A planning optimization system for distribution network maintenance

Abstract

The application discloses a kind of plan optimization systems of distribution network maintenance, it is related to power grid management technical field, including intelligent maintenance dispatching optimization module, equipment monitoring and prediction module, panoramic maintenance plan simulation module, dynamic risk assessment module, inspection robot management module.Intelligent maintenance dispatching optimization module is used to analyze distribution network data, equipment monitoring and prediction module is used to monitor distribution network equipment operating state, and future fault occurrence time and position are predicted.Panoramic maintenance plan simulation module carries out dynamic simulation to maintenance scheme.The application introduces multiple models, effectively overcomes the deficiency of traditional technology in flexibility, real-time and accuracy, so that the system has adaptive learning and dynamic adjustment capability, can quickly respond to failure in complex and changeable working environment, optimize resource scheduling, and significantly reduce maintenance cost and time, the system greatly improves the stability and reliability of distribution network equipment, provides solid technical support for intelligent power operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of power grid management technology, specifically to a distribution network maintenance planning optimization system. Background Technology

[0002] In the operation and maintenance of power distribution networks, traditional maintenance plans often rely on manual experience and regular maintenance strategies. This approach lacks flexibility in dealing with sudden faults, leading to long fault location and repair times, which in turn affects power supply reliability and customer experience. In recent years, with the development of big data, the Internet of Things (IoT), machine learning, and artificial intelligence technologies, the intelligent and automated maintenance of power distribution networks has gradually become a research hotspot. However, existing technologies still have shortcomings in many aspects.

[0003] Traditional maintenance scheduling systems typically use fixed rules or static algorithms for task allocation, making it difficult to adapt to complex and ever-changing working environments in real time.

[0004] Most current predictive maintenance systems rely on monitoring mechanisms with fixed thresholds, triggering alarms only when equipment conditions exceed predetermined ranges. This approach is lagging in detecting sudden failures and cannot provide accurate fault prediction.

[0005] Most existing risk assessment methods adopt static risk level classification, which has low sensitivity to changes in the external environment and is difficult to respond quickly in case of emergencies. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the present invention provides a distribution network maintenance planning optimization system that can solve the problems mentioned in the background art.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a distribution network maintenance planning optimization system, comprising: an intelligent maintenance scheduling optimization module for analyzing distribution network data and optimizing the scheduling of maintenance tasks; an equipment monitoring and prediction module, which, based on sensor networks and an IoT platform, monitors the operating status of distribution network equipment in real time and uses a predictive maintenance model to predict the time and location of future faults; a panoramic maintenance planning simulation module, which provides maintenance personnel with a maintenance planning simulation environment through virtual reality technology and simulation models, and dynamically simulates different maintenance schemes; a dynamic risk assessment module, which assesses risks during maintenance and dynamically adjusts the risk level according to real-time conditions, and, based on a risk assessment model, models the probability of equipment failure, the operational proficiency of maintenance personnel, and variables of external environmental factors, and calculates the risk level in real time; and an inspection robot management module, which controls inspection robots deployed at the distribution network site, using machine vision and deep learning algorithms, enabling the robots to autonomously inspect without human intervention, automatically identify equipment faults and potential risks, and transmit the data to the planning optimization system in real time.

[0009] As a preferred embodiment of the distribution network maintenance planning optimization system described in this invention, the distribution network data includes historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status data.

[0010] As a preferred embodiment of the distribution network maintenance planning optimization system described in this invention, the intelligent maintenance scheduling optimization module optimizes resource allocation, fault priority, and cost-effectiveness based on a scheduling model using machine learning algorithms, and employs an adaptive neural network optimization algorithm to learn and improve itself according to actual maintenance conditions.

[0011] The scheduling model, operating in a dynamic environment, learns from historical maintenance data of the distribution network and the current equipment status to minimize maintenance time and cost while maximizing task completion rate and equipment health. Specifically, it includes the following steps:

[0012] Define the variables and parameters of the scheduling model;

[0013] Define a multi-objective optimization function As the objective of the scheduling model, the formula is:

[0014] ;

[0015] in, Indicates the first The estimated completion time for each maintenance task; Indicates the first The cost of a maintenance task includes labor costs and equipment usage costs; Indicates the first The probability of failure for each maintenance task; Indicates the first The location weight of each maintenance task is set according to the geographical location and importance of the task location; Indicates the first The number of resources required for each task includes the number of maintenance teams and the number of equipment. S i This represents the device's health score, ranging from 0 to 1. The closer to 1, the healthier the device. These represent the weights of time, cost, failure probability, and equipment health score, respectively.

