Power distribution network resource allocation method and device, storage medium and electronic equipment

Abstract

The invention provides a power distribution network resource allocation method and device, a storage medium and electronic equipment, and belongs to the technical field of power systems. The power distribution network resource allocation method comprises the following steps: acquiring service data in real time, wherein the service data comprises a plurality of services to be processed and corresponding service characteristics; based on the business characteristics of the to-be-processed businesses, performing priority evaluation on the to-be-processed businesses by adopting a preset priority evaluation model to obtain priority levels corresponding to the to-be-processed businesses; acquiring current resource use information in real time; and based on the priority level corresponding to each to-be-processed service and the current resource use information, performing resource allocation on each to-be-processed service to obtain a service resource allocation result. The processing efficiency and the resource utilization rate of power grid services can be remarkably improved, the processing delay of key services is reduced, and the stability and the reliability of a system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distribution network resource allocation method, a distribution network resource allocation device, a machine-readable storage medium, and an electronic device. Background Art

[0002] With the development of computer technology, the power industry can build corresponding business processing systems to handle grid business. With the widespread application of new loads such as distributed energy, microgrids, and electric vehicle charging stations in medium-voltage distribution networks, the complexity and real-time requirements of grid business are increasing.

[0003] Traditional distribution network resource allocation is based on static rules or fixed algorithms. This method is difficult to cope with the dynamic changes in business needs and the suddenness of resource usage, resulting in delays in key business processing and low resource utilization. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a distribution network resource allocation method, a distribution network resource allocation device, a machine-readable storage medium and an electronic device. The distribution network resource allocation method can evaluate business priorities in real time and dynamically adjust the resource allocation strategy according to the current resource usage, ensuring that key businesses and emergency businesses are given priority processing while maximizing resource utilization efficiency, thereby significantly improving the processing efficiency and resource utilization of power grid businesses, reducing the processing delay of key businesses, and improving the stability and reliability of the system.

[0005] In order to achieve the above objectives, the present application provides a first aspect of a method for allocating resources in a distribution network, comprising:

[0006] Acquire business data in real time, the business data including multiple pending businesses and corresponding business characteristics;

[0007] Based on the service characteristics of each pending service, a preset priority evaluation model is used to perform priority evaluation on the plurality of pending services to obtain a priority level corresponding to each pending service;

[0008] Get current resource usage information in real time;

[0009] Based on the priority level corresponding to each of the pending services and the current resource usage information, resources are allocated to each of the pending services to obtain a service resource allocation result.

[0010] In the embodiment of the present application, the service characteristics include service characteristics of multiple dimensions;

[0011] The priority evaluation of the plurality of pending services is performed based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service, including:

[0012] Determine scores for each dimension of each pending business based on the business characteristics of each dimension of each pending business;

[0013] Performing weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score for each pending business;

[0014] Based on the comprehensive evaluation scores of the various pending businesses, priority evaluation is performed on the multiple pending businesses to obtain priority levels corresponding to the various pending businesses.

[0015] In the embodiment of the present application, the priority evaluation of the plurality of pending services is performed based on the comprehensive evaluation score of each pending service to obtain the priority level corresponding to each pending service, including:

[0016] Sorting the plurality of pending businesses based on the comprehensive evaluation scores of the pending businesses to obtain a sorting result;

[0017] Based on the sorting result, the priority level corresponding to each of the pending services is obtained.

[0018] In an embodiment of the present application, allocating resources to each pending service based on the priority level corresponding to each pending service and the current resource usage information to obtain a service resource allocation result includes:

[0019] Based on the priority level corresponding to each pending business and the current resource usage information, a preset intelligent scheduling model is used to allocate resources to each pending business to obtain a business resource allocation result. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

[0020] In the embodiment of the present application, it also includes:

[0021] Based on the service resource allocation result, a resource quota of a virtual machine or container corresponding to each service to be processed is determined, and the virtual machine or container corresponding to each service to be processed is used to process the corresponding service to be processed.

[0022] In the embodiment of the present application, it also includes:

[0023] Get feedback;

[0024] Based on the feedback information, the preset priority evaluation model and / or the service resource allocation result is adjusted.

