node.

[0053] After obtaining the resource configuration node parameters, we can generate a resource configuration model for the operation and maintenance resources to be allocated based on the optimal node performance and the constraint model of the operation and maintenance resources to be allocated. This model should be able to comprehensively consider the resource requirements, performance indicators and constraints of the nodes to find an optimal or suboptimal resource allocation solution. To achieve this goal, various optimization algorithms and mathematical tools, such as linear programming, integer programming, heuristic algorithms, etc., can be used to solve the resource allocation problem.

[0054] In the process of generating the operation and maintenance resource configuration model, it is necessary to ensure that the resource allocation scheme obtained not only meets the requirements of optimal node performance, but also complies with the constraints. At the same time, the model needs to be verified and optimized to ensure its accuracy and practicality.

[0055] S204: According to the configuration model of the operation and maintenance resources to be allocated, an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix are obtained.

[0056] Specifically, in the process of optimizing the configuration of operation and maintenance resources according to the configuration model of the operation and maintenance resources to be allocated, an initial configuration scheme will be obtained first. The initial configuration scheme is calculated based on the quantitative indicator parameters in the model, the optimal orientation of node performance, and the constraints. However, due to the complexity and uncertainty in the actual operation and maintenance environment, this initial configuration scheme may deviate from the ideal state. In order to quantify this deviation, a resource configuration deviation matrix can be further generated.

[0057] First, mathematical optimization tools or algorithms, such as linear programming, integer programming, and heuristic algorithms, can be used to solve the allocation model of operation and maintenance resources. This solution process will comprehensively consider various factors in the model, including the resource requirements, performance indicators, cost constraints, etc. of the node, to find an optimal or suboptimal resource allocation plan. After the solution is completed, the model will output an initial configuration plan. This plan will list in detail the type and quantity of resources that should be allocated to each operation and maintenance node, and how these resources meet the performance requirements and constraints of the node.

[0058] Resource configuration deviation refers to the difference between the actual allocated resources and the ideal state (or model prediction). This difference may be caused by a variety of factors, such as insufficient resource supply, demand changes, environmental interference, etc. In order to quantify the deviation of resource configuration, a resource configuration deviation matrix can be constructed. The rows of the resource configuration deviation matrix represent the operation and maintenance nodes, and the columns represent the resource types, such as manpower, material resources, time, etc. Each element in the matrix represents the deviation value of the corresponding node and type of resource. The deviation value can be calculated by comparing the actual allocated resources with the ideal resources predicted by the model. For example, for a certain node and a certain resource type, the difference or percentage difference between the actual allocation amount and the model prediction amount can be calculated as the deviation value of the element. After the resource configuration deviation matrix is ​​generated, the deviation can be analyzed. This includes identifying nodes and resource types with large deviations, exploring the causes of the deviations, and proposing improvement measures to reduce the deviations. Based on the results of the deviation analysis, the initial configuration plan can be optimized and adjusted. This includes reallocating resources, adjusting constraints, or modifying model parameters to get closer to the ideal state.

[0059] In summary, by obtaining the initial configuration plan based on the allocation model of the operation and maintenance resources to be allocated and generating the resource configuration deviation matrix, a more comprehensive evaluation and adjustment of the optimal configuration of operation and maintenance resources can be carried out. This helps to ensure the effectiveness and accuracy of resource allocation and improve the efficiency and quality of operation and maintenance work.

[0060] In an optional implementation, obtaining an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix based on the configuration model of the operation and maintenance resources to be allocated may include: (1) Based on the allocation model of the operation and maintenance resources to be allocated, a nonlinear programming method is combined with a genetic algorithm to solve an initial configuration scheme for the optimal configuration of the operation and maintenance resources to be allocated under preset conditions; (2) Construct an operation and maintenance resource data prediction model based on CNN-LSTM; (3) The initial configuration plan is adjusted according to the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

[0061] Specifically, we can use the constructed allocation model of operation and maintenance resources to be allocated, combine nonlinear programming with genetic algorithm to solve the initial configuration plan for the optimal configuration of operation and maintenance resources to be allocated under preset conditions. The allocation model of operation and maintenance resources to be allocated comprehensively considers factors such as quantitative indicator parameters, optimal node performance orientation, and constraints.

[0062] In order to solve this complex optimization problem, we can use a method that combines nonlinear programming (NLP) and genetic algorithms (GA). Nonlinear programming can handle optimization problems that contain nonlinear constraints and objectives, while genetic algorithms are a global optimization search algorithm that can find approximate optimal solutions. By combining these two methods, we can solve the initial configuration plan for the optimal configuration of the operation and maintenance resources to be allocated under preset conditions.