[0016] The weight parameters are dynamically adjusted based on historical data and the current system status. The weight adjustment is performed using the following formula:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] in, It is an adaptive adjustment coefficient, and its value range is... ; These are the deviations of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high.

[0022] Based on the above adjustments, a dynamic optimization scheduling strategy is implemented to maintain the optimal state under different circumstances.

[0023] As a preferred embodiment of the distribution network maintenance planning optimization system described in this invention, the adaptive neural network optimization algorithm incorporates an optimization model suitable for distribution network maintenance tasks. By adjusting network weights and learning rates in real time, it intelligently optimizes maintenance task scheduling based on the current state of the distribution network and changes in the external environment.

[0024] The optimization model includes a forward propagation formula, a dynamic learning rate adjustment formula, a weight and bias update formula, and a reinforcement learning feedback mechanism.

[0025] The forward propagation formula uses the input feature vector Calculate network output :

[0026] ;

[0027] in, For the first The output of the layer, It is the total number of layers in the neural network. For the first The layer output is the weight matrix for the neurons in the l-th layer. This is the bias vector added to the linearly transformed layer l.

[0028] The formula for adjusting the dynamic learning rate is shown below:

[0029] ;

[0030] in, This represents the rate of change of error between the network's prediction and the actual result. By adjusting the learning rate, This is the dynamic learning rate.

[0031] The weight and bias update formula uses a weight and bias update mechanism with an adaptive adjustment factor, as shown below:

[0032] ;

[0033] in, and These are the loss functions. The gradient of the weights and biases, These are parameters used for weight regularization, which maintains the sparsity of weights through L1 regularization. loss function The overall change in error in the current training round. For the first Layer output for the first l The weight matrix of layer neurons.

[0034] A reinforcement learning feedback mechanism is introduced, employing reinforcement learning algorithms to optimize task scheduling strategies.

[0035] ;

[0036] in, It is a state Take action below Value assessment It's the learning rate. This is the current reward. It is a discount factor. For the next state Under these circumstances, the system schedules all available actions. The highest expected return that can be achieved.

[0037] As a preferred embodiment of the distribution network maintenance planning optimization system described in this invention, the optimization model includes the following steps when in use: inputting distribution network data and converting it into feature vectors. The optimization model is input; in each layer of the neural network, forward propagation calculations are performed layer by layer according to the formula. The output of each layer is passed to the next layer after passing through the activation function, and finally outputs the priority and resource allocation suggestions for the maintenance task; during training, the learning rate is dynamically adjusted according to the current error and changes in the external environment, and fine-tuning is maintained in a stable state to avoid over-adjustment; using the gradient information of the forward propagation error, combined with the adaptive adjustment mechanism, the weights and biases of the neural network are updated in real time, and the adaptive adjustment factor is used to adjust the weights and biases of the neural network. This system controls the sensitivity to environmental changes, thereby enabling efficient decision-making in task scheduling. By utilizing the feedback results of the current scheduling decisions, the system continuously optimizes the scheduling strategy. Based on the current task status and the adopted scheduling strategy, the system adjusts the output of the neural network, making each scheduling choice tend towards the global optimum. The neural network outputs the priority of the maintenance task and the optimal scheduling scheme. The system allocates resources according to the scheduling scheme and feeds the results back into the model for further reinforcement learning and optimization.

[0038] As a preferred embodiment of the planned optimization system for distribution network maintenance described in this invention, the predictive maintenance model includes a hidden state update formula, a time-aware adjustment formula, an output prediction formula, and a health status score.

[0039] The hidden state update formula uses an LSTM structure with a time-aware factor to calculate the current hidden state. Based on the influence of input characteristics and environmental factors:

[0040] ;

[0041] in, This represents the weight of the hidden state in the previous time step. The weights of the input features, Assigning weights to environmental variables, and dynamically adjusting the internal representation of device status based on historical data and the current external environment. Indicates time A set of device operation characteristics collected in real time. Indicates time t External environmental variables that constantly affect the operating status of equipment. It is the bias vector of the hidden layer.