[0025] In the embodiment of the present application, the real-time acquisition of business data includes:

[0026] Obtain initial business data in real time;

[0027] The initial service data is preprocessed to obtain service data.

[0028] A second aspect of the present application provides a distribution network resource allocation device, comprising:

[0029] A first acquisition module is used to acquire business data in real time, wherein the business data includes a plurality of pending businesses and corresponding business features;

[0030] An evaluation module, configured to perform priority evaluation on the plurality of pending services based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service;

[0031] The second acquisition module is used to obtain current resource usage information in real time;

[0032] The allocation module is used to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result.

[0033] In the embodiment of the present application, the service characteristics include service characteristics of multiple dimensions;

[0034] The evaluation module includes:

[0035] A scoring unit, configured to determine scores for each dimension of each business to be processed based on business characteristics of each dimension of each business to be processed;

[0036] A summing unit, configured to perform weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score of each pending business;

[0037] An evaluation unit is configured to perform priority evaluation on the plurality of pending businesses based on the comprehensive evaluation scores of the plurality of pending businesses, so as to obtain a priority level corresponding to each pending business.

[0038] In this embodiment of the present application, the allocation module includes:

[0039] The intelligent scheduling unit is used to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result using a preset intelligent scheduling model. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

[0040] A third aspect of the present application provides an electronic device, the electronic device comprising:

[0041] at least one processor;

[0042] a memory connected to the at least one processor;

[0043] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the above-mentioned distribution network resource allocation method by executing the instructions stored in the memory.

[0044] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned distribution network resource allocation method.

[0045] Through the above technical solution, business data is acquired in real time, and the business data includes multiple pending businesses and corresponding business characteristics; based on the business characteristics of each pending business, a preset priority evaluation model is used to perform priority evaluation on the multiple pending businesses to obtain the priority level corresponding to each pending business; current resource usage information is acquired in real time; based on the priority level corresponding to each pending business and the current resource usage information, resources are allocated to each pending business to obtain a business resource allocation result. It is possible to evaluate business priorities in real time and dynamically adjust resource allocation strategies based on current resource usage to ensure that critical businesses and emergency businesses are given priority while maximizing resource utilization efficiency, thereby significantly improving the processing efficiency and resource utilization of power grid businesses, reducing processing delays for critical businesses, and improving system stability and reliability. It is possible to respond to changes in business needs in real time, optimize resource allocation strategies, and improve resource utilization and business processing efficiency.

[0046] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0048] Figure 1The following schematically illustrates a flow chart of a method for allocating resources in a power distribution network according to an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a system architecture according to an embodiment of the present application is shown;

[0050] Figure 3 The following schematically illustrates a flow chart of dynamic intelligent service priority scheduling and adaptive resource optimization allocation according to an embodiment of the present application;

[0051] Figure 4 The following schematically illustrates a flow chart of a method for evaluating distribution network services based on multi-dimensional priorities according to an embodiment of the present application;

[0052] Figure 5 The following schematically shows a structural block diagram of a distribution network resource allocation device according to an embodiment of the present application;

[0053] Figure 6 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown.

[0054] Description of Reference Numerals

[0055] 410 - first acquisition module; 420 - evaluation module; 430 - second acquisition module; 440 - allocation module; A01 - processor; A02 - network interface; A03 - internal memory; A04 - display screen; A05 - input device; A06 - non-volatile storage medium; B01 - operating system; B02 - computer program. DETAILED DESCRIPTION

[0056] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0057] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0058] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0059] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0060] Please see Figure 1 , Figure 1 The following is a schematic diagram of a flow chart of a method for allocating resources in a power distribution network according to an embodiment of the present application. This embodiment provides a method for allocating resources in a power distribution network, comprising the following steps:

[0061] Step 210: Acquire business data in real time, where the business data includes a plurality of pending businesses and corresponding business features;

[0062] In this embodiment, the above acquisition can be obtained by collecting business data in real time from data sources such as sensors, monitoring equipment, and user input in the power distribution network. The above business characteristics include business type, urgency, resource requirements, execution time, etc.

