Electric power communication network scheduling optimization method and device based on AI prediction

Abstract

The invention provides an electric power communication network scheduling optimization method and device based on AI prediction, and relates to the technical field of smart power grids, and the method comprises the steps: executing node monitoring of an electric power communication network, taking a real-time monitoring result as an execution data set, carrying out node state prediction based on a node prediction model, and building a prediction data set; and establishing a comprehensive evaluation network based on the node revenue function and the revenue proportion, taking the execution data set and the prediction data set as input data, performing collaborative effect optimization by the comprehensive evaluation network, and establishing a node action and collaborative weight distribution scheme based on an optimization result to perform network scheduling optimization. According to the invention, the technical problem that the working performance and efficiency of the communication network are poor due to the fact that accurate resource regulation and control cannot be carried out on the power communication network in time according to the operation condition in the existing power communication network scheduling method can be solved, and the reliability and efficiency of the power communication network can be improved; and the technical effects of power communication service stability and high quality are ensured.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method and device for optimizing power communication network scheduling based on AI prediction. Background Art

[0002] As the power system's reliance on communication networks continues to increase, the requirements for network performance and efficiency are also getting higher and higher. However, in the face of complex and changing operating conditions, due to the dynamic and complex nature of the network, traditional scheduling methods are difficult to respond to changes in network traffic in real time, resulting in problems such as uneven resource allocation, which limits the performance and efficiency of the network.

[0003] At present, due to the complex and changeable operating conditions of power communications, the existing power communication network scheduling methods have the technical problem of being unable to accurately regulate the resources of the power communication network in a timely manner according to the operating conditions, resulting in poor working performance and efficiency of the entire communication network. Summary of the invention

[0004] The purpose of this application is to provide an AI-based prediction-based power communication network scheduling optimization method and device to solve the technical problem that due to the complex and changeable power communication operating conditions, the existing power communication network scheduling method is unable to accurately regulate the resources of the power communication network in a timely manner according to the operating conditions, resulting in poor working performance and efficiency of the entire communication network.

[0005] In view of the above problems, the present application provides a method and device for optimizing power communication network scheduling based on AI prediction.

[0006] In the first aspect, the present application provides a method for optimizing power communication network scheduling based on AI prediction, which is implemented by a power communication network scheduling optimization device based on AI prediction, including: deploying a monitoring sensor group at the node of the power communication network, establishing a node data set with the monitoring sensor group, and each node data in the node data set includes network traffic data, node load data, delay data, fault data and energy consumption data; synchronously acquiring external environment data, the external environment data includes weather environment data and task data, and establishing a node prediction model according to the external environment data and the node data set, and the node prediction model corresponds to the power communication network node one by one; performing node monitoring of the power communication network to monitor in real time The measurement results are used as the execution data set, and the real-time monitoring results are used as the benchmark data, node status prediction is performed based on the node prediction model, and a prediction data set is established; the connectivity data of the node is read, the node coordination relationship is called with the connectivity data, the node coordination weight is established, and the node benefit function is configured with the node coordination weight; the benefit ratio of the execution state and the prediction state is configured, and a comprehensive evaluation network is established based on the node benefit function and the benefit ratio, the execution data set and the prediction data set are used as input data, and the comprehensive evaluation network is used to optimize the coordination effect of the power communication network, and a node action and coordination weight allocation scheme is established based on the optimization result; the power communication network scheduling is optimized through the node action and coordination weight allocation scheme.