[0042] The time-aware adjustment formula enhances the model's adaptability across different time periods by introducing a time-aware coefficient to dynamically adjust the weights for updating hidden states based on historical data trends.

[0043] ;

[0044] in, This is the time difference between the current time and the target prediction time. When the target prediction time is far in the future, the model focuses more on historical data; when the target time is closer, the model focuses more on the current state and short-term trends. The weight of hidden state updates is dynamically adjusted based on the trend of historical data.

[0045] The output prediction formula is based on the hidden state. Calculate the predicted value of the equipment health status. :

[0046] ;

[0047] in, This is the sigmoid function, used to map the output to the (0,1) interval, representing the probability of the device failing in the future. Hidden state vector Mapping to the final prediction result The weight matrix, To increase the bias in the linear combination result of the output layer.

[0048] The health status score is used to quantify the health of the equipment and provide a predicted time of failure:

[0049] ;

[0050] in, The closer the score is to 100, the healthier the equipment is; conversely, the lower the score, the higher the risk of failure.

[0051] As a preferred embodiment of the distribution network maintenance planning optimization system described in this invention, the risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network, which dynamically updates the conditional probabilities according to real-time conditions, continuously updating the conditional probabilities of each node during the maintenance process to reflect the real-time changes in equipment status and external environment; the risk assessment model includes a joint probability distribution function, a conditional probability update formula for risk factors, and a risk score calculation formula.

[0052] The joint probability distribution function, risk level It is the joint probability function of multiple condition variables:

[0053] ;

[0054] in, This indicates the overall risk level of the system under its current maintenance condition. This indicates the probability that the target device will fail under the current operating conditions. This indicates the skill level of the personnel involved in the maintenance. This indicates the safety risk status of the external environment during the maintenance period. This indicates other risk factors related to the maintenance task. Indicates an event Under conditions The probability of the following occurring.

[0055] The conditional probability update formula for the risk factor is as follows:

[0056] ;

[0057] in, The current risk level. It represents the risk level at the previous moment. This indicates the latest sensor data or maintenance information. Given the current risk level, what is the probability of the occurrence of various monitoring data collected by the sensors in real time? To avoid considering any specific risk level, the probability of the current monitoring data appearing as a whole is calculated only from a statistical perspective.

[0058] The risk scoring formula will determine the overall risk level. Transform it into a visual rating system:

[0059] ;

[0060] in, It is an adjustment coefficient used to control the sensitivity of risk scoring, standardizing the risk level to a value between 0 and 100, and visualizing the degree of risk.

[0061] To further address the aforementioned technical problems, this invention provides the following technical solution: a method for optimizing the planning of distribution network maintenance, comprising:

[0062] Collect power distribution network data, which includes historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status data;

[0063] Based on the power distribution network data, a scheduling model is constructed using an adaptive neural network optimization algorithm to generate a preliminary maintenance plan with the goal of minimizing maintenance time and cost while maximizing task completion rate and equipment health.

[0064] Using a predictive maintenance model based on LSTM architecture, the system predicts the time and location of potential equipment failures based on real-time monitored equipment operating status data, and dynamically updates the priority of maintenance plans.

[0065] A dynamic adaptive Bayesian network is used to assess the risk of maintenance plans. The risk level is calculated based on the probability of equipment failure, the proficiency of maintenance personnel, and external environmental factors, and a risk score is generated.

[0066] The maintenance plan is adjusted based on the risk score, and the inspection robot is controlled to perform autonomous inspections. The machine vision and deep learning algorithms are used to identify equipment faults and potential risks, and the data is fed back in real time to optimize the maintenance plan.

[0067] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the distribution network maintenance planning optimization system as described above.

[0068] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the planned optimization system for distribution network maintenance as described above.

[0069] The beneficial effects of this invention are as follows: By introducing multiple models, this invention effectively overcomes the shortcomings of traditional technologies in terms of flexibility, real-time performance, and accuracy, enabling the system to have adaptive learning and dynamic adjustment capabilities. It can quickly respond to faults and optimize resource scheduling in complex and ever-changing working environments, and significantly reduce maintenance costs and time. This system greatly improves the stability and reliability of distribution network equipment, providing solid technical support for intelligent power operation and maintenance. Attached Figure Description

[0070] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a schematic diagram of the structure of a distribution network maintenance planning optimization system proposed in this invention;

[0072] Figure 2 This is a flowchart illustrating the scheduling model in a planned optimization system for distribution network maintenance proposed in this invention.

