An ITSS operation and maintenance resource configuration optimization method and system

Abstract

This invention discloses an ITSS (Information Technology Service Provider) operation and maintenance resource configuration optimization method and system. The method includes: firstly, establishing a network node model of the operation and maintenance resources to be allocated; secondly, constructing a constraint model of the operation and maintenance resources to be allocated based on the constraints of the operation and maintenance resources to be allocated; thirdly, determining a configuration model of the operation and maintenance resources to be allocated based on the network node model and the constraint model; fourthly, obtaining an initial configuration scheme and a resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model; and fifthly, obtaining a target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix. Through a systematic and model-based operation and maintenance resource configuration optimization method, it achieves efficient, accurate, and flexible configuration of operation and maintenance resources, which has significant effects on improving the quality of operation and maintenance services, reducing costs, and enhancing enterprise competitiveness.

Description

Technical Field

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

[0002] With the rapid development of information technology, IT Service Management (ITSM) plays a crucial role in enterprise operations. IT Service Support (ITSS), as an important component of ITSM, directly impacts the stability and efficiency of enterprise services through the effective allocation and optimization of its operational resources. Traditional operational resource allocation methods often rely on manual experience or simple static allocation strategies, making it difficult to adapt to complex and ever-changing business needs and service environments.

[0003] In practical applications, operational resources encompass multiple aspects, including hardware equipment, software tools, and human resources. The allocation of these resources is subject to various constraints, such as cost budgets, resource availability, and Service Level Agreement (SLA) requirements. Therefore, achieving the scientific allocation and dynamic optimization of operational resources under limited resource conditions has become a key issue in improving IT service quality and operational efficiency.

[0004] Traditional resource allocation methods often suffer from low configuration efficiency, uneven resource utilization, and difficulty in meeting immediate service demands when facing large-scale, highly complex operational environments. Especially with the advancement of emerging technologies such as cloud computing and big data, IT service environments are becoming increasingly complex, and the configuration requirements for operational resources are becoming more diverse and dynamic. This places higher demands on the intelligence and automation of operational resource allocation, making it a pressing technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide an ITSS operation and maintenance resource configuration optimization method and system to address the shortcomings of existing technologies. Through a systematic and model-based operation and maintenance resource configuration optimization method, it achieves efficient, accurate, and flexible configuration of operation and maintenance resources, which has significant effects on improving the quality of operation and maintenance services, reducing costs, and enhancing enterprise competitiveness.

[0006] One embodiment of this application provides an ITSS operation and maintenance resource configuration optimization method, the method comprising:

[0007] Establish a network node model for the operation and maintenance resources to be allocated;

[0008] Based on the constraints of the operation and maintenance resources to be allocated, construct a constraint model for the operation and maintenance resources to be allocated;

[0009] 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, the configuration model of the operation and maintenance resources to be allocated is determined.

[0010] Based on the configuration model of the operation and maintenance resources to be allocated, the initial configuration scheme and resource configuration deviation matrix of the optimized configuration of the operation and maintenance resources to be allocated are obtained;

[0011] Based on the initial configuration scheme and the resource configuration deviation matrix, the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated is obtained.

[0012] Optionally, establishing the network node model for the operation and maintenance resources to be allocated includes:

[0013] Identify the network nodes for which maintenance resources are to be allocated;

[0014] An adjacency matrix and a feature matrix are established based on the connection relationships between the nodes of the network to be allocated operation and maintenance resources; wherein, the feature data of the feature matrix includes node resource status and node performance indicators;

[0015] Based on the adjacency matrix and feature matrix, a network node model for the operation and maintenance resources to be allocated is generated.

[0016] Optionally, the step of constructing a constraint model for the operation and maintenance resources to be allocated based on the constraints of the resources to be allocated includes:

[0017] Based on the allocation objectives of minimizing cost and maximizing the utilization rate of operation and maintenance resources, and with the constraints of limiting the maximum cost of each node and the maximum total cost, a constraint model of operation and maintenance resources to be allocated is generated by using a multi-objective optimization function as a linear programming function and a mixed integer programming method and constraint satisfaction solution algorithm.

[0018] Optionally, 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:

[0019] The network nodes and maintenance resources in the network node model of the unallocated maintenance resources are converted into quantitative indicator parameters.

[0020] Based on the aforementioned quantitative index parameters, and with the goal of optimizing node performance, a configuration model for the allocated operation and maintenance resources is constructed in conjunction with the constraint model for the operation and maintenance resources to be allocated.

[0021] Optionally, the step of constructing a configuration model for the allocated operation and maintenance resources based on the quantitative index parameters and with the goal of optimizing node performance, combined with the constraint model of the operation and maintenance resources to be allocated, includes:

[0022] Based on the quantitative index parameters, a mapping relationship is constructed 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.

[0023] Based on the mapping relationship, obtain the resource configuration node parameters of the operation and maintenance resource configuration model to be allocated;

[0024] Based on the resource configuration node parameters, with optimal node performance as the goal, and in conjunction with the resource allocation constraint model, a resource allocation model is generated.