[0007] In the second aspect, the present application also provides an AI-prediction-based power communication network scheduling optimization device, which is used to execute an AI-prediction-based power communication network scheduling optimization method as described in the first aspect, including: a node data set establishment module, which is used to deploy a monitoring sensor group at the node of the power communication network, and establish a node data set with the monitoring sensor group, and each node data in the node data set includes network traffic data, node load data, delay data, fault data and energy consumption data; a node prediction model establishment module, which is used to synchronously obtain external environment data, and the external environment data includes weather environment data and task data, and establish a node prediction model according to the external environment data and the node data set, and the node prediction model corresponds to the power communication network node one by one; a node state prediction module, which is used to perform node monitoring of the power communication network. , using the real-time monitoring results as the execution data set, and the real-time monitoring results as the benchmark data, based on the node prediction model, node state prediction is performed to establish a prediction data set; a node benefit function configuration module is used to read the node connectivity data, call the node coordination relationship with the connectivity data, establish the node coordination weight, and configure the node benefit function with the node coordination weight; a coordination effect optimization module is used to configure the benefit ratio of the execution state and the prediction state, establish a comprehensive evaluation network based on the node benefit function and the benefit ratio, use the execution data set and the prediction data set as input data, use the comprehensive evaluation network to optimize the coordination effect of the power communication network, and establish a node action and coordination weight allocation plan based on the optimization result; a network scheduling optimization module is used to optimize the scheduling of the power communication network through the node action and coordination weight allocation plan.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By utilizing the monitoring sensor group to perform node monitoring of the power communication network, the real-time monitoring results are used as the execution data set, and the real-time monitoring results are used as the benchmark data, and the node state prediction is performed based on the node prediction model to establish a prediction data set; then the node connectivity data is read, the node coordination relationship is called with the connectivity data, the node coordination weight is established, and the node benefit function is configured with the node coordination weight; the benefit ratio of the execution state and the prediction state is further configured, and a comprehensive evaluation network is established based on the node benefit function and the benefit ratio, and the execution data set and the prediction data set are used as input data. The comprehensive evaluation network is used to optimize the coordination effect of the power communication network, and a node action and coordination weight allocation scheme is established based on the optimization result; finally, the power communication network scheduling is optimized through the node action and coordination weight allocation scheme; the timeliness and accuracy of resource regulation in the power communication network can be improved, so that the power communication network can better adapt to the changing environment and communication needs, so as to improve the reliability and efficiency of the entire power communication network, and ensure the stability of power communication services and high-quality technical effects.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A flowchart of a method for optimizing power communication network scheduling based on AI prediction is provided in this application; Figure 2 A schematic diagram of a flow chart of establishing a node action and collaborative weight allocation scheme based on optimization results in a power communication network scheduling optimization method based on AI prediction in this application; Figure 3 This is a structural schematic diagram of a power communication network scheduling optimization device based on AI prediction in this application.

[0012] Description of reference numerals: Node data set establishment module 11, node prediction model establishment module 12, node state prediction module 13, node benefit function configuration module 14, synergy effect optimization module 15, network scheduling optimization module 16. DETAILED DESCRIPTION

[0013] This application provides a method and device for optimizing power communication network scheduling based on AI prediction, which solves the technical problem that the existing power communication network scheduling method cannot timely and accurately regulate the resources of the power communication network according to the operating conditions due to the complex and changeable power communication operating conditions, resulting in poor performance and efficiency of the entire communication network. It can improve the timeliness and accuracy of resource regulation in the power communication network, so that the power communication network can better adapt to the changing environment and communication needs, thereby improving the reliability and efficiency of the entire power communication network and ensuring the stability and high quality of power communication services.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] For example, please refer to the attached Figure 1 The present application provides a method for optimizing power communication network scheduling based on AI prediction, which is applied to a device for optimizing power communication network scheduling based on AI prediction, and specifically includes the following steps: Step 1: Deploy a monitoring sensor group at the node of the power communication network, and establish a node data set with the monitoring sensor group. Each node data in the node data set includes network traffic data, node load data, delay data, fault data and energy consumption data.

[0016] Specifically, multiple communication nodes of the power communication network are obtained, and monitoring sensor groups are deployed on multiple communication nodes. These sensors can collect key operation data, and the sensors can be configured according to the expected monitoring data type. Then, based on multiple sensor groups, operation data of multiple communication nodes are collected to obtain node data sets. Each node data in the node data set includes network traffic data, node load data, delay data, fault data and energy consumption data. Among them, network traffic data refers to the network traffic situation of each node, including the number and rate of incoming and outgoing data packets, etc. These data are crucial for analyzing network usage and predicting future network needs; node load data involves the processing capacity usage of each node, including CPU utilization, memory utilization, etc. By monitoring node load, potential overload problems can be discovered in time and network resource allocation can be adjusted accordingly; delay data reflects the time for data to be transmitted in the network. Monitoring delay helps to identify bottlenecks in the network, thereby optimizing the data transmission path and reducing communication delays; fault data records the fault conditions of the node, including fault type, occurrence time, duration, etc. These data are of great significance for diagnosing network health and preventing future faults; energy consumption data monitors the energy consumption of each node, which helps to optimize energy use and reduce operating costs. By monitoring and obtaining node data sets, data support is provided for subsequent optimization analysis of power communication network scheduling.

[0017] Step 2: synchronously obtain external environmental data, the external environmental data including weather environment data and task data, and establish a node prediction model based on the external environmental data and the node data set, wherein the node prediction model corresponds one-to-one to the power communication network node.

[0018] Specifically, at the same time node, an environmental monitoring sensor group is used to synchronously obtain external environmental data, which includes weather environment data and mission data. The weather environment data includes temperature, humidity, wind speed, rainfall, solar radiation, etc. These factors may affect the operation of the power communication network, especially for outdoor equipment. For example, high temperature or extreme weather may cause equipment performance to decline or failure rate to increase; mission data includes power trading, remote monitoring, emergency response, etc. Changes in mission data will affect network traffic and resource requirements.