[0073] Figure 3 This is a schematic diagram illustrating the composition and process of the optimization model in a planned optimization system for distribution network maintenance proposed in this invention.

[0074] Figure 4 This is a schematic diagram illustrating the composition and process of a predictive maintenance model in a distribution network maintenance planning optimization system proposed in this invention.

[0075] Figure 5 This is a schematic diagram illustrating the composition and process of a risk assessment model in a distribution network maintenance planning optimization system proposed in this invention. Detailed Implementation

[0076] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0077] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0078] Example 1, referring to Figure 1 As an embodiment of the present invention, a distribution network maintenance planning optimization system is provided.

[0079] This application provides a system that can effectively solve the problems mentioned above. The following will describe in detail how to implement this power distribution network maintenance planning optimization system using multiple embodiments. The system includes:

[0080] The intelligent maintenance scheduling optimization module is used to analyze power distribution network data and optimize the scheduling of maintenance tasks.

[0081] Specifically, power distribution network data includes historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status data.

[0082] Furthermore, the intelligent maintenance scheduling optimization module uses a scheduling model based on machine learning algorithms to optimize resource allocation, fault priority, and cost-effectiveness. It also employs an adaptive neural network optimization algorithm to learn and improve itself based on actual maintenance conditions.

[0083] The scheduling model, operating in a dynamic environment, learns from historical maintenance data of the distribution network and the current equipment status to minimize maintenance time and cost while maximizing task completion rate and equipment health. Specifically, it includes the following steps:

[0084] Define the variables and parameters of the scheduling model, including:

[0085] Indicates the first The estimated completion time for each maintenance task;

[0086] Indicates the first The cost of a maintenance task includes labor costs and equipment usage costs.

[0087] Indicates the first The probability of failure for each maintenance task;

[0088] Indicates the first The location weight of each maintenance task is set according to the geographical location and importance of the task location;

[0089] Indicates the first The number of resources required for each task includes the number of maintenance teams and the number of equipment.

[0090] This represents the device's health score, ranging from 0 to 1. The closer to 1, the healthier the device.

[0091] These represent the weights of time, cost, failure probability, and equipment health score, respectively.

[0092] Define a multi-objective optimization function As the objective of the scheduling model, the formula is:

[0093] ;

[0094] in, Indicates the first The estimated completion time for each maintenance task; Indicates the first The cost of a maintenance task includes labor costs and equipment usage costs. Indicates the first The probability of failure for each maintenance task; Indicates the first The location weight of each maintenance task is set according to the geographical location and importance of the task location; Indicates the first The number of resources required for each task includes the number of maintenance teams and the number of equipment. S i This represents the device's health score, ranging from 0 to 1. The closer to 1, the healthier the device. These represent the weights of time, cost, failure probability, and equipment health score, respectively.

[0095] To minimize the time cost of repair tasks, the geographical location factor is taken into account. If the location of a task is more important, i.e., has a higher weight, the model will prioritize it to reduce the total time.

[0096] To minimize costs, considering the amount of resources required, maintenance costs are reduced by optimizing resource efficiency.

[0097] This is used to maximize task completion rates and improve overall network reliability by reducing the impact of device failure probability.

[0098] This is used to maximize the health status of equipment, prioritize the scheduling of critical equipment that has a greater impact on the system, and improve the overall health score of equipment.

[0099] The weight parameters are dynamically adjusted based on historical data and the current system status. The weight adjustment is performed using the following formula:

[0100] ;

[0101]

[0102] ;

[0103] ;

[0104] in, It is an adaptive adjustment coefficient, and its value range is... ; These are the deviations of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high.

[0105] Based on the above adjustments, a dynamic optimization scheduling strategy is implemented to maintain the optimal state under different circumstances.

[0106] Furthermore, the adaptive neural network optimization algorithm incorporates an optimization model suitable for distribution network maintenance tasks. By adjusting network weights and learning rates in real time, it intelligently optimizes maintenance task scheduling based on the current state of the distribution network and changes in the external environment.

[0107] The optimized model includes the forward propagation formula, the dynamic learning rate adjustment formula, the weight and bias update formula, and the reinforcement learning feedback mechanism.