[0025] Optionally, obtaining the initial configuration scheme and resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model of the operation and maintenance resources to be allocated includes:

[0026] Based on the configuration model of the operation and maintenance resources to be allocated, the nonlinear programming method and the genetic algorithm are combined to solve the initial configuration scheme of the optimal configuration of the operation and maintenance resources to be allocated under the preset conditions.

[0027] Construct a prediction model for operation and maintenance resource data based on CNN-LSTM;

[0028] The initial configuration scheme is adjusted based on the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

[0029] Optionally, the step of obtaining the target configuration scheme for optimizing the allocation of operation and maintenance resources based on the initial configuration scheme and the resource configuration deviation matrix includes:

[0030] Based on the initial configuration scheme and the resource configuration deviation matrix, the optimization target value of the operation and maintenance resource configuration scheme set is obtained;

[0031] Based on the optimization target value, the improved ant colony algorithm is used to analyze the set of operation and maintenance resource configuration schemes to obtain the target configuration scheme for the optimized allocation of operation and maintenance resources to be allocated.

[0032] Another embodiment of this application provides an ITSS operation and maintenance resource configuration optimization system, the system comprising:

[0033] The module is used to establish a network node model for the operation and maintenance resources to be allocated;

[0034] The construction module is used to build a constraint model for the operation and maintenance resources to be allocated based on the constraints of the operation and maintenance resources to be allocated;

[0035] The determination module is used to determine 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;

[0036] The acquisition module is used to obtain the initial configuration scheme and resource configuration deviation matrix of the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model of the operation and maintenance resources to be allocated;

[0037] The module is used to obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated, based on the initial configuration scheme and the resource configuration deviation matrix.

[0038] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to implement the method described in any of the above-described embodiments when running.

[0039] Another embodiment of this 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 the method described in any of the above embodiments.

[0040] Compared with existing technologies, this invention first establishes a network node model of the operational resources to be allocated; based on the constraints of the operational resources to be allocated, a constraint model of the operational resources to be allocated is constructed; based on the network node model and the constraint model of the operational resources to be allocated, a configuration model of the operational resources to be allocated is determined; based on the configuration model of the operational resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for optimized configuration of the operational resources to be allocated are obtained; and based on the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimized configuration of the operational resources to be allocated is obtained. Through a systematic and model-based method for optimizing operational resource configuration, it achieves efficient, accurate, and flexible configuration of operational resources, which has significant effects on improving the quality of operational services, reducing costs, and enhancing enterprise competitiveness. Attached Figure Description

[0041] Figure 1 A hardware structure block diagram of a computer terminal for an ITSS operation and maintenance resource configuration optimization method provided in an embodiment of the present invention;

[0042] Figure 2 A flowchart illustrating an ITSS operation and maintenance resource configuration optimization method provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of an ITSS operation and maintenance resource configuration optimization system provided in an embodiment of the present invention. Detailed Implementation

[0044] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0045] This invention first 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.

[0046] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware structure block diagram of a computer terminal for an ITSS operation and maintenance resource configuration optimization method provided in an embodiment of the present invention. Figure 1 As shown, a computer terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the computer terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, the computer terminal may also include components that are more complex than those described above. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0047] 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 this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include 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 memory remotely located relative to the processor 102, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0048] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0049] ITSS (Information Technology Service Standards) is a systematic and comprehensive library of information technology service standards. It fully regulates information technology service products and their components, guides the implementation of standardized information technology services, summarizes and improves best practices in the information technology service industry, and solidifies the independent innovation achievements of various organizations engaged in information technology service research and development, supply, promotion and application.

[0050] ITSS-based operation and maintenance resource allocation is a crucial step in ensuring the efficient and stable operation of information technology services. Enterprises need to rationally allocate operation and maintenance personnel, technology, and resources based on business and technical requirements, and improve service efficiency and quality through process optimization and continuous monitoring and optimization. This will help enhance enterprise competitiveness and achieve sustainable business development.

[0051] See Figure 2 , Figure 2 A flowchart illustrating an ITSS operation and maintenance resource configuration optimization method provided in this embodiment of the invention may include the following steps:

[0052] S201: Establish a network node model for the operation and maintenance resources to be allocated.

[0053] Specifically, establishing the network node model for the operation and maintenance resources to be allocated may include:

[0054] 1. Identify the network nodes for which maintenance resources need to be allocated;

[0055] 2. Establish an adjacency matrix and a feature matrix based on the connection relationships between the nodes of the network to be allocated operation and maintenance resources; wherein, the feature data of the feature matrix includes node resource status and node performance indicators;

[0056] 3. Based on the adjacency matrix and feature matrix, generate a network node model for the operation and maintenance resources to be allocated.

[0057] First, identify the network nodes for the operational resources to be allocated. This requires clarifying which resources or entities will be considered nodes in the network. These nodes may represent different operational teams, devices, servers, or other resources that need to be managed and maintained. The process of identifying nodes is based on the actual needs and distribution of operational resources, ensuring that all critical resources are included in the model.