[0063] In some embodiments, the real-time acquisition of business data includes:

[0064] First, obtain initial business data in real time;

[0065] Then, the initial service data is preprocessed to obtain service data.

[0066] In this embodiment, the above-mentioned initial business data may refer to business data collected in real time from data sources such as sensors, monitoring equipment, and user input in the distribution network. The above-mentioned preprocessing includes: cleaning the collected business data, removing invalid and abnormal data, and formatting the data to ensure data consistency and availability, thereby improving the accuracy of the business data.

[0067] Step 220: Based on the service characteristics of each service to be processed, a preset priority evaluation model is used to perform priority evaluation on the plurality of services to be processed to obtain a priority level corresponding to each service to be processed;

[0068] In this embodiment, the preset priority assessment model can be pre-set and used to assess the processing level of pending services. Services can be evaluated in multiple dimensions based on preset priority assessment dimensions (such as urgency, resource requirements, service type, etc.). Based on the assessment results, each service is assigned a priority level.

[0069] In some embodiments, see Figure 4 , Figure 4 The schematic diagram of the flow chart of the distribution network service evaluation method based on multi-dimensional priority according to an embodiment of the present application is shown. The service characteristics include service characteristics of multiple dimensions; accordingly, based on the service characteristics of each pending service, a preset priority evaluation model is used to perform priority evaluation on the multiple pending services to obtain the priority level corresponding to each pending service, including the following steps:

[0070] First, based on the business characteristics of each dimension of each business to be processed, the scores of each dimension of each business to be processed are determined;

[0071] In this embodiment, multiple dimensions of service characteristics may include: 1. Service Type: This can be determined based on the nature of the service, such as emergency maintenance, routine maintenance, preventive maintenance, new equipment installation, system upgrade, etc., and can be assigned different weights. 2. Urgency: Based on the service's impact on the safe and stable operation of the power grid and the scope of user impact, services can be categorized into four levels: important and urgent, important but not urgent, urgent but not important, and unimportant and not urgent. 3. Historical Data: This can analyze the processing efficiency, user satisfaction, fault recurrence rate, and resource consumption of similar services in the past as a reference for evaluation. 4. Resource Requirements: This can assess the total amount of resources (such as human, material, and time) required to complete the service and categorize them into high, medium, and low levels. 5. Other Dimensions: This can be evaluated based on safety, economy, environmental protection, and other factors. For each service to be processed, a corresponding score can be determined based on the service characteristics of each dimension and the weight coefficients assigned to each service characteristic. For example, for a service of emergency maintenance type, a weight coefficient of 3 can be assigned, resulting in a corresponding score of 3. For a service of important and urgent urgency, a weight coefficient of 2 can be assigned, resulting in a corresponding score of 2.

[0072] It should be noted that the methods for determining the above-mentioned weight coefficients include: adopting expert scoring method, historical data analysis method or machine learning algorithm, etc. Specifically, the weight coefficients of each dimension can be determined according to business characteristics and grid requirements.

[0073] Then, the weighted sum of the scores of each dimension of each pending business is performed to obtain a comprehensive evaluation score of each pending business;

[0074] In this embodiment, each business to be processed has scores in various dimensions, and the scores in various dimensions can be weighted and summed to obtain a comprehensive evaluation score.

[0075] Finally, based on the comprehensive evaluation scores of the various pending businesses, priority evaluation is performed on the multiple pending businesses to obtain priority levels corresponding to the various pending businesses.

[0076] In this embodiment, after obtaining the comprehensive evaluation score of each pending business, the corresponding priority level can be determined based on the comprehensive evaluation score. For example, a threshold value for each priority level can be set, and the comprehensive evaluation score can be compared with the threshold value for each priority level to determine the corresponding priority level.

[0077] Based on the business characteristics of each dimension of each pending business, a score for each dimension is determined. The weighted sum of the scores for each dimension is then used to obtain a comprehensive assessment score for each business. This comprehensive assessment score allows for a multi-dimensional quantitative evaluation of distribution network businesses, helping to accurately determine the priority level of each pending business. By comprehensively considering factors across multiple dimensions, optimizing the distribution network business processing process helps improve resource allocation efficiency, enhance grid security, improve user satisfaction, and promote intelligent management.