[0108] First, define the variables and parameters of the optimization model, including:

[0109] The input feature vector includes historical maintenance data, equipment failure probability, equipment health status, and geographical location;

[0110] The output vector represents the priority, resource allocation, and estimated completion time of each maintenance task;

[0111] For the first The weight matrix of a layered neural network;

[0112] For the first Bias vector of a layered neural network;

[0113] For the first Activation functions of layered neural networks;

[0114] The learning rate is dynamic, changing over time. Adjust according to changes;

[0115] and The adjustment amount for weights and biases;

[0116] This is an adaptive adjustment coefficient used to control the network's response speed to changes in the external environment.

[0117] The forward propagation formula uses the input feature vector Calculate network output :

[0118] ;

[0119] in, For the first The output of the layer, It is the total number of layers in the neural network. For the first The layer output is the weight matrix for the neurons in the l-th layer. The bias vector added to the linearly transformed layer l;

[0120] The formula for adjusting the dynamic learning rate is shown below:

[0121] ;

[0122] in, This represents the rate of change of error between the network's prediction and the actual result. By adjusting the learning rate, To adjust the coefficient for the magnitude of change in the learning rate, The learning rate is dynamic.

[0123] The weight and bias update formula uses a weight and bias update mechanism with an adaptive adjustment factor, as shown below:

[0124] ;

[0125] in, and These are the loss functions. The gradient of the weights and biases, These are parameters used for weight regularization, which maintains the sparsity of weights through L1 regularization. loss function The overall change in error in the current training round. For the first Layer output for the first The weight matrix of layer neurons;

[0126] A reinforcement learning feedback mechanism is introduced, employing reinforcement learning algorithms to optimize task scheduling strategies.

[0127] ;

[0128] in, It is a state Take action below Value assessment It's the learning rate. This is the current reward. It is a discount factor. For the next state Under these circumstances, the system schedules all available actions. The highest expected return that can be achieved.

[0129] Reinforcement learning mechanisms improve the accuracy of task priority decisions by interacting with the environment and updating scheduling strategies in real time.

[0130] The optimization model involves the following steps when used:

[0131] Data preprocessing and input: Input distribution network data and convert it into feature vectors. Input the optimization model;

[0132] Adaptive forward propagation: In each layer of the neural network, the forward propagation calculation is performed layer by layer according to the formula. The output of each layer is passed to the next layer after passing through the activation function, and finally outputs the priority of the maintenance task and resource allocation suggestions.

[0133] Dynamic learning rate adjustment: During training, the learning rate is dynamically adjusted based on the current error and changes in the external environment. In a stable state, it is kept fine-tuned to avoid over-adjustment.

[0134] Adaptive update of weights and biases: Utilizing the gradient information from the forward propagation error, combined with an adaptive adjustment mechanism, the weights and biases of the neural network are updated in real time through adaptive adjustment factors. This allows for control over sensitivity to environmental changes, thereby enabling efficient decision-making in task scheduling;

[0135] Reinforcement learning feedback optimization: Utilize the feedback results of the current scheduling decision to continuously optimize the scheduling strategy. Adjust the output of the neural network according to the current task state and the adopted scheduling strategy, so that the scheduling choice at each step tends to the global optimum.

[0136] Output scheduling scheme and implementation: The neural network outputs the priority and optimal scheduling scheme of the maintenance task. The system allocates resources according to the scheduling scheme and feeds the results back to the model for further reinforcement learning and optimization.

[0137] The equipment monitoring and prediction module, based on sensor networks and IoT platforms, monitors the operating status of distribution network equipment in real time and uses predictive maintenance models to predict the time and location of future faults.

[0138] The predictive maintenance model specifies the following variables and parameters:

[0139] Input features for the time series include historical values ​​of sensor data such as vibration, temperature, current, and voltage;

[0140] The output value represents the probability of future equipment health or failure.

[0141] These are environmental factors, such as external temperature, humidity, and workload, that affect the equipment's condition.

[0142] The hidden state represents the model at time. The internal state at any given moment;

[0143] These are the weight matrices for the hidden state, input features, and environmental features, respectively.

[0144] These are the bias terms for the hidden and output layers;

[0145] For activation functions, such as ReLU or sigmoid;

[0146] This is a time-aware adjustment coefficient used to adjust the model's focus on historical data.