[0058] An adjacency matrix is ​​a matrix that represents the connections between nodes in a graph. In an operational resource network, each row and column of the adjacency matrix corresponds to a node, and the elements in the matrix represent the connections between nodes. If there is a direct connection between two nodes, such as a collaborative relationship between two teams or a physical connection between two devices, the corresponding element will have a value of 1 or some non-zero value; otherwise, it will have a value of 0.

[0059] The feature matrix is ​​used to describe the specific attributes and status of each node. In an operational resource network, the feature matrix can contain various feature data, including but not limited to:

[0060] Node resource status, such as the node's current load, remaining capacity, and availability, reflects the node's resource usage.

[0061] Node performance metrics, such as response time, failure rate, and stability, reflect the node's operational efficiency and reliability.

[0062] Each row of the feature matrix corresponds to a node, and each column corresponds to a feature data point. The feature matrix provides a more comprehensive understanding of the state and capabilities of each node.

[0063] Finally, after establishing the adjacency matrix and feature matrix, these matrix data can be used to generate a network node model for the resources to be allocated in operations and maintenance. This model is a complex network structure integrating node connectivity and node characteristics. In practical applications, this model can be used for various operations and maintenance resource management tasks, such as resource allocation, load balancing, and fault prediction. By analyzing the node connectivity and feature data in the model, more effective operations and maintenance strategies can be formulated, improving operational efficiency and resource utilization.

[0064] S202: Based on the constraints of the operation and maintenance resources to be allocated, construct the constraint model of the operation and maintenance resources to be allocated.

[0065] Specifically, the construction of the constraint model for the operation and maintenance resources to be allocated, based on the constraints of the resources to be allocated, may include:

[0066] Based on the allocation objectives of minimizing cost and maximizing the utilization rate of operation and maintenance resources, and with the constraints of limiting the maximum cost of each node and the maximum total cost, a constraint model of operation and maintenance resources to be allocated is generated by using a multi-objective optimization function as a linear programming function and a mixed integer programming method and constraint satisfaction solution algorithm.

[0067] In order to achieve the goals of minimizing costs and maximizing the utilization of operation and maintenance resources, and considering the maximum cost of each node and the total cost limit as constraints, a multi-objective optimization function can be used for linear programming. A mixed integer programming method and a constraint satisfaction solution algorithm can be adopted to generate a constraint model for the operation and maintenance resources to be allocated.

[0068] For example, minimizing the total cost can be expressed by the following formula:

[0069] ;

[0070] Where N represents the total number of operation and maintenance resources, M represents the total number of nodes, and c ij x represents the cost of allocating maintenance resource i to node j. ij Indicates whether maintenance resource i is allocated to node j, x ij It is a binary variable, such as 1 indicating allocation and 0 indicating no allocation.

[0071] Maximizing the utilization rate of operation and maintenance resources can be expressed by the following formula:

[0072] ;

[0073] Among them, u ij This represents the utilization rate of maintenance resource i allocated to node j.

[0074] By limiting the maximum cost of each node and the maximum total cost as constraints, and using a multi-objective optimization function as a linear programming function, the constraint model of the operation and maintenance resources to be allocated is generated through mixed integer programming methods and constraint satisfaction solving algorithms. This model is considered as a multi-objective optimization problem. For example, it can be transformed into a single-objective optimization problem through transformation methods such as weighted sum method and ε-constraint method.

[0075] For example, using the weighted sum method, the maximum cost of each node and the maximum total cost are constrained, and the two objective functions are weighted and summed, i.e.:

[0076] ;

[0077] Among them, u demand,j This represents the total demand for operation and maintenance resources for node j, and β represents the weighting parameter used to balance cost and resource utilization.

[0078] The cost constraints for each node satisfy the following: This means that the cost of each node cannot exceed its budget, C. max,j Let represent the maximum cost limit for node j. The total cost limit satisfies: C total,max This represents the maximum limit on the total cost. The binary variable constraint satisfies: x ij∈{0,1}, ∀i∈{1,...,N}, ∀j∈{,...,M}; the resource constraints of each node satisfy: R i Wherein represents the total amount of the i-th type of resource.

[0079] S203: 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, determine the configuration model of the operation and maintenance resources to be allocated.

[0080] Specifically, when determining the configuration model for the operation and maintenance resources to be allocated, the previously constructed network node model and constraint model for the operation and maintenance resources to be allocated can be considered. These two models provide the structural information of the network nodes and the constraints for resource allocation, respectively.

[0081] First, the nodes, connections, and feature data in the network node model of the resources to be allocated are integrated into a unified framework. This includes the resource status, performance metrics, and connections between nodes. Then, the constraints in the resource constraint model, such as maximum cost limits for each node and maximum total cost limits, as well as objective functions, such as minimizing cost and maximizing resource utilization, are also integrated into this framework. Based on the integrated model, a resource allocation strategy is formulated. This may include determining how much resource should be allocated to each node, the type of resource, and the allocation order.