[0019] Then, multiple node prediction models are constructed based on multiple power communication network nodes. The node prediction models correspond to the power communication network nodes one by one. The node prediction models are used to predict the node operation data in the future time period. The appropriate prediction model can be selected according to the actual situation, such as BP neural network model, time series analysis model, random forest, etc., wherein the input of the node prediction model is the external environment data and node data under the current node, and the output is the node prediction data in the future period. The construction method of the node prediction model is as follows: first, a first network node is randomly selected from multiple power communication network nodes, and then a search is performed based on the first network node to obtain a sample training data set; then, the sample training data set is used to supervise the node prediction model constructed based on the BP neural network. The supervised training can be performed using the back propagation algorithm and the loss function to obtain the first node prediction model that meets the convergence conditions, and multiple node prediction models are constructed using the same method. By using a neural network to construct a node prediction model, the accuracy and efficiency of node data prediction can be improved.

[0020] Step three: Execute node monitoring of the power communication network, use the real-time monitoring results as the execution data set, and use the real-time monitoring results as the benchmark data, perform node state prediction based on the node prediction model, and establish a prediction data set.

[0021] Specifically, the monitoring sensor group is used to perform node monitoring of the power communication network to obtain real-time monitoring results, including network traffic, node load, delay, failure and energy consumption, etc., and the real-time monitoring results are used as the execution data set; on the other hand, real-time external environment data is monitored and acquired, including weather environment data and task data. Then, the real-time monitoring results and real-time external environment data are input into the node prediction model to predict the node state, and a prediction data set is obtained, wherein the prediction data includes the predicted state of the node in the future period of time. By obtaining the prediction data set, data support is provided for the subsequent optimization analysis of node collaborative scheduling of the power communication network.

[0022] Step 4: Read the connectivity data of the node, use the connectivity data to call the node coordination relationship, establish the node coordination weight, and configure the node benefit function with the node coordination weight.

[0023] Specifically, the connectivity data of the nodes is read, such as through a network management system (NMS) or directly from a network device. These data generally include the connection status, link capacity, communication delay, link quality, etc. between nodes. Then, the node coordination relationship is called with the connectivity data, that is, based on the connectivity data, the coordination relationship between nodes is analyzed to determine which nodes are critical in data transmission and the interdependence relationship; further, the node coordination weight is established according to the node coordination relationship, that is, a weight value is assigned to the coordination relationship between each pair of nodes according to the node coordination relationship. The weight value can be determined based on factors such as the bandwidth, delay, and reliability of the link. The weight can be calculated using an expert system, statistical analysis, or machine learning method. The weight value reflects the degree of interdependence between nodes, that is, the closeness of the coordination relationship. Then, the node benefit function is configured according to the node coordination weight, where the benefit function can quantify the network value and contribution of each node and reflect its importance in the network. In this way, network administrators can better understand the performance and importance of each node in the network and optimize the allocation and scheduling of network resources accordingly. For example, when resources are limited, resources can be allocated preferentially based on node benefits, or when a failure occurs, those nodes that have the greatest impact on the overall performance of the network can be restored preferentially. This data- and model-driven decision-making method helps to improve the intelligence level and adaptability of power communication networks.

[0024] Step 5: Configure the profit ratio of the execution state and the prediction state, establish a comprehensive evaluation network based on the node profit function and the profit ratio, use the execution data set and the prediction data set as input data, use the comprehensive evaluation network to optimize the synergy effect of the power communication network, and establish a node action and synergy weight allocation plan based on the optimization results.

[0025] Specifically, first, it is necessary to determine the relative importance of the execution state (i.e., the current actual state) and the prediction state (i.e., the future prediction state) in the comprehensive evaluation, and configure the benefit ratio of the execution state and the prediction state according to the relative importance, wherein the benefit ratio is proportional to the relative importance. Then, a comprehensive evaluation network is established based on the node benefit function and the benefit ratio. The comprehensive evaluation network can be a multi-objective optimization model that combines the benefits of the execution state and the prediction state to evaluate the overall performance of the power communication network. Then, the execution data set and the prediction data set are input into the comprehensive evaluation network as input data, and the synergy effect of the power communication network is optimized through the comprehensive evaluation network, wherein the comprehensive evaluation network contains the target optimization function to obtain the optimization result. Then, based on the optimization result, a node action and a synergy weight allocation scheme are established, wherein the node action defines the specific operations that each node should take, such as increasing / decreasing bandwidth, adjusting routing strategies, performing maintenance tasks, etc.; the synergy weight allocation adjusts the synergy relationship between nodes according to the output of the comprehensive evaluation network to optimize the overall performance of the network.

[0026] Step 6: Optimize the power communication network scheduling through the node actions and collaborative weight allocation scheme.