[0147] The predictive maintenance model includes a hidden state update formula, a time-aware adjustment formula, an output prediction formula, and a health status score.

[0148] The hidden state update formula uses an LSTM structure with a time-aware factor to compute the current hidden state. Based on the influence of input characteristics and environmental factors:

[0149] ;

[0150] in, This represents the weight of the hidden state in the previous time step. The weights of the input features, Assigning weights to environmental variables, and dynamically adjusting the internal representation of device status based on historical data and the current external environment. Indicates time A set of device operation characteristics collected in real time. Indicates time t External environmental variables that constantly affect the operating status of equipment. It is the bias vector of the hidden layer.

[0151] The time-aware adjustment formula enhances the model's adaptability across different time periods by introducing a time-aware coefficient to dynamically adjust the weights for updating hidden states based on historical data trends.

[0152] ;

[0153] in, This is the time difference between the current time and the target prediction time. When the target prediction time is far in the future, the model focuses more on historical data; when the target time is closer, the model focuses more on the current state and short-term trends. The weight of hidden state updates is dynamically adjusted based on the trend of historical data.

[0154] The output prediction formula is based on the hidden state. Calculate the predicted value of the equipment health status. :

[0155] ;

[0156] in, This is the sigmoid function, used to map the output to the (0,1) interval, representing the probability of the device failing in the future. Hidden state vector Mapping to the final prediction result The weight matrix, To increase the bias in the linear combination result of the output layer.

[0157] Health status scores are used to quantify the health of equipment and provide predicted failure times.

[0158] ;

[0159] in, The closer the score is to 100, the healthier the equipment is; conversely, the lower the score, the higher the risk of failure.

[0160] The panoramic maintenance plan simulation module provides maintenance personnel with a maintenance plan simulation environment through virtual reality technology and simulation models, and dynamically simulates different maintenance plans.

[0161] The dynamic risk assessment module is used to assess risks during maintenance and dynamically adjust the risk level based on real-time conditions. Based on the risk assessment model, it models variables such as equipment failure probability, maintenance personnel's operational proficiency, and external environmental factors, and calculates the risk level in real time.

[0162] The risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network, which dynamically updates the conditional probabilities based on real-time conditions. During the maintenance process, the conditional probabilities of each node are continuously updated to reflect the real-time changes in equipment status and external environment.

[0163] Define the following variables and parameters in the risk assessment model:

[0164] The overall risk level is scored, ranging from 0 to 1;

[0165] For equipment failure probability variables;

[0166] To assess the operational proficiency of the maintenance personnel;

[0167] External environmental factors, such as weather conditions, temperature, and humidity;

[0168] Other relevant conditional variables, such as equipment historical fault records and current equipment load;

[0169] To indicate an event Under conditions The probability of the following occurring.

[0170] The risk assessment model includes a joint probability distribution function, a conditional probability update formula for risk factors, and a risk score calculation formula.

[0171] Joint probability distribution function, risk level It is the joint probability function of multiple condition variables:

[0172] ;

[0173] in, This indicates the overall risk level of the system under its current maintenance condition. This indicates the probability that the target device will fail under the current operating conditions. This indicates the skill level of the personnel involved in the maintenance. This indicates the safety risk status of the external environment during the maintenance period. This indicates other risk factors related to the maintenance task. Indicates an event Under conditions The probability of the following occurring.

[0174] The conditional probability update formula for risk factors is shown below:

[0175] ;

[0176] in, The current risk level. It represents the risk level at the previous moment. This indicates the latest sensor data or maintenance information. Given the current risk level, what is the probability of the occurrence of various monitoring data collected by the sensors in real time? To avoid considering any specific risk level, the probability of the current monitoring data appearing as a whole is calculated only from a statistical perspective.

[0177] The risk scoring formula will determine the overall risk level. Transform it into a visual rating system:

[0178] ;

[0179] in, It is an adjustment coefficient used to control the sensitivity of risk scoring, standardizing the risk level to a value between 0 and 100, and visualizing the degree of risk.

[0180] The inspection robot management module is used to control the inspection robots deployed at the power distribution network site. It adopts machine vision and deep learning algorithms, enabling the robots to conduct autonomous inspections without human intervention, automatically identify equipment faults and potential risks, and transmit the data to the planning optimization system in real time.