[0082] The initially developed operation and maintenance resource allocation model is validated to ensure it meets all constraints and optimizes the objective function. After validation and optimization, the final operation and maintenance resource allocation model is generated. This model should be a complete solution integrating network node structure, resource requirements, and constraints, guiding actual operation and maintenance resource allocation. The generated operation and maintenance resource allocation model can then be applied to actual operation and maintenance work, dynamically adjusted and optimized according to actual conditions. Simultaneously, a monitoring mechanism is established to monitor and evaluate resource usage in real time, ensuring effective resource utilization and smooth operation and maintenance.

[0083] In an optional implementation, 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:

[0084] Step 1: Convert the network nodes and maintenance resources in the network node model of the operation and maintenance resources to be allocated into quantitative indicator parameters;

[0085] Step 2: Based on the quantitative index parameters and with the goal of optimizing node performance, construct the configuration model for the operation and maintenance resources to be allocated, combined with the constraint model of the operation and maintenance resources to be allocated.

[0086] First, it is necessary to quantify each network node and operational resource in the network node model to be allocated operational resources, transforming them into quantifiable and comparable metrics. These parameters should comprehensively reflect the resource status, performance indicators, and connectivity relationships between nodes. For example, the resource status of a node can be quantified into metrics such as remaining capacity and availability, while performance indicators can be quantified into metrics such as response time and failure rate.

[0087] When constructing the operation and maintenance resource allocation model, the goal is to optimize node performance, ensuring that the performance indicators of all nodes reach or approach optimal levels. To achieve this, the resource requirements, performance indicators, and interrelationships of nodes are comprehensively considered to ensure the rationality and effectiveness of resource allocation. Furthermore, the constraints in the resource allocation model must be fully considered. These constraints include maximum cost limits for each node and maximum total cost limits, which significantly influence the selection of the resource allocation scheme. Therefore, these constraints must be taken into account when constructing the model to ensure that the resulting resource allocation scheme satisfies both performance optimization requirements and the constraints.

[0088] Based on the above steps, we can begin constructing the resource allocation model for the system to be allocated. This model should be a complex system that comprehensively considers quantitative indicator parameters, optimal node performance, and constraints. When constructing the model, various optimization algorithms and mathematical tools, such as linear programming, integer programming, and dynamic programming, can be used to solve for the optimal or near-optimal resource allocation scheme.

[0089] Finally, the constructed operation and maintenance resource configuration model needs to be validated 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 the model is found to be inadequate or cannot fully meet the requirements, further optimization and adjustments are needed to ensure the model's accuracy and practicality.

[0090] The step of constructing a configuration model for operation and maintenance resources based on the quantitative index parameters, with the goal of optimizing node performance, and in conjunction with the constraint model for the operation and maintenance resources to be allocated, may include:

[0091] a. Based on the quantitative index parameters, construct 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;

[0092] b. Based on the mapping relationship, obtain the resource configuration node parameters of the operation and maintenance resource configuration model to be allocated;

[0093] c. Based on the resource configuration node parameters, with optimal node performance as the goal, and in conjunction with the resource allocation constraint model, generate the resource allocation model to be assigned.

[0094] In constructing the configuration model for the operational resources to be allocated, we need to establish a mapping relationship between the network node model and the constraint model of the operational resources to be allocated based on quantitative indicator parameters. We then obtain the resource configuration node parameters based on this mapping relationship, and finally generate the operational resource configuration model by combining the guidance and constraints for optimal node performance. The following are detailed steps:

[0095] First, a mapping relationship can be constructed between the network node model and the constraint model of the resources to be allocated, based on quantified indicator parameters. This mapping relationship should accurately reflect the correspondence 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 and availability, and performance indicators, such as response time and failure rate, can be used as inputs to establish a mapping with constraints such as cost limits and resource utilization in the constraint model.

[0096] After establishing the mapping relationship, 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 comprehensively reflect the resource requirements and performance indicators of each node, as well as their location and connection relationships in the network. Through the mapping relationship, nodes in the network node model can be transformed into resource configuration nodes in the resource configuration model, and corresponding resource requirements and performance indicator parameters can be assigned to each node.

[0097] After obtaining the node parameters for resource allocation, a resource allocation model can be generated, aiming for optimal node performance and combining it with the constraint model of the resources to be allocated. This model should be able to comprehensively consider the node's resource requirements, performance indicators, and constraints to find an optimal or near-optimal resource allocation scheme. To achieve this goal, various optimization algorithms and mathematical tools, such as linear programming, integer programming, and heuristic algorithms, can be used to solve the resource allocation problem.

[0098] In generating the operation and maintenance resource configuration model, it is necessary to ensure that the obtained resource allocation scheme not only meets the requirements of optimal node performance but also complies with the constraints. Simultaneously, the model needs to be validated and optimized to ensure its accuracy and practicality.