[0027] Specifically, the power communication network scheduling optimization is finally carried out through the node action and collaborative weight allocation scheme, such as deploying the scheme, configuring network equipment, adjusting the configuration of network equipment according to the scheme, such as routers, switches and other communication equipment, to ensure the stability of the network and the continuity of services; performing specific node actions according to the optimization scheme, such as adjusting bandwidth, optimizing routing paths, performing maintenance tasks, etc.; adjusting the collaborative relationship between nodes according to the collaborative weight allocation scheme, implementing dynamic weight adjustment, and dynamically adjusting the collaborative weight according to the real-time changes in network traffic to optimize the overall performance of the network. Through the above steps, the power communication network can achieve efficient management of resources and ensure that the network can maintain optimal performance when facing different operating conditions. This decision-making method based on data and model drive helps to improve the intelligence level and adaptive ability of the network, thereby improving the performance and efficiency of the entire power communication network.

[0028] The power communication network scheduling optimization method based on AI prediction is applied to a power communication network scheduling optimization device based on AI prediction, which can solve the technical problem that due to the complex and changeable power communication operating conditions, the existing power communication network scheduling method is unable to accurately regulate the resources of the power communication network in time according to the operating conditions, resulting in poor working performance and efficiency of the entire communication network. By utilizing the monitoring sensor group to perform node monitoring of the power communication network, the real-time monitoring results are used as the execution data set, and the real-time monitoring results are used as the benchmark data, and the node state prediction is performed based on the node prediction model to establish a prediction data set; then the node connectivity data is read, the node coordination relationship is called with the connectivity data, the node coordination weight is established, and the node benefit function is configured with the node coordination weight; the benefit ratio of the execution state and the prediction state is further configured, and a comprehensive evaluation network is established based on the node benefit function and the benefit ratio, and the execution data set and the prediction data set are used as input data. The comprehensive evaluation network is used to optimize the coordination effect of the power communication network, and a node action and coordination weight allocation scheme is established based on the optimization result; finally, the power communication network scheduling is optimized through the node action and coordination weight allocation scheme; the timeliness and accuracy of resource regulation in the power communication network can be improved, so that the power communication network can better adapt to the changing environment and communication needs, so as to improve the reliability and efficiency of the entire power communication network, and ensure the stability of power communication services and high-quality technical effects.

[0029] Further, the connectivity data of the node is read, the node coordination relationship is called with the connectivity data, the node coordination weight is established, and the node benefit function is configured with the node coordination weight. Step 4 of this application includes: Establish node collaboration weights, the formula is as follows: ;in, Representing any two nodes and The collaborative weight of Representing any two nodes and communication capacity, Representing any two nodes and The communication delay, Representing any two nodes and Communication quality parameters, It is a comprehensive function; configure the node benefit function as follows: ;in, Characterization The node profit function of nodes is Characterization Node Actions, Characterization Processing Node The action set of other nodes, Characterization Node and The benefit function generated by the synergy between them.

[0030] Specifically, first, obtain the node collaborative weight calculation formula. In the node collaborative weight expression, Representing any two nodes and The collaborative weight of Representing any two nodes and communication capacity, Representing any two nodes and The communication delay, Representing any two nodes and Communication quality parameters, It is a comprehensive function used to calculate the collaborative weight. The purpose of this function is to combine factors such as communication capacity, communication delay and communication quality to form a weight value that can reflect the collaborative relationship between nodes. Through this collaborative weight expression that comprehensively considers multiple factors, the power communication network can more accurately evaluate the collaborative relationship between nodes, thereby achieving more effective scheduling optimization and resource management.

[0031] Get the calculation formula of the node profit function. In the node profit function, Characterization The node benefit function of nodes is Characterization Node Actions, Characterization Processing Node The action set of other nodes, Characterization Node and The purpose of this function is to combine the actions of a node, the actions of other nodes, and the collaborative relationship between nodes to form a benefit value that can reflect the value of the node. Through this node benefit function that takes into account multiple factors, the power communication network can more accurately evaluate the value and contribution of the node, thereby achieving more effective scheduling optimization and resource management. This approach helps to improve the performance and efficiency of the network and ensure the stability and high quality of power communication services.

[0032] Further, the profit ratio of the execution state and the prediction state is configured. Step 5 of this application includes: Perform reliability analysis on the execution state based on the execution data set to establish a reliability evaluation result; perform uncertainty analysis on the prediction state based on the prediction data set to establish an uncertainty evaluation result; obtain the stability requirement of the power communication network, and configure the profit ratio according to the stability requirement, reliability evaluation result, and uncertainty evaluation result.

[0033] Specifically, the execution data set is used to conduct a reliability analysis of the current operating status of the power communication network, including indicators such as the network's failure rate, fault recovery time, and the impact of faults on network performance. Based on the analysis results, a reliability evaluation result is established to reflect the current stability and reliability level of the network. On the other hand, the prediction data set is used to conduct an uncertainty analysis of the future operating status of the power communication network, including fluctuations in network traffic, the probability of failure, prediction errors in network performance, etc. Based on the analysis results, an uncertainty evaluation result is established to reflect the uncertainty and potential risks of the future state of the network.