[0078] In some embodiments, the priority evaluation of the plurality of pending businesses is performed based on the comprehensive evaluation scores of the respective pending businesses to obtain the priority level corresponding to the respective pending businesses, including:

[0079] The first step is to sort the plurality of pending businesses based on the comprehensive evaluation scores of the respective pending businesses to obtain a sorting result;

[0080] The second step is to obtain the priority level corresponding to each of the pending services based on the sorting result.

[0081] In this embodiment, all pending services may be ranked according to the comprehensive evaluation scores, with higher scores indicating higher priorities, thereby determining the priority level corresponding to each pending service.

[0082] By sorting all pending businesses according to the comprehensive evaluation scores, the priority level corresponding to each pending business can be quickly determined, which helps to guide the optimal allocation of resources and the order of business processing.

[0083] Step 230: Obtain current resource usage information in real time;

[0084] In this embodiment, the current resource usage can be monitored in real time, including CPU usage, memory usage, network bandwidth, etc., to obtain current resource usage information. Based on the current resource usage information, resource usage trends can be analyzed to predict future resource requirements.

[0085] Step 240: Based on the priority level corresponding to each of the pending services and the current resource usage information, resources are allocated to each of the pending services to obtain a service resource allocation result.

[0086] In this embodiment, the current resource usage information can indicate the current resource usage. Then, the resource allocation strategy can be dynamically adjusted based on the priority and resource usage of each pending service. The resource requirements of high-priority services can be met first, thereby ensuring that critical services receive sufficient resources.

[0087] In some embodiments, resources are allocated to each pending business based on the priority level corresponding to each pending business and the current resource usage information to obtain a business resource allocation result, including: based on the priority level corresponding to each pending business and the current resource usage information, resources are allocated to each pending business using a preset intelligent scheduling model to obtain a business resource allocation result, and the preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

[0088] In this embodiment, based on the evaluation results of the multi-dimensional priority evaluation module and combined with the current resource usage (such as CPU usage, memory usage, network bandwidth, etc.), the resource allocation strategy is dynamically adjusted to allocate resources to each business, ensuring that high-priority businesses receive sufficient resource support. The above-mentioned preset intelligent scheduling model can be obtained based on an intelligent optimization algorithm, which includes but is not limited to a genetic algorithm, a particle swarm algorithm, etc. Assume that there are 5 businesses and 3 types of resources, and these resources need to be allocated to the businesses to minimize the completion time. Genetic algorithms or particle swarm algorithms can be used to solve this problem.

[0089] Taking the preset intelligent scheduling model based on genetic algorithm as an example, the above resource allocation may include the following steps:

[0090] 1. Initialize the population: Create an initial population of multiple individuals, each representing a possible resource allocation scheme. The genes of an individual can represent the amount or ratio of different resources allocated to different tasks.

[0091] 2. Fitness evaluation: Define a fitness function to evaluate the performance of each individual. The fitness function can be calculated based on business requirements, such as task completion time, resource utilization, and cost.

[0092] 3. Selection: Based on the fitness value, a portion of outstanding individuals are selected as parents to produce the next generation. Roulette selection, tournament selection, and other methods can be used.

[0093] 4. Crossover operation: Crossover is performed on selected parent individuals to produce new offspring individuals. Crossover operation can simulate genetic recombination and produce individuals with new characteristics.

[0094] 5. Mutation: Mutate offspring individuals to introduce new genetic information. Mutation can increase population diversity and avoid premature convergence.

[0095] 6. Iterative update: Repeat the selection, crossover, and mutation operations until the preset number of iterations is reached or the fitness value converges. In each iteration, the population is updated according to the fitness value, and the best individuals are retained.

[0096] 7. Output results: Finally, the individual with the highest fitness value is selected from the population as the optimal solution, that is, the best resource allocation plan, and the business resource allocation result is obtained.

[0097] Taking the preset intelligent scheduling model based on the particle swarm algorithm as an example, the above resource allocation may include the following steps:

[0098] 1. Initialize the particle swarm: Randomly generate a certain number of particles, each of which represents a possible resource allocation solution. Each particle contains position and velocity information. The position represents the resource allocation solution, and the velocity represents the change trend of the solution.