[0063] In order to more accurately predict future O&M resource demand, an O&M resource data prediction model based on convolutional neural network (CNN) and long short-term memory network (LSTM) can be constructed. CNN can extract spatial features in data, while LSTM is good at processing long-term dependencies in time series data. By combining these two network structures, the model can more accurately predict the demand trend of O&M resources. The constructed O&M resource data prediction model is applied to multiple IT service support (ITSS) scenarios to obtain O&M resource demand forecasts in different scenarios. These prediction results will serve as the basis for adjusting the initial configuration plan.

[0064] Based on the prediction results of the operation and maintenance resource data prediction model, the initial configuration plan can be adjusted. This includes increasing or decreasing the resource allocation of certain nodes according to the predicted resource demand and adjusting the order and priority of resource allocation. After the adjusted configuration plan is implemented, the actual operation and maintenance resource usage data is collected and compared with the adjusted configuration plan. By calculating the difference between the actual usage data and the configuration plan, the resource configuration deviation matrix can be determined. The matrix will quantitatively display the deviation of each node and resource type, providing guidance for subsequent optimization work.

[0065] For example, based on the operation and maintenance resource configuration model to be allocated, the nonlinear programming method can be combined with the genetic algorithm to solve the initial configuration scheme under the preset conditions. Assuming that the operation and maintenance resource set is R and the operation and maintenance task set is T, the initial configuration scheme can be expressed as a mapping from resources to tasks: up , where u represents the resource index and p represents the task index. The objective function f(x) represents the cost or efficiency of resource allocation, and the constraint g k (x)≤0 and h l (x) = 0 represents the resource and task constraints respectively. Therefore, the solution of the initial configuration scheme can be expressed as the following nonlinear programming problem: ; ; ; ; Among them, the genetic algorithm is used to search for the optimal solution or the approximate optimal solution in the solution space.

[0066] Next, we construct an operation and maintenance resource data prediction model based on convolutional neural network (CNN) and long short-term memory network (LSTM). We assume that the general form of the operation and maintenance resource data prediction model based on convolutional neural network and long short-term memory network can be expressed as: ; in, Represents the mapping input variable function of the operation and maintenance resource data prediction model, Represents the trainable activation function of the operation and maintenance resource data prediction model.

[0067] Then, assuming that the time series of operation and maintenance resource data is {y t} , where t represents the time index. CNN is used to extract local features in the data, and LSTM is used to capture long-term dependencies in the time series. The prediction model can be expressed as the following function: ; in, Indicates the predicted value of operation and maintenance resource data at the next time point.

[0068] Finally, the initial configuration plan is adjusted based on the operation and maintenance resource data prediction model applied to multiple ITSS scenarios. Assume that the adjusted configuration plan is z up , the resource allocation deviation matrix can be expressed as D, where D up represents the configuration deviation of resource u on the task. The adjustment process can be expressed as the following optimization problem: ; ; ; ; ; Among them, w up represents the weight of resource u on task p, which is used to measure the importance of configuration deviation. The goal of the optimization problem is to minimize the weighted deviation between the adjusted configuration plan and the initial configuration plan. The resource configuration deviation matrix D can be calculated based on the adjusted configuration plan. and initial configuration scheme z up The calculations show that: ; In summary, by combining nonlinear programming, genetic algorithm, CNN-LSTM prediction model and operation and maintenance resource data of multiple ITSS scenarios, we can obtain a more accurate initial configuration plan and determine the resource configuration deviation matrix, thereby providing strong support for the optimal configuration of operation and maintenance resources.

[0069] S205: Obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

[0070] Specifically, obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix may include: According to the initial configuration plan and the resource configuration deviation matrix, an optimization target value in the operation and maintenance resource configuration plan set is obtained; according to the optimization target value, an improved ant colony algorithm is used to analyze the operation and maintenance resource configuration plan set to obtain a target configuration plan for the optimized configuration of the operation and maintenance resources to be allocated.

[0071] First, it is necessary to calculate the optimization target value of the operation and maintenance resource configuration plan set based on the initial configuration plan and the resource configuration deviation matrix. This optimization target value may cover multiple aspects, including but not limited to maximizing resource utilization, minimizing costs, improving service quality, and reducing resource configuration deviations.

[0072] Among them, in order to calculate the optimization target value, a weighted summation method can be used to assign a weight to each target and then calculate the weighted sum. The allocation of weights should be based on business needs and strategic priorities to ensure that the optimization plan can balance various goals. Next, the set of operation and maintenance resource configuration solutions is analyzed using the improved ant colony algorithm. The ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants. It finds the optimal solution by simulating the pheromone update and path selection mechanism of ants. In the improved ant colony algorithm, the following adjustments can be made to adapt to the characteristics of the operation and maintenance resource configuration problem: State representation: The resource allocation of each operation and maintenance node is regarded as an ant state and represented as a vector or matrix.