[0034] Then, the stability requirements of the power communication network are obtained, that is, according to the operational requirements and safety standards of the power communication network, the stability requirements of the network are determined, including the allowable occurrence rate of faults, the minimum standard of network performance, etc.; finally, the benefit ratio of the execution state and the prediction state is configured according to the stability requirements, reliability evaluation results, and uncertainty evaluation results. The configured benefit ratio reflects the importance of the network to the current performance and future predictions. For example, if the network has high requirements for the stability of the current performance, the benefit ratio of the execution state may be higher; if the network has greater concerns about future uncertainties, the benefit ratio of the prediction state may be higher. Through this process, the power communication network can realize a comprehensive evaluation of the execution state and the prediction state, so as to configure the benefit ratio more reasonably and guide the scheduling optimization decision of the network. This data-driven approach helps to improve the intelligence level of network management and ensure that the network can maintain stable and efficient operation in the face of complex and changing environments.

[0035] Further, a comprehensive evaluation network is established based on the node benefit function and benefit ratio. Step five of this application includes: The calculation layer of the comprehensive evaluation network is configured according to the node benefit function as follows: ;in, Characterize the optimization objective function, Representation weight matrix, is a collection of actions, is the total number of nodes in the power communication network, is the adjustment coefficient used to balance the network performance and weight adjustment cost in the objective function. is the edge set between nodes, which is used to characterize the node pairs that may have a collaborative relationship in the power communication network. is a cost function for weight adjustment, which represents the cost required to adjust the collaborative weight during the optimization process; a proportional collaborative layer is established based on the profit ratio, and a comprehensive evaluation network is established based on the proportional collaborative layer and the calculation layer.

[0036] Specifically, first, the computing layer of the comprehensive evaluation network is configured according to the node benefit function, where the computing layer is embedded with an optimization function. In the optimization function expression, Characterize the optimization objective function, which is the core of power communication network dispatch optimization and reflects the optimization goal of network performance and efficiency; Representation weight matrix, is a collection of actions, is the total number of nodes in the power communication network, is the adjustment coefficient used to balance the network performance and weight adjustment cost in the objective function. is the edge set between nodes, which is used to characterize the node pairs that may have a collaborative relationship in the power communication network. The cost function for weight adjustment characterizes the cost required to adjust the coordination weight during the optimization process. By constructing the optimization objective function, the power communication network scheduling optimization can more accurately evaluate and optimize the performance and efficiency of the network, and provide support for optimizing the coordination effect of the power communication network.

[0037] A proportional coordination layer is established based on the profit ratio. The proportional coordination layer reflects the importance the network attaches to current performance and future predictions. The elements of the proportional coordination layer represent the coordination weights of two nodes, which can be adjusted according to the profit ratio. Finally, a comprehensive evaluation network is established based on the proportional coordination layer and the calculation layer.

[0038] Furthermore, based on the optimization results, a node action and collaborative weight allocation scheme is established, as shown in the attached Figure 2 As shown, step five of this application includes: Acquire a node historical data set of the power communication network, and establish a node state evaluation model with the node historical data set; perform state analysis of each node based on the prediction data set and the state evaluation model to generate a first state analysis result; perform execution state prediction of each node based on the optimization result and the state evaluation model to establish a second state analysis result; generate optimization feedback according to the first state analysis result and the second state analysis result, and perform optimization result compensation with the optimization feedback.

[0039] Specifically, the operation history data of each node in the power communication network is collected to obtain the node history data set, which includes network traffic, node load, delay, failure and energy consumption, etc. Then, a node status evaluation model is established based on the node history data set. The status evaluation model can quantify the value and contribution of the node and reflect its importance in the network. First, according to the goal of the evaluation model, a suitable model is selected, such as linear regression, support vector machine, random forest, etc., considering the model's prediction ability and computational complexity, as well as the model's interpretability. Then, the selected model is supervised and trained using historical data to learn the patterns and laws in the data and obtain a status evaluation model.

[0040] Then, the predicted data set is input into the state evaluation model to perform state analysis of each node, and a first state analysis result is generated to reflect the predicted state of the node; on the other hand, the optimization result is input into the state evaluation model to perform state analysis of each node, and a second state analysis result is output, that is, the predicted state of the optimization result is obtained; then, deviation analysis is performed based on the first state analysis result and the second state analysis result, and optimization feedback is generated based on the deviation analysis result. The optimization feedback should include adjustment suggestions for the existing scheduling strategy to reduce the deviation; finally, the optimization result is compensated with the optimization feedback, such as adjusting the node action, resource allocation, scheduling strategy, etc. in the optimization result according to the optimization feedback. By executing optimization feedback to compensate for the optimization result, the accuracy of the optimization result can be improved, efficient management of resources can be achieved, and the network can be ensured to maintain optimal performance when facing different operating conditions.