[0099] 2. Fitness evaluation: Calculate the fitness value of each particle based on the objective function (such as the completion time of business requirements, resource utilization, etc.).

[0100] 3. Update individual and global optimality: Compare the current fitness value of each particle with the historical optimal fitness value, and update the individual optimal position. Compare the individual optimal fitness values ​​of all particles and update the global optimal position.

[0101] 4. Update particle speed and position: Adjust the particle speed and position according to the update formula to move it towards the individual optimal and global optimal positions.

[0102] 5. Iterative update: Repeat the operations of fitness evaluation, updating individual optimal and global optimal, and updating particle speed and position until the maximum number of iterations is reached or the fitness value converges.

[0103] 6. Output results: Ultimately, the global optimal position is the best resource allocation solution, and the business resource allocation result is obtained.

[0104] By dynamically adjusting resource allocation strategies based on business priorities and resource usage, and using intelligent scheduling algorithms to intelligently schedule and allocate resources for businesses, the optimal business resource allocation plan is obtained, improving the accuracy of business resource allocation results.

[0105] In the above implementation process, business data is acquired in real time, and the business data includes multiple pending businesses and corresponding business characteristics; based on the business characteristics of each pending business, a preset priority evaluation model is used to perform priority evaluation on the multiple pending businesses to obtain the priority level corresponding to each pending business; current resource usage information is acquired in real time; based on the priority level corresponding to each pending business and the current resource usage information, resources are allocated to each pending business to obtain a business resource allocation result. It is capable of evaluating business priorities in real time and dynamically adjusting resource allocation strategies based on current resource usage to ensure that critical businesses and emergency businesses are given priority while maximizing resource utilization efficiency, thereby significantly improving the processing efficiency and resource utilization of power grid businesses, reducing processing delays for critical businesses, and improving system stability and reliability. It is capable of responding to changes in business needs in real time, optimizing resource allocation strategies, and improving resource utilization and business processing efficiency.

[0106] In some embodiments, the method further includes: determining a resource quota of a virtual machine or container corresponding to each of the services to be processed based on the service resource allocation result, and the virtual machine or container corresponding to each of the services to be processed is used to process the corresponding services to be processed.

[0107] In this embodiment, virtual machines / containers can be created or destroyed based on resource allocation policies on the virtualization and containerization platform. Resource quotas for virtual machines / containers, such as CPU, memory, and network bandwidth, can be adjusted. Specifically, virtual machines / containers can be created when business demand increases, business expands or upgrades, or when high-priority business arrives. They can be destroyed when business demand ends, resource utilization is low, or a failure or anomaly occurs. The application of virtualization technology and containerized deployment will provide strong support for flexible system expansion and upgrades.

[0108] In some embodiments, the following steps are further included:

[0109] First, get feedback;

[0110] In this embodiment, the feedback information includes service feedback information and system logs, etc., so as to understand the real-time changes in system performance and service requirements.

[0111] Then, based on the feedback information, the preset priority evaluation model and / or the service resource allocation result is adjusted.

[0112] In this embodiment, the system performance and business satisfaction can be analyzed based on the feedback information. Based on the analysis results, necessary adjustments and optimizations can be made to the system, such as adjusting the resource allocation strategy, optimizing the priority evaluation dimension, optimizing the intelligent scheduling algorithm, etc., to improve the stability and performance of the system to adapt to the ever-changing business needs.

[0113] In specific implementations, the intelligent scheduling and optimization module can continuously learn and optimize to improve the accuracy and efficiency of the scheduling algorithm to adapt to changing business needs. It is responsible for monitoring the operating status of the system and making adjustments and optimizations as needed.

[0114] Through continuous feedback and iterative optimization, the system can continuously adapt to changes in the environment and business needs, achieving long-term stable operation and optimization.

[0115] The following is a specific example to illustrate the solution, please refer to Figure 2 , Figure 2 The following schematically illustrates a system architecture diagram according to an embodiment of the present application. A dynamic intelligent service priority scheduling and adaptive resource optimization allocation system includes: a data acquisition module, a multi-dimensional priority evaluation module, an adaptive resource optimization allocation module, a virtualization and containerization deployment module, and an intelligent scheduling and optimization module.