[0181] In summary, by introducing multiple models, this invention effectively overcomes the shortcomings of traditional technologies in terms of flexibility, real-time performance, and accuracy. It enables the system to have adaptive learning and dynamic adjustment capabilities, allowing it to quickly respond to faults and optimize resource scheduling in complex and ever-changing working environments. It also significantly reduces maintenance costs and time. This system greatly improves the stability and reliability of distribution network equipment and provides solid technical support for intelligent power operation and maintenance.

[0182] Example 2, an embodiment of the present invention, provides a method for optimizing the planning of distribution network maintenance, comprising:

[0183] S1: Collect data from the power distribution network, including historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status.

[0184] S2: Based on distribution network data, a scheduling model is constructed through an adaptive neural network optimization algorithm to generate a preliminary maintenance plan with the goal of minimizing maintenance time and cost while maximizing task completion rate and equipment health.

[0185] S3: Using a predictive maintenance model based on LSTM structure, based on real-time monitored equipment operating status data, it predicts the time and location of possible equipment failures and dynamically updates the priority of maintenance plans;

[0186] S4: A dynamic adaptive Bayesian network is used to conduct risk assessment of the maintenance plan. The risk level is calculated based on the probability of equipment failure, the proficiency of maintenance personnel, and external environmental factors, and a risk score is generated.

[0187] S5: Adjust the maintenance plan based on the risk score and control the inspection robot to perform autonomous inspections. Identify equipment faults and potential risks through machine vision and deep learning algorithms, and provide real-time feedback data to optimize the maintenance plan.

[0188] Example 3, referring to Figure 2 This is one embodiment of the present invention, which differs from the previous embodiment in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0189] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0190] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0191] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0192] Example 4 is an embodiment of the present invention, which provides a distribution network maintenance planning optimization system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0193]

[0194] As shown in Table 1, in terms of maintenance efficiency, the average response time of this invention is only 18.5 minutes, which is 59.2% shorter than that of the traditional system and 43.4% shorter than that of the simple machine learning system. The maintenance task completion rate reaches 96.8%, which is 11.4 and 7.6 percentage points higher than the other two systems, respectively.

[0195] Fault prediction performance: This invention achieves a fault prediction accuracy of 92.4%, far exceeding the 71.6% of traditional systems and 83.5% of simple machine learning systems. The early warning time reaches 48.5 hours, providing maintenance personnel with ample preparation time.

[0196] Cost control: The average cost per maintenance of this invention is 2,850 yuan, which is 32.5% lower than that of traditional systems and 22.6% lower than that of simple machine learning systems. It also achieves significant results in extending equipment lifespan, with an extension rate of 25.4%.

[0197] System performance: This invention achieves a real-time data processing capacity of 1250 data entries per second, which is 3.3 times that of traditional systems, and the system response time is only 85ms, which greatly improves maintenance efficiency.