[0041] Further, according to the first state analysis result and the second state analysis result, the optimization feedback is generated, and the present application further includes the following steps: Performing a difference analysis of the first state analysis result and the second state analysis result at the same node to establish a difference penalty; performing a state threshold judgment on the second state analysis result, and generating a state threshold trigger penalty based on the state threshold judgment result; and generating the optimization feedback according to the difference penalty and the state threshold trigger penalty.

[0042] Specifically, first, the data of the same node in the first state analysis result and the second state analysis result are compared, the difference between the two analysis results is identified and quantified, and the difference analysis result is obtained; then, according to the result of the difference analysis, a difference penalty mechanism is established, including adjusting weights, increasing adjustment costs, limiting certain operations, etc. The purpose of the difference penalty is to encourage network managers to reduce the difference between prediction and reality during the adjustment process. On the other hand, the state threshold is judged for the second state analysis result to determine the key threshold of network performance, such as the maximum allowed delay, the minimum available bandwidth, etc., and then based on the state threshold judgment result, the state threshold trigger penalty is generated; finally, the optimization feedback is generated according to the difference penalty and the state threshold trigger penalty, wherein the optimization feedback includes adjustment suggestions for the existing scheduling strategy to reduce differences and avoid state threshold triggering.

[0043] Furthermore, the present application also includes the following steps: A temporary backtracking window is established, collaborative backtracking data is collected based on the temporary backtracking window, and node collaborative taboos are generated; the node collaborative taboos are used to constrain the node collaborative relationship, and the node collaborative weights are reconstructed.

[0044] Specifically, a temporary backtracking window is established, that is, a time window is defined for backtracking analysis. The temporary backtracking window can be a fixed time period or can be adjusted dynamically as needed; then, collaborative backtracking data collection is performed based on the temporary backtracking window to obtain a collaborative backtracking data set, that is, within the temporary backtracking window, the collaborative relationship data between nodes in the network is collected, including communication traffic, data packet exchange, task collaboration records, etc. between nodes; then, node collaborative taboos are generated based on the collaborative backtracking data set, that is, the collaborative backtracking data is analyzed, node pairs or collaborative relationships that may have problems are identified, and node collaborative taboos are generated. The node collaborative taboos represent the prohibition or restriction of collaborative operations between certain nodes within a certain period of time. Node collaborative taboos are further introduced into the node collaborative relationship as constraints to ensure that node collaborative taboos are avoided in collaborative operations; finally, according to the node collaborative taboos, the original node collaborative weights are adjusted, and the reconstructed node collaborative weights reflect the restriction and optimization of the collaborative relationship. Through this process, the power communication network can better manage the collaborative relationship between nodes, avoid potential problems, and optimize the use of network resources.

[0045] In summary, the power communication network scheduling optimization method based on AI prediction provided by this application has the following technical effects: By utilizing the monitoring sensor group to perform node monitoring of the power communication network, the real-time monitoring results are used as the execution data set, and the real-time monitoring results are used as the benchmark data, and the node state prediction is performed based on the node prediction model to establish a prediction data set; then the node connectivity data is read, the node coordination relationship is called with the connectivity data, the node coordination weight is established, and the node benefit function is configured with the node coordination weight; the benefit ratio of the execution state and the prediction state is further configured, and a comprehensive evaluation network is established based on the node benefit function and the benefit ratio, and the execution data set and the prediction data set are used as input data. The comprehensive evaluation network is used to optimize the coordination effect of the power communication network, and a node action and coordination weight allocation scheme is established based on the optimization result; finally, the power communication network scheduling is optimized through the node action and coordination weight allocation scheme; the timeliness and accuracy of resource regulation in the power communication network can be improved, so that the power communication network can better adapt to the changing environment and communication needs, so as to improve the reliability and efficiency of the entire power communication network, and ensure the stability of power communication services and high-quality technical effects.

[0046] Embodiment 2, based on the power communication network scheduling optimization method based on AI prediction in the previous embodiment, the same inventive concept, this application also provides a power communication network scheduling optimization device based on AI prediction, please refer to the attached Figure 3 ,include: A node data set establishment module 11 is used to deploy a monitoring sensor group at a node of the power communication network, and to establish a node data set with the monitoring sensor group. Each node data in the node data set includes network traffic data, node load data, delay data, fault data, and energy consumption data. A node prediction model establishment module 12 is used to synchronously obtain external environment data. The external environment data includes weather environment data and task data, and to establish a node prediction model based on the external environment data and the node data set. The node prediction model corresponds to the node of the power communication network one by one. A node state prediction module 13 is used to perform node monitoring of the power communication network, and to use the real-time monitoring results as the execution data set, and the real-time monitoring results as the benchmark data. The point prediction model predicts the node state and establishes a prediction data set; the node benefit function configuration module 14 is used to read the node connectivity data, call the node coordination relationship with the connectivity data, establish the node coordination weight, and configure the node benefit function with the node coordination weight; the coordination effect optimization module 15 is used to configure the benefit ratio of the execution state and the prediction state, establish a comprehensive evaluation network based on the node benefit function and the benefit ratio, use the execution data set and the prediction data set as input data, use the comprehensive evaluation network to optimize the coordination effect of the power communication network, and establish the node action and coordination weight allocation plan based on the optimization result; the network scheduling optimization module 16 is used to optimize the power communication network scheduling through the node action and coordination weight allocation plan.