[0116] Data Collection Module: This module is responsible for collecting real-time business data from the distribution network, including information such as business type, urgency, and resource requirements. Data sources may include sensors, monitoring equipment, and user input. The collected data is input into a multi-dimensional priority assessment model for priority assessment.

[0117] Multi-dimensional Priority Assessment Module: Based on the business data provided by the data collection module, it conducts a multi-dimensional priority assessment of the business. The assessment results are used as the basis for resource allocation to ensure that high-priority businesses are given priority.

[0118] Adaptive Resource Optimization Allocation Module: Dynamically adjusts resource allocation strategies based on the evaluation results of the multi-dimensional priority evaluation module and current resource usage (such as CPU utilization, memory usage, and network bandwidth). It is responsible for allocating resources to various businesses, ensuring that high-priority businesses receive sufficient resources.

[0119] Virtualization and Containerized Deployment Module: Leveraging virtualization technology and containerized deployment solutions, this module provides an independent computing environment for services within the distribution network. This module is responsible for creating and destroying virtual machines and containers on the virtualization platform, as well as adjusting resource quotas. This module utilizes virtualization technology to achieve flexible expansion and isolation of computing resources, improving system stability and security.

[0120] Intelligent Scheduling and Optimization Module: This module uses intelligent scheduling algorithms to intelligently schedule services and allocate resources. Through continuous learning and optimization, it improves the accuracy and efficiency of scheduling algorithms to adapt to changing business needs. It monitors the system's operating status and makes adjustments and optimizations as needed.

[0121] Please see Figure 3 , Figure 3 A flowchart of dynamic intelligent business priority scheduling and adaptive resource optimization allocation according to an embodiment of the present application is schematically shown. The process of the above-mentioned system when performing dynamic intelligent business priority scheduling and adaptive resource optimization allocation includes: first, system configuration and initialization, including resource monitoring frequency, priority assessment dimensions, resource allocation strategy, etc. Configure data acquisition source and acquisition frequency. Initialize the virtualization and containerization platform and prepare computing resources. Then, real-time data acquisition is performed, including real-time collection of business data from data sources such as sensors, monitoring equipment, and user input in the distribution network. The data includes business type, urgency, resource requirements, execution time, etc. Then, data preprocessing is performed, including cleaning the collected data and removing invalid and abnormal data. The data is formatted to ensure data consistency and availability. Then, a multi-dimensional priority assessment is performed, and the business is evaluated in multiple dimensions according to preset priority assessment dimensions (such as urgency, resource requirement size, business type, etc.). Based on the assessment results, a priority level is assigned to each business. Then, resource usage monitoring is performed, and current resource usage, including CPU usage, memory usage, network bandwidth, etc., is monitored in real time. Next, the adaptive resource optimization phase begins, dynamically adjusting resource allocation strategies based on business priorities and resource usage. Prioritizing resource needs for high-priority businesses ensures sufficient resources for critical businesses. After allocation, the system determines whether the resource allocation results meet requirements. If so, the process ends. If not, the multi-dimensional evaluation model is adjusted and the multi-dimensional priority evaluation is repeated.

[0122] Figure 1 FIG. 1 is a flow chart of the method for allocating resources in the power distribution network in the embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0123] Please see Figure 5 , Figure 5 The following schematically illustrates a block diagram of a distribution network resource allocation device according to an embodiment of the present application. This embodiment provides a distribution network resource allocation device, comprising a first acquisition module 410, an evaluation module 420, a second acquisition module 430, and an allocation module 440, wherein:

[0124] A first acquisition module 410 is configured to acquire business data in real time, wherein the business data includes a plurality of pending businesses and corresponding business characteristics;

[0125] An evaluation module 420 is configured to perform priority evaluation on the plurality of pending services based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service;

[0126] The second acquisition module 430 is used to obtain current resource usage information in real time;

[0127] The allocation module 440 is configured to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result.