[0198] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A planning optimization system for distribution network maintenance, characterized in that, include: The intelligent maintenance scheduling optimization module is used to analyze power distribution network data and optimize the scheduling of maintenance tasks. The equipment monitoring and prediction module, based on sensor networks and IoT platforms, monitors the operating status of distribution network equipment in real time and uses a predictive maintenance model to predict the time and location of future faults. The panoramic maintenance plan simulation module provides maintenance personnel with a maintenance plan simulation environment through virtual reality technology and simulation models, and dynamically simulates different maintenance plans. The dynamic risk assessment module is used to assess risks during maintenance and dynamically adjust the risk level based on real-time conditions. Based on the risk assessment model, it models variables such as equipment failure probability, maintenance personnel's operational proficiency, and external environmental factors, and calculates the risk level in real time. The inspection robot management module is used to control the inspection robots deployed at the power distribution network site. It adopts machine vision and deep learning algorithms, and the robot can carry out autonomous inspections without human operation, automatically identify equipment faults and potential risks, and transmit the data to the planning optimization system in real time. The predictive maintenance model includes a hidden state update formula, a time-aware adjustment formula, an output prediction formula, and a health status score. The hidden state update formula uses an LSTM structure with a time-aware factor to calculate the current hidden state. Based on the influence of input characteristics and environmental factors: ； in, This represents the weight of the hidden state in the previous time step. The weights of the input features, Assigning weights to environmental variables, and dynamically adjusting the internal representation of device status based on historical data and the current external environment. Indicates time A set of device operation characteristics collected in real time. Indicates time t External environmental variables that constantly affect the operating status of equipment. It is the bias vector of the hidden layer; The time-aware adjustment formula enhances the model's adaptability across different time periods by introducing a time-aware coefficient to dynamically adjust the weights for updating hidden states based on historical data trends. ； in, This is the time difference between the current time and the target prediction time. When the target prediction time is far in the future, the model focuses more on historical data; when the target time is closer, the model focuses more on the current state and short-term trends. The weight of hidden state updates is dynamically adjusted based on the trend of historical data; The output prediction formula is based on the hidden state. Calculate the predicted value of the equipment health status. : ； in, This is the sigmoid function, used to map the output to the (0,1) interval, representing the probability of the device failing in the future. Hidden state vector Mapping to the final prediction result The weight matrix, To increase the bias in the linear combination result of the output layer; The health status score is used to quantify the health of the equipment and provide a predicted time of failure: ； in, The closer the score is to 100, the healthier the equipment is; conversely, the lower the score, the higher the risk of failure. The risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network, which dynamically updates the conditional probability according to the real-time situation. During the maintenance process, the conditional probability of each node is continuously updated to reflect the real-time changes in equipment status and external environment. The risk assessment model includes a joint probability distribution function, a conditional probability update formula for risk factors, and a risk score calculation formula. The joint probability distribution function, risk level It is the joint probability function of multiple condition variables: ； in, This indicates the overall risk level of the system under its current maintenance condition. This indicates the probability that the target device will fail under the current operating conditions. This indicates the skill level of the personnel involved in the maintenance. This indicates the safety risk status of the external environment during the maintenance period. This indicates other risk factors related to the maintenance task. Indicates an event Under conditions The probability of the following occurring; The conditional probability update formula for the risk factor is as follows: ； in, The current risk level. It represents the risk level at the previous moment. This indicates the latest sensor data or maintenance information. Given the current risk level, what is the probability of the occurrence of various monitoring data collected by the sensors in real time? To calculate the overall probability of the current monitoring data without considering any specific risk level, only from a statistical perspective; The risk scoring formula will determine the overall risk level. Transform it into a visual rating system: ； in, It is an adjustment coefficient used to control the sensitivity of risk scoring, standardizing the risk level to a value between 0 and 100, and visualizing the degree of risk.

2. The distribution network maintenance planning optimization system as described in claim 1, characterized in that: The power distribution network data includes historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status data.

3. The distribution network maintenance planning optimization system as described in claim 2, characterized in that: The intelligent maintenance scheduling optimization module is based on a scheduling model using machine learning algorithms. It optimizes resource allocation, fault priority, and cost-effectiveness, and uses an adaptive neural network optimization algorithm to learn and improve itself based on actual maintenance conditions. The scheduling model, operating in a dynamic environment, learns from historical maintenance data of the distribution network and the current equipment status to minimize maintenance time and cost while maximizing task completion rate and equipment health. Specifically, it includes the following steps: Define the variables and parameters of the scheduling model; Define a multi-objective optimization function As the objective of the scheduling model, the formula is: ； in, Indicates the first The estimated completion time for each maintenance task; Indicates the first The cost of a maintenance task includes labor costs and equipment usage costs. Indicates the first The probability of failure for each maintenance task; Indicates the first The location weight of each maintenance task is set according to the geographical location and importance of the task location; Indicates the first The number of resources required for each task includes the number of maintenance teams and the number of equipment. S i This indicates the device's health score. These represent the weights of time, cost, failure probability, and equipment health score, respectively; n represents the total number of maintenance tasks to be scheduled. The weight parameters are dynamically adjusted based on historical data and the current system status. The weight adjustment is performed using the following formula: ； ； ； in, It is an adaptive adjustment coefficient, and its value range is... ; These are the deviations of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high. Based on the above adjustments, a dynamic optimization scheduling strategy is implemented to maintain the optimal state under different circumstances.

4. The distribution network maintenance planning optimization system as described in claim 3, characterized in that: The adaptive neural network optimization algorithm has a built-in optimization model suitable for distribution network maintenance tasks. By adjusting the network weights and learning rate in real time, it can intelligently optimize the scheduling of maintenance tasks