[0047] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: Establish node collaboration weights, the formula is as follows: ;in, Representing any two nodes and The collaborative weight of Representing any two nodes and communication capacity, Representing any two nodes and The communication delay, Representing any two nodes and Communication quality parameters, It is a comprehensive function; configure the node benefit function as follows: ;in, Characterization The node profit function of nodes is Characterization Node Actions, Characterization Processing Node The action set of other nodes, Characterization Node and The benefit function generated by the synergy between them.

[0048] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: The calculation layer of the comprehensive evaluation network is configured according to the node benefit function as follows: ;in, Characterize the optimization objective function, Representation weight matrix, is a collection of actions, is the total number of nodes in the power communication network, is the adjustment coefficient used to balance the network performance and weight adjustment cost in the objective function. is the edge set between nodes, which is used to characterize the node pairs that may have a collaborative relationship in the power communication network. is a cost function for weight adjustment, which represents the cost required to adjust the collaborative weight during the optimization process; a proportional collaborative layer is established based on the profit ratio, and a comprehensive evaluation network is established based on the proportional collaborative layer and the calculation layer.

[0049] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: Acquire a node historical data set of the power communication network, and establish a node state evaluation model with the node historical data set; perform state analysis of each node based on the prediction data set and the state evaluation model to generate a first state analysis result; perform execution state prediction of each node based on the optimization result and the state evaluation model to establish a second state analysis result; generate optimization feedback according to the first state analysis result and the second state analysis result, and perform optimization result compensation with the optimization feedback.

[0050] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: Performing a difference analysis of the first state analysis result and the second state analysis result at the same node to establish a difference penalty; performing a state threshold judgment on the second state analysis result, and generating a state threshold trigger penalty based on the state threshold judgment result; and generating the optimization feedback according to the difference penalty and the state threshold trigger penalty.

[0051] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: Perform reliability analysis on the execution state based on the execution data set to establish a reliability evaluation result; perform uncertainty analysis on the prediction state based on the prediction data set to establish an uncertainty evaluation result; obtain the stability requirement of the power communication network, and configure the profit ratio according to the stability requirement, reliability evaluation result, and uncertainty evaluation result.

[0052] Furthermore, the power communication network dispatch optimization device based on AI prediction is also used for: A temporary backtracking window is established, collaborative backtracking data is collected based on the temporary backtracking window, and node collaborative taboos are generated; the node collaborative taboos are used to constrain the node collaborative relationship, and the node collaborative weights are reconstructed.

[0053] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The power communication network scheduling optimization method based on AI prediction and the specific examples in the aforementioned embodiment 1 are also applicable to the power communication network scheduling optimization device based on AI prediction in this embodiment. Through the aforementioned detailed description of the power communication network scheduling optimization method based on AI prediction, those skilled in the art can clearly know the power communication network scheduling optimization device based on AI prediction in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method description.

[0054] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A power communication network dispatch optimization method based on AI prediction, characterized in that: include: Deploy a monitoring sensor group at a node of the electric power communication network, and establish a node data set with the monitoring sensor group, wherein each node data in the node data set includes network traffic data, node load data, delay data, fault data and energy consumption data; Synchronously acquiring external environment data, the external environment data including weather environment data and task data, and establishing a node prediction model according to the external environment data and the node data set, wherein the node prediction model corresponds one-to-one to the power communication network node; Perform node monitoring of the power communication network, use the real-time monitoring results as the execution data set, and use the real-time monitoring results as the benchmark data to perform node state prediction based on the node prediction model to establish a prediction data set; Reading the connectivity data of the node, calling the node coordination relationship with the connectivity data, establishing the node coordination weight, and configuring the node benefit function with the node coordination weight; Configure the profit ratio of the execution state and the prediction state, establish a comprehensive evaluation network based on the node profit function and the profit ratio, use the execution data set and the prediction data set as input data, use the comprehensive evaluation network to optimize the synergy effect of the power communication network, and establish a node action and synergy weight allocation plan based on the optimization result; The power communication network scheduling is optimized through the node action and collaborative weight allocation scheme.