[0099] S204: Based on the configuration model of the operation and maintenance resources to be allocated, obtain the initial configuration scheme of the optimized configuration of the operation and maintenance resources to be allocated and the resource configuration deviation matrix.

[0100] Specifically, in the process of optimizing the allocation of operation and maintenance resources based on the resource allocation model, an initial configuration scheme is first obtained. This initial configuration scheme is calculated based on factors such as quantitative indicator parameters in the model, optimal node performance guidance, and constraints. However, due to the complexity and uncertainty of the actual operation and maintenance environment, this initial configuration scheme may deviate from the ideal state. To quantify this deviation, a resource allocation deviation matrix can be further generated.

[0101] First, mathematical optimization tools or algorithms, such as linear programming, integer programming, and heuristic algorithms, can be used to solve the resource allocation model. This solution process comprehensively considers various factors in the model, including node resource requirements, performance metrics, and cost constraints, to find an optimal or near-optimal resource allocation scheme. After the solution is completed, the model will output an initial configuration scheme. This scheme will detail the types and quantities of resources that should be allocated to each operational node, and how these resources meet the node's performance requirements and constraints.

[0102] Resource allocation deviation refers to the difference between the actual allocated resources and the ideal state (or model prediction). This difference can be caused by various factors, such as insufficient resource supply, changes in demand, and environmental disturbances. To quantify resource allocation deviation, a resource allocation deviation matrix can be constructed. The rows of the matrix represent operational nodes, and the columns represent resource types, such as manpower, materials, and time. Each element in the matrix represents the deviation value for the corresponding node and resource type. 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 resource type, the difference or percentage difference between the actual allocated amount and the model prediction can be calculated as the deviation value for that element. After generating the resource allocation deviation matrix, 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 them. Based on the results of the deviation analysis, the initial configuration scheme can be optimized and adjusted. This includes reallocating resources, adjusting constraints, or modifying model parameters to more closely approximate the ideal state.

[0103] In summary, by obtaining an initial configuration scheme based on the resource allocation model and generating a resource configuration deviation matrix, a more comprehensive evaluation and adjustment of the optimized allocation of operational resources can be achieved. This helps ensure the effectiveness and accuracy of resource allocation and improves the efficiency and quality of operational work.

[0104] In one optional implementation, obtaining the initial configuration scheme and resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model of the operation and maintenance resources to be allocated may include:

[0105] (1) Based on the configuration model of the operation and maintenance resources to be allocated, the nonlinear programming method and the genetic algorithm are combined to solve the initial configuration scheme of the optimal configuration of the operation and maintenance resources to be allocated under the preset conditions;

[0106] (2) Construct a CNN-LSTM-based model for predicting operation and maintenance resource data;

[0107] (3) Adjust the initial configuration scheme according to the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

[0108] Specifically, the existing configuration model for the operation and maintenance resources to be allocated can be used to combine nonlinear programming and genetic algorithms to solve the initial configuration scheme for the optimal allocation of the operation and maintenance resources under preset conditions. The configuration model for the operation and maintenance resources to be allocated comprehensively considers factors such as quantitative index parameters, optimal node performance guidance, and constraints.

[0109] To solve this complex optimization problem, a combination of nonlinear programming (NLP) and genetic algorithms (GA) can be used. Nonlinear programming can handle optimization problems with nonlinear constraints and objectives, while genetic algorithms are global optimization search algorithms capable of finding approximate optimal solutions. By combining these two methods, we can find the initial configuration scheme for the optimal allocation of operational resources under preset conditions.

[0110] To more accurately predict future operational resource demands, an operational resource data prediction model based on Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) can be constructed. CNNs are adept at extracting spatial features from data, while LSTMs excel at handling long-term dependencies in time-series data. By combining these two network structures, the model can more accurately predict operational resource demand trends. The constructed operational resource data prediction model will be applied to multiple IT Service Support (ITSS) scenarios to obtain operational resource demand predictions under different scenarios. These prediction results will serve as the basis for adjusting the initial configuration plan.

[0111] Based on the prediction results of the operation and maintenance resource data prediction model, the initial configuration scheme can be adjusted. This includes increasing or decreasing the resource allocation of certain nodes according to the predicted resource requirements, as well as adjusting the order and priority of resource allocation. After the adjusted configuration scheme is implemented, actual operation and maintenance resource usage data is collected and compared with the adjusted configuration scheme. By calculating the difference between the actual usage data and the configuration scheme, a resource configuration deviation matrix can be determined. This matrix quantitatively displays the deviation of each node and resource type, providing guidance for subsequent optimization work.

[0112] For example, based on the operational resource allocation model to be allocated, nonlinear programming and genetic algorithms can be combined to solve the initial allocation scheme under preset conditions. Assuming the set of operational resources is R and the set of operational tasks is T, the initial allocation scheme can be represented as the mapping z 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 constraints of resources and tasks, respectively. Therefore, solving the initial configuration scheme can be expressed as the following nonlinear programming problem:

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] Among them, the genetic algorithm is used to search for the optimal solution or a near-optimal solution in the solution space.