[0073] Transfer probability: Calculate the probability of an ant transferring from one node to another based on the current state and resource requirements. This probability can be adjusted based on factors such as resource utilization, cost, service quality, and resource allocation deviation.

[0074] Pheromone update: In each iteration, the pheromone is updated according to the optimization objective value. The pheromone update should encourage solutions that can improve the optimization objective value and suppress those solutions that perform poorly.

[0075] Local search: Based on the ant colony algorithm, a local search mechanism is introduced to accelerate the convergence speed and improve the quality of the solution.

[0076] Through multiple iterations and improved ant colony algorithms, one or more target configuration schemes can be found from the set of operation and maintenance resource configuration schemes. These schemes should be able to balance various optimization objectives, reduce resource configuration deviations, and improve resource utilization and service quality.

[0077] Finally, the target configuration plan is verified and adjusted. This includes testing the feasibility of the plan in the actual operation and maintenance environment, collecting feedback data, and making necessary adjustments based on the data. Through continuous iteration and optimization, an operation and maintenance resource configuration plan that better meets actual needs, is efficient, and is reliable can be obtained.

[0078] In summary, by determining the optimization target value, applying the improved ant colony algorithm, and performing verification and adjustment, the target configuration scheme for the optimal configuration of the operation and maintenance resources to be allocated can be derived from the initial configuration scheme and the resource configuration deviation matrix. This process requires comprehensive consideration of multiple factors, including resource utilization, cost, service quality, and resource configuration deviation, to ensure that the final solution can meet business needs and improve operation and maintenance efficiency.

[0079] Compared with the prior art, the present invention first establishes a network node model of operation and maintenance resources to be allocated; constructs a constraint model of operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated; determines the configuration model of operation and maintenance resources to be allocated based on the network node model of operation and maintenance resources to be allocated and the constraint model of operation and maintenance resources to be allocated; obtains the initial configuration scheme and resource configuration deviation matrix of the optimal configuration of operation and maintenance resources to be allocated based on the configuration model of operation and maintenance resources to be allocated; obtains the target configuration scheme of the optimal configuration of operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. It realizes the efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing the competitiveness of enterprises.

[0080] Another embodiment of the present application provides an ITSS operation and maintenance resource configuration optimization system, such as Figure 3 The structural diagram of an ITSS operation and maintenance resource configuration optimization system shown in FIG. 1 includes: Establishing module 301, used to establish a network node model of operation and maintenance resources to be allocated; A construction module 302 is used to construct a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; A determination module 303 is used to determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; An acquisition module 304 is used to obtain an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix according to the configuration model of the operation and maintenance resources to be allocated; The obtaining module 305 is used to obtain a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

[0081] Compared with the prior art, the present invention first establishes a network node model of operation and maintenance resources to be allocated; constructs a constraint model of operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated; determines the configuration model of operation and maintenance resources to be allocated based on the network node model of operation and maintenance resources to be allocated and the constraint model of operation and maintenance resources to be allocated; obtains the initial configuration scheme and resource configuration deviation matrix of the optimal configuration of operation and maintenance resources to be allocated based on the configuration model of operation and maintenance resources to be allocated; obtains the target configuration scheme of the optimal configuration of operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. It realizes the efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing the competitiveness of enterprises.

[0082] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to implement the steps in the above method embodiment when running.

[0083] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201: Establishing a network node model of operation and maintenance resources to be allocated; S202: constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; S203: Determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; S204: obtaining an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix according to the configuration model of the operation and maintenance resources to be allocated; S205: Obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

[0084] Specifically, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0085] Compared with the prior art, the present invention first establishes a network node model of operation and maintenance resources to be allocated; constructs a constraint model of operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated; determines the configuration model of operation and maintenance resources to be allocated based on the network node model of operation and maintenance resources to be allocated and the constraint model of operation and maintenance resources to be allocated; obtains the initial configuration scheme and resource configuration deviation matrix of the optimal configuration of operation and maintenance resources to be allocated based on the configuration model of operation and maintenance resources to be allocated; obtains the target configuration scheme of the optimal configuration of operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. It realizes the efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing the competitiveness of enterprises.

[0086] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in the above method embodiment.

[0087] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0088] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201: Establishing a network node model of operation and maintenance resources to be allocated; S202: constructing a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; S203: Determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; S204: obtaining an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix according to the configuration model of the operation and maintenance resources to be allocated; S205: Obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

[0089] Compared with the prior art, the present invention first establishes a network node model of operation and maintenance resources to be allocated; constructs a constraint model of operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated; determines the configuration model of operation and maintenance resources to be allocated based on the network node model of operation and maintenance resources to be allocated and the constraint model of operation and maintenance resources to be allocated; obtains the initial configuration scheme and resource configuration deviation matrix of the optimal configuration of operation and maintenance resources to be allocated based on the configuration model of operation and maintenance resources to be allocated; obtains the target configuration scheme of the optimal configuration of operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. It realizes the efficient, accurate and flexible configuration of operation and maintenance resources through a systematic and modeled operation and maintenance resource configuration optimization method, which has a significant effect on improving the quality of operation and maintenance services, reducing costs and enhancing the competitiveness of enterprises.