2. The power communication network scheduling optimization method based on AI prediction according to claim 1 is characterized in that: The reading of the connectivity data of the node, calling the node coordination relationship with the connectivity data, establishing the node coordination weight, and configuring the node benefit function with the node coordination weight also includes: Establish node collaboration weight, the formula is as follows: ； in, Representing any two nodes and The collaborative weight of Representing any two nodes and communication capacity, Representing any two nodes and The communication delay, Representing any two nodes and Communication quality parameters, is a comprehensive function; Configure the node profit function as follows: ； in, Characterization The node profit function of nodes is Characterization Node Actions, Characterization Processing Node The action set of other nodes, Characterization Node and The benefit function generated by the synergy between them.

3. The power communication network scheduling optimization method based on AI prediction according to claim 2 is characterized in that: The establishing of a comprehensive evaluation network based on the node benefit function and benefit ratio also includes: The calculation layer of the comprehensive evaluation network is configured according to the node benefit function as follows: ； in, Characterize the optimization objective function, Representation weight matrix, is a collection of actions, is the total number of nodes in the power communication network, is the adjustment coefficient used to balance the network performance and weight adjustment cost in the objective function. is the edge set between nodes, which is used to characterize the node pairs that may have a collaborative relationship in the power communication network. is the cost function for weight adjustment, which represents the cost required to adjust the collaborative weights during the optimization process; A proportional coordination layer is established based on the income ratio, and a comprehensive evaluation network is established based on the proportional coordination layer and the calculation layer.

4. The power communication network scheduling optimization method based on AI prediction according to claim 1 is characterized in that: The node action and collaborative weight distribution scheme is established based on the optimization result, and further includes: Acquire a node historical data set of the power communication network, and establish a node status evaluation model based on the node historical data set; Performing a state analysis of each node based on the prediction data set and the state evaluation model to generate a first state analysis result; Predicting the execution state of each node based on the optimization result and the state evaluation model, and establishing a second state analysis result; Optimization feedback is generated according to the first state analysis result and the second state analysis result, and optimization result compensation is performed using the optimization feedback.

5. The power communication network scheduling optimization method based on AI prediction according to claim 4 is characterized in that: The generating optimization feedback according to the first state analysis result and the second state analysis result also includes: Performing a same-node difference analysis on the first state analysis result and the second state analysis result, and establishing a difference penalty; Performing state threshold determination on the second state analysis result, and generating a state threshold trigger penalty based on the state threshold determination result; The optimization feedback is generated according to the difference penalty and the state threshold trigger penalty.

6. The power communication network dispatch optimization method based on AI prediction according to claim 1, characterized in that: The profit ratio of the configuration execution state and the prediction state also includes: Performing reliability analysis on the execution status based on the execution data set to establish a reliability evaluation result; Performing uncertainty analysis on the predicted state based on the predicted data set to establish an uncertainty evaluation result; The stability requirement of the electric power communication network is obtained, and the profit ratio is configured according to the stability requirement, the reliability evaluation result, and the uncertainty evaluation result.

7. The method for optimizing power communication network scheduling based on AI prediction according to claim 1, characterized in that: Also includes: Establishing a temporary backtracking window, performing collaborative backtracking data collection based on the temporary backtracking window, and generating node collaborative taboos; The node collaboration taboos are used to constrain the node collaboration relationship and reconstruct the node collaboration weights.

8. Power communication network dispatch optimization device based on AI prediction, characterized in that: The steps for implementing the power communication network dispatch optimization method based on AI prediction as described in any one of claims 1 to 7 include: A node data set establishment module, used to deploy a monitoring sensor group at a node of the electric power communication network, and establish a node data set with the monitoring sensor group, wherein each node data in the node data set includes network flow data, node load data, delay data, fault data and energy consumption data; A node prediction model establishment module is used to synchronously acquire external environment data, the external environment data includes weather environment data and task data, and establish a node prediction model according to the external environment data and the node data set, wherein the node prediction model corresponds to the power communication network node one by one; A node state prediction module is used to perform node monitoring of the power communication network, use the real-time monitoring results as the execution data set, and use the real-time monitoring results as the benchmark data to perform node state prediction based on the node prediction model and establish a prediction data set; A node benefit function configuration module is used to read the connectivity data of the node, call the node coordination relationship with the connectivity data, establish the node coordination weight, and configure the node benefit function with the node coordination weight; A synergy effect optimization module is used to configure the profit ratio of the execution state and the prediction state, establish a comprehensive evaluation network based on the node profit function and the profit ratio, use the execution data set and the prediction data set as input data, use the comprehensive evaluation network to optimize the synergy effect of the power communication network, and establish a node action and synergy weight allocation plan based on the optimization result; The network scheduling optimization module is used to optimize the scheduling of the power communication network through the node action and collaborative weight allocation scheme.

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