[0118] Next, we construct an operation and maintenance resource data prediction model based on convolutional neural networks (CNN) and long short-term memory networks (LSTM). We assume that the general form of this model can be expressed as:

[0119] ;

[0120] in, The function representing the mapping input variables of the operation and maintenance resource data prediction model. This represents the activation function that the operation and maintenance resource data prediction model can train.

[0121] Then, assume 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 from the data, while LSTM is used to capture long-term dependencies in the time series. The prediction model can be represented as the following function:

[0122] ;

[0123] in, This represents a predicted value for the operation and maintenance resource data at the next point in time.

[0124] Finally, the initial configuration scheme was adjusted based on the operation and maintenance resource data prediction model applied to multiple ITSS scenarios. Let's assume the adjusted configuration scheme is z.up The resource allocation deviation matrix can be represented as D, where D up This represents the configuration deviation of resource u on the task. The adjustment process can be represented as the following optimization problem:

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] Among them, w up The resource configuration deviation matrix D represents the weight of resource u on task p, used to measure the importance of configuration deviations. The goal of the optimization problem is to minimize the weighted deviation between the adjusted configuration and the initial configuration. The resource configuration deviation matrix D can be determined based on the adjusted configuration. and initial configuration scheme z up The calculation shows that:

[0131] ;

[0132] In summary, by combining nonlinear programming, genetic algorithms, CNN-LSTM prediction models, and operational resource data from multiple ITSS scenarios, we can obtain a more accurate initial configuration scheme and determine the resource configuration deviation matrix, thereby providing strong support for the optimized configuration of operational resources.

[0133] S205: Based on the initial configuration scheme and the resource configuration deviation matrix, obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated.

[0134] Specifically, the step of obtaining the target configuration scheme for optimizing the allocation of operation and maintenance resources based on the initial configuration scheme and the resource configuration deviation matrix may include:

[0135] Based on the initial configuration scheme and the resource configuration deviation matrix, the optimization target value of the operation and maintenance resource configuration scheme set is obtained; based on the optimization target value, the improved ant colony algorithm is used to analyze the operation and maintenance resource configuration scheme set to obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated.

[0136] First, based on the initial configuration scheme and the resource configuration deviation matrix, it is necessary to calculate the optimization target value of the operation and maintenance resource configuration scheme set. 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 deviation.

[0137] To calculate the optimization target value, a weighted summation method can be used, assigning a weight to each target and then calculating the weighted sum. The weight allocation should be based on business needs and strategic priorities to ensure that the optimization scheme balances the various targets. Next, an improved ant colony algorithm is used to analyze the set of operational resource allocation schemes. 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 mechanisms of ants. In the improved ant colony algorithm, the following adjustments can be made to adapt to the characteristics of the operational resource allocation problem:

[0138] State representation: The resource allocation status of each operation and maintenance node is regarded as a state of the ant, and represented as a vector or matrix.

[0139] Transfer probability: Calculate the probability that an ant will move 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 deviations.

[0140] Pheromone Update: In each iteration, the pheromone is updated based on the optimization objective value. The pheromone update should encourage solutions that improve the optimization objective value while suppressing poorly performing solutions.

[0141] 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.

[0142] Through multiple iterations and improvements, the ant colony algorithm can identify one or more target configuration schemes from a set of operational resource configuration schemes. These schemes should be able to balance various optimization objectives while reducing resource configuration bias and improving resource utilization and service quality.

[0143] Finally, the target configuration scheme is verified and adjusted. This includes testing the feasibility of the scheme in a real-world operational environment, collecting feedback data, and making necessary adjustments based on the data. Through continuous iteration and optimization, a more efficient and reliable operational resource configuration scheme that better meets actual needs can be obtained.

[0144] In summary, by determining the optimization target value, applying the improved ant colony algorithm, and conducting verification and adjustments, the target configuration scheme for optimizing the allocation of operational resources 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 scheme meets business needs and improves operational efficiency.

[0145] Compared with existing technologies, this invention first establishes a network node model of the operational resources to be allocated; based on the constraints of the operational resources to be allocated, a constraint model of the operational resources to be allocated is constructed; based on the network node model and the constraint model of the operational resources to be allocated, a configuration model of the operational resources to be allocated is determined; based on the configuration model of the operational resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for optimized configuration of the operational resources to be allocated are obtained; and based on the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimized configuration of the operational resources to be allocated is obtained. Through a systematic and model-based method for optimizing operational resource configuration, it achieves efficient, accurate, and flexible configuration of operational resources, which has significant effects on improving the quality of operational services, reducing costs, and enhancing enterprise competitiveness.