[0090] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, 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 required by the present invention.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided by the present invention, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0093] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the above-mentioned 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 invention, 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, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present invention. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0096] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for optimizing ITSS operation and maintenance resource configuration, characterized in that: The method comprises: Establish a network node model of operation and maintenance resources to be allocated; According to the constraints of the operation and maintenance resources to be allocated, a constraint model of the operation and maintenance resources to be allocated is constructed; Determining a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; According to the allocation model of the operation and maintenance resources to be allocated, an initial allocation scheme for optimizing the allocation of the operation and maintenance resources to be allocated and a resource allocation deviation matrix are obtained; According to the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated is obtained.

2. The method according to claim 1, characterized in that The step of establishing a network node model of operation and maintenance resources to be allocated includes: Determine the network nodes of the operation and maintenance resources to be allocated; An adjacency matrix and a characteristic matrix are established according to the connection relationship between the nodes of the operation and maintenance resource network to be allocated; wherein the characteristic data of the characteristic matrix include the node resource status and the node performance index; Based on the adjacency matrix and the characteristic matrix, a network node model of the operation and maintenance resources to be allocated is generated.

3. The method according to claim 2, characterized in that The step of constructing a constraint model of the operation and maintenance resources to be allocated based on the constraint conditions of the operation and maintenance resources to be allocated includes: Based on minimizing cost and maximizing operation and maintenance resource utilization as allocation goals, limiting the maximum cost of each node and the maximum total cost as constraints, using the multi-objective optimization function as a linear programming function, and through the mixed integer programming method and constraint satisfaction solving algorithm, the constraint model of the operation and maintenance resources to be allocated is generated.

4. The method according to claim 3, characterized in that The determining the configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated includes: Converting the network nodes and operation and maintenance resources in the network node model of the operation and maintenance resources to be allocated into quantitative indicator parameters; Based on the quantitative indicator parameters and with the optimization of node performance as the orientation, a configuration model of the operation and maintenance resources to be allocated is constructed in combination with the constraint model of the operation and maintenance resources to be allocated.

5. The method according to claim 4, characterized in that The method of constructing a configuration model of operation and maintenance resources to be allocated based on the quantitative indicator parameters and taking the optimal node performance as the orientation and combining the constraint model of operation and maintenance resources to be allocated includes: According to the quantitative indicator parameters, a mapping relationship between the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated is constructed; According to the mapping relationship, obtain resource configuration node parameters of the operation and maintenance resource configuration model to be allocated; According to the resource configuration node parameters, with the optimization of node performance as the goal, and in combination with the constraint model of the operation and maintenance resources to be allocated, a configuration model of the operation and maintenance resources to be allocated is generated.

6. The method according to claim 5, characterized in that The step of obtaining an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix based on the configuration model of the operation and maintenance resources to be allocated includes: Based on the allocation model of the operation and maintenance resources to be allocated, a nonlinear programming method is combined with a genetic algorithm to solve an initial configuration scheme for the optimal configuration of the operation and maintenance resources to be allocated under preset conditions; Construct an operation and maintenance resource data prediction model based on CNN-LSTM; The initial configuration plan is adjusted according to the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

7. The method according to any one of claims 1 to 6, characterized in that: The step of obtaining a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix includes: According to the initial configuration scheme and the resource configuration deviation matrix, an optimization target value in the operation and maintenance resource configuration scheme set is obtained; According to the optimization target value, the operation and maintenance resource configuration scheme set is analyzed using an improved ant colony algorithm to obtain a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated.

8. An ITSS operation and maintenance resource configuration optimization system, characterized in that: The system comprises: Establish a module for establishing a network node model of operation and maintenance resources to be allocated; A construction module, used to construct a constraint model of the operation and maintenance resources to be allocated according to the constraint conditions of the operation and maintenance resources to be allocated; A determination module, configured to determine a configuration model of the operation and maintenance resources to be allocated based on the network node model of the operation and maintenance resources to be allocated and the constraint model of the operation and maintenance resources to be allocated; An acquisition module, used to obtain an initial configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated and a resource configuration deviation matrix according to the configuration model of the operation and maintenance resources to be allocated; The module is used to obtain a target configuration scheme for optimizing the configuration of the operation and maintenance resources to be allocated according to the initial configuration scheme and the resource configuration deviation matrix.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to implement the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the computer program to implement the method according to any one of claims 1 to 7.

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