[0128] The business characteristics include business characteristics in multiple dimensions;

[0129] The evaluation module 420 includes:

[0130] A scoring unit, configured to determine scores for each dimension of each business to be processed based on business characteristics of each dimension of each business to be processed;

[0131] A summing unit, configured to perform weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score of each pending business;

[0132] An evaluation unit is configured to perform priority evaluation on the plurality of pending businesses based on the comprehensive evaluation scores of the plurality of pending businesses, so as to obtain a priority level corresponding to each pending business.

[0133] The allocation module 440 includes:

[0134] The intelligent scheduling unit is used to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result using a preset intelligent scheduling model. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

[0135] The distribution network resource allocation device includes a processor and a memory. The above-mentioned first acquisition module 410, evaluation module 420, second acquisition module 430 and allocation module 440 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0136] The processor contains a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels, and the distribution network resources are allocated by adjusting the kernel parameters.

[0137] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0138] An embodiment of the present invention provides a machine-readable storage medium having a program stored thereon, which implements the distribution network resource allocation method when executed by a processor.

[0139] An embodiment of the present invention provides a processor, which is used to run a program, wherein the distribution network resource allocation method is executed when the program is run.

[0140] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a method for allocating resources in a distribution network is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display screen, and the input device A05 of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0141] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0142] In one embodiment, the distribution network resource allocation device provided by the present application can be implemented in the form of a computer program. Figure 6 The computer device is run on the computer device shown. The memory of the computer device can store various program modules that constitute the distribution network resource allocation device, such as, Figure 5 The first acquisition module 410, the evaluation module 420, the second acquisition module 430 and the allocation module 440 are shown. The computer program composed of various program modules enables the processor to execute the steps of the distribution network resource allocation method of each embodiment of the present application described in this specification.

[0143] Figure 6 The computer device shown can be Figure 5 In the distribution network resource allocation device shown, the first acquisition module 410 executes step 210 , the evaluation module 420 executes step 220 , the second acquisition module 430 executes step 230 , and the allocation module 440 executes step 240 .

[0144] An embodiment of the present application provides a device, comprising a processor, a memory, and a program stored in the memory and executable on the processor, the device being applied to a security module; when the processor executes the program, the following steps are implemented:

[0145] Acquire business data in real time, the business data including multiple pending businesses and corresponding business characteristics;

[0146] Based on the service characteristics of each pending service, a preset priority evaluation model is used to perform priority evaluation on the plurality of pending services to obtain a priority level corresponding to each pending service;

[0147] Get current resource usage information in real time;

[0148] Based on the priority level corresponding to each of the pending services and the current resource usage information, resources are allocated to each of the pending services to obtain a service resource allocation result.

[0149] In one embodiment, the business characteristics include business characteristics in multiple dimensions;

[0150] The priority evaluation of the plurality of pending services is performed based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service, including:

[0151] Determine scores for each dimension of each pending business based on the business characteristics of each dimension of each pending business;

[0152] Performing weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score for each pending business;

[0153] Based on the comprehensive evaluation scores of the various pending businesses, priority evaluation is performed on the multiple pending businesses to obtain priority levels corresponding to the various pending businesses.

[0154] In one embodiment, the priority evaluation of the plurality of pending businesses is performed based on the comprehensive evaluation scores of the respective pending businesses to obtain the priority level corresponding to the respective pending businesses, including:

[0155] Sorting the plurality of pending businesses based on the comprehensive evaluation scores of the pending businesses to obtain a sorting result;

[0156] Based on the sorting result, the priority level corresponding to each of the pending services is obtained.

[0157] In one embodiment, allocating resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information to obtain a service resource allocation result includes:

[0158] Based on the priority level corresponding to each pending business and the current resource usage information, a preset intelligent scheduling model is used to allocate resources to each pending business to obtain a business resource allocation result. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

[0159] In one embodiment, it further includes:

[0160] Based on the service resource allocation result, a resource quota of a virtual machine or container corresponding to each service to be processed is determined, and the virtual machine or container corresponding to each service to be processed is used to process the corresponding service to be processed.

[0161] In one embodiment,

[0162] Get feedback;

[0163] Based on the feedback information, the preset priority evaluation model and / or the service resource allocation result is adjusted.