[0146] Another embodiment of this application provides an ITSS operation and maintenance resource configuration optimization system, such as... Figure 3 The diagram shown illustrates the structure of an ITSS operations and maintenance resource configuration optimization system. The system includes:

[0147] Establish module 301 to establish a network node model for the operation and maintenance resources to be allocated;

[0148] Module 302 is used to construct a constraint model for the operation and maintenance resources to be allocated based on the constraints of the operation and maintenance resources to be allocated.

[0149] The determination module 303 is used to determine 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;

[0150] The module 304 is used to obtain the initial configuration scheme and resource configuration deviation matrix of the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model of the operation and maintenance resources to be allocated.

[0151] The module 305 is used to obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated based on the initial configuration scheme and the resource configuration deviation matrix.

[0152] Compared with existing technologies, this invention first establishes a network node model of the operational resources to be allocated; based on the constraints of the operational resources to be allocated, a constraint model of the operational resources to be allocated is constructed; based on the network node model and the constraint model of the operational resources to be allocated, a configuration model of the operational resources to be allocated is determined; based on the configuration model of the operational resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for optimized configuration of the operational resources to be allocated are obtained; and based on the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimized configuration of the operational resources to be allocated is obtained. Through a systematic and model-based method for optimizing operational resource configuration, it achieves efficient, accurate, and flexible configuration of operational resources, which has significant effects on improving the quality of operational services, reducing costs, and enhancing enterprise competitiveness.

[0153] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to implement the steps in the above method embodiments when running.

[0154] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0155] S201: Establish a network node model for the operation and maintenance resources to be allocated;

[0156] S202: Based on the constraints of the operation and maintenance resources to be allocated, construct the constraint model of the operation and maintenance resources to be allocated;

[0157] S203: 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, determine the configuration model of the operation and maintenance resources to be allocated;

[0158] S204: Based on the configuration model of the operation and maintenance resources to be allocated, obtain the initial configuration scheme and resource configuration deviation matrix of the optimized configuration of the operation and maintenance resources to be allocated;

[0159] S205: Based on the initial configuration scheme and the resource configuration deviation matrix, obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated.

[0160] Specifically, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0161] Compared with existing technologies, this invention first establishes a network node model of the operational resources to be allocated; based on the constraints of the operational resources to be allocated, a constraint model of the operational resources to be allocated is constructed; based on the network node model and the constraint model of the operational resources to be allocated, a configuration model of the operational resources to be allocated is determined; based on the configuration model of the operational resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for optimized configuration of the operational resources to be allocated are obtained; and based on the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimized configuration of the operational resources to be allocated is obtained. Through a systematic and model-based method for optimizing operational resource configuration, it achieves efficient, accurate, and flexible configuration of operational resources, which has significant effects on improving the quality of operational services, reducing costs, and enhancing enterprise competitiveness.

[0162] This invention also 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 perform the steps described in the method embodiments above.

[0163] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0164] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0165] S201: Establish a network node model for the operation and maintenance resources to be allocated;

[0166] S202: Based on the constraints of the operation and maintenance resources to be allocated, construct the constraint model of the operation and maintenance resources to be allocated;

[0167] S203: 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, determine the configuration model of the operation and maintenance resources to be allocated;

[0168] S204: Based on the configuration model of the operation and maintenance resources to be allocated, obtain the initial configuration scheme and resource configuration deviation matrix of the optimized configuration of the operation and maintenance resources to be allocated;

[0169] S205: Based on the initial configuration scheme and the resource configuration deviation matrix, obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated.

[0170] Compared with existing technologies, this invention first establishes a network node model of the operational resources to be allocated; based on the constraints of the operational resources to be allocated, a constraint model of the operational resources to be allocated is constructed; based on the network node model and the constraint model of the operational resources to be allocated, a configuration model of the operational resources to be allocated is determined; based on the configuration model of the operational resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for optimized configuration of the operational resources to be allocated are obtained; and based on the initial configuration scheme and the resource configuration deviation matrix, a target configuration scheme for optimized configuration of the operational resources to be allocated is obtained. Through a systematic and model-based method for optimizing operational resource configuration, it achieves efficient, accurate, and flexible configuration of operational resources, which has significant effects on improving the quality of operational services, reducing costs, and enhancing enterprise competitiveness.

[0171] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0172] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0173] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0174] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0177] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing ITSS operation and maintenance resource configuration, characterized in that, The method includes: Establish a network node model for the operational resources to be allocated; wherein, the network nodes for the operational resources to be allocated represent different operational teams, equipment, servers, or other resources that need to be managed and maintained; wherein, establishing the network node model for the operational resources to be allocated includes: determining the network nodes for the operational resources to be allocated; establishing an adjacency matrix and a feature matrix based on the connection relationships between the nodes of the network for the operational resources to be allocated; wherein, the feature data of the feature matrix includes node resource status and node performance indicators, wherein the node resource status includes the node's current load, remaining capacity, or availability, and the node performance indicators include response time, failure rate, or stability; and generating the network node model for the operational resources to be allocated based on the adjacency matrix and the feature matrix. Based on the constraints of the operation and maintenance resources to be allocated, a constraint model for the operation and maintenance resources to be allocated is constructed. Among them, based on the allocation objectives of minimizing cost and maximizing the utilization rate of operation and maintenance resources, a weighted sum method is used with the maximum cost of each node and the maximum total cost as constraints. A multi-objective optimization function is used as the linear programming function. Through mixed integer programming method and constraint satisfaction solution algorithm, the constraint model for the operation and maintenance resources to be allocated is generated. 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, a configuration model for the operation and maintenance resources to be allocated is determined; wherein, the step of determining the configuration model for 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 goal, and in conjunction with the constraint model of the operation and maintenance resources to be allocated, constructing a configuration model for the operation and maintenance resources to be allocated; Based on the configuration model of the operation and maintenance resources to be allocated, an initial configuration scheme and a resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated are obtained; wherein, the solution of the initial configuration scheme is expressed as a nonlinear programming problem, and the resource configuration deviation matrix is ​​determined after adjusting the initial configuration scheme based on the operation and maintenance resource data prediction model of convolutional neural network and long short-term memory network applied to multiple ITSS scenarios; Based on the initial configuration scheme and the resource configuration deviation matrix, the target configuration scheme for the optimized 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 constructing a configuration model for operation and maintenance resources based on the quantitative index parameters, with the goal of optimizing node performance, and in conjunction with the constraint model of the operation and maintenance resources to be allocated, includes: Based on the quantitative index parameters, a mapping relationship is constructed 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 mapping relationship, obtain the resource configuration node parameters of the operation and maintenance resource configuration model to be allocated; Based on the resource configuration node parameters, with optimal node performance as the goal, and in conjunction with the resource allocation constraint model, a resource allocation model is generated.

3. The method according to claim 2, characterized in that, The step of obtaining the initial configuration scheme and resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated based on the configuration model of the operation and maintenance resources to be allocated includes: Based on the configuration model of the operation and maintenance resources to be allocated, the nonlinear programming method and the genetic algorithm are combined to solve the initial configuration scheme of the optimal configuration of the operation and maintenance resources to be allocated under the preset conditions. Construct a prediction model for operation and maintenance resource data based on CNN-LSTM; The initial configuration scheme is adjusted based on the operation and maintenance resource data prediction model applied to multiple ITSS scenarios to determine the resource configuration deviation matrix.

4. The method according to any one of claims 1 to 3, characterized in that, The step of obtaining the target configuration scheme for optimizing the allocation of operation and maintenance resources based on the initial configuration scheme and the resource configuration deviation matrix includes: Based on the initial configuration scheme and the resource configuration deviation matrix, the optimization target value in the set of operation and maintenance resource configuration schemes is obtained; Based on the optimization target value, the improved ant colony algorithm is used to analyze the set of operation and maintenance resource configuration schemes to obtain the target configuration scheme for the optimized allocation of operation and maintenance resources to be allocated.

5. An ITSS operation and maintenance resource configuration optimization system, characterized in that, The system includes: A module is established to build a network node model of the operational resources to be allocated. These nodes represent different operational teams, devices, servers, or other resources requiring management and maintenance. The process of building the network node model includes: determining the nodes to be allocated; establishing an adjacency matrix and a feature matrix based on the connection relationships between the nodes; wherein the feature matrix includes node resource status and node performance indicators, where the node resource status includes the node's current load, remaining capacity, or availability, and the node performance indicators include response time, failure rate, or stability; and generating the network node model based on the adjacency matrix and feature matrix. The construction module is used to build a constraint model of the operation and maintenance resources to be allocated based on the constraints of the operation and maintenance resources to be allocated. Among them, based on the allocation objectives of minimizing cost and maximizing the utilization rate of operation and maintenance resources, the weighted sum method is used with the maximum cost of each node and the maximum total cost as constraints. The multi-objective optimization function is used as the linear programming function, and the constraint model of the operation and maintenance resources to be allocated is generated through mixed integer programming method and constraint satisfaction solution algorithm. The determining module is used to determine 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; wherein, the step of 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; and constructing the configuration model of the operation and maintenance resources to be allocated based on the quantitative indicator parameters, with the optimization of node performance as the goal, and in conjunction with the constraint model of the operation and maintenance resources to be allocated. The acquisition module is used to obtain an initial configuration scheme and a resource configuration deviation matrix for the optimized configuration of the operation and maintenance resources to be allocated, based on the configuration model of the operation and maintenance resources to be allocated; wherein, the solution of the initial configuration scheme is expressed as a nonlinear programming problem, and the resource configuration deviation matrix is ​​determined after adjusting the initial configuration scheme based on the operation and maintenance resource data prediction model of convolutional neural network and long short-term memory network applied to multiple ITSS scenarios; The module is used to obtain the target configuration scheme for the optimized configuration of the operation and maintenance resources to be allocated, based on the initial configuration scheme and the resource configuration deviation matrix.

6. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to implement the method of any one of claims 1 to 4 when it is run.

7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to implement the method of any one of claims 1 to 4.

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