[0164] In one embodiment, the real-time acquisition of business data includes:

[0165] Obtain initial business data in real time;

[0166] The initial service data is preprocessed to obtain service data.

[0167] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0168] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0169] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0171] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0172] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0173] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0174] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0175] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for allocating distribution network resources, characterized in that: include: Acquire business data in real time, the business data including multiple pending businesses and corresponding business characteristics; Based on the service characteristics of each pending service, a preset priority evaluation model is used to perform priority evaluation on the plurality of pending services to obtain a priority level corresponding to each pending service; Get current resource usage information in real time; Based on the priority level corresponding to each of the pending services and the current resource usage information, resources are allocated to each of the pending services to obtain a service resource allocation result.

2. The method for allocating distribution network resources according to claim 1, wherein: The business characteristics include business characteristics in multiple dimensions; The priority evaluation of the plurality of pending services is performed based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service, including: Determine scores for each dimension of each pending business based on the business characteristics of each dimension of each pending business; Performing weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score for each pending business; Based on the comprehensive evaluation scores of the various pending businesses, priority evaluation is performed on the multiple pending businesses to obtain priority levels corresponding to the various pending businesses.

3. The method for allocating distribution network resources according to claim 2, wherein: The step of performing priority evaluation on the plurality of pending businesses based on the comprehensive evaluation scores of the pending businesses to obtain the priority levels corresponding to the respective pending businesses includes: Sorting the plurality of pending businesses based on the comprehensive evaluation scores of the pending businesses to obtain a sorting result; Based on the sorting result, the priority level corresponding to each of the pending services is obtained.

4. The method for allocating distribution network resources according to claim 1, wherein: Allocating resources to the respective pending services based on the priority levels corresponding to the respective pending services and the current resource usage information to obtain a service resource allocation result includes: Based on the priority level corresponding to each pending business and the current resource usage information, a preset intelligent scheduling model is used to allocate resources to each pending business to obtain a business resource allocation result. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

5. The method for allocating distribution network resources according to claim 1, characterized in that: Also includes: Based on the service resource allocation result, a resource quota of a virtual machine or container corresponding to each service to be processed is determined, and the virtual machine or container corresponding to each service to be processed is used to process the corresponding service to be processed.

6. The method for allocating distribution network resources according to claim 1, characterized in that: Also includes: Get feedback; Based on the feedback information, the preset priority evaluation model and / or the service resource allocation result is adjusted.

7. The method for allocating distribution network resources according to claim 1, characterized in that: The real-time acquisition of business data includes: Acquire initial business data in real time; The initial service data is preprocessed to obtain service data.

8. A distribution network resource allocation device, characterized in that: include: A first acquisition module is used to acquire business data in real time, wherein the business data includes a plurality of pending businesses and corresponding business features; An evaluation module is used to evaluate the priority of the plurality of pending services based on the service characteristics of each pending service using a preset priority evaluation model to obtain a priority level corresponding to each pending service; The second acquisition module is used to obtain current resource usage information in real time; The allocation module is used to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result.

9. The distribution network resource allocation device according to claim 8, characterized in that: The business characteristics include business characteristics in multiple dimensions; The evaluation module includes: A scoring unit, configured to determine scores for each dimension of each business to be processed based on business characteristics of each dimension of each business to be processed; A summing unit, configured to perform weighted summation on the scores of each dimension of each pending business to obtain a comprehensive evaluation score of each pending business; An evaluation unit is configured to perform priority evaluation on the plurality of pending businesses based on the comprehensive evaluation scores of the plurality of pending businesses, so as to obtain a priority level corresponding to each pending business.

10. The distribution network resource allocation device according to claim 8, characterized in that: The allocation module includes: The intelligent scheduling unit is used to allocate resources to each of the pending services based on the priority level corresponding to each of the pending services and the current resource usage information, and obtain a service resource allocation result using a preset intelligent scheduling model. The preset intelligent scheduling model is constructed based on an intelligent optimization algorithm.

11. An electronic device, characterized in that: The electronic device includes: at least one processor; a memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the distribution network resource allocation method according to any one of claims 1 to 7 by executing the instructions stored in the memory.

12. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the power distribution network resource allocation method according to any one of claims 1 to 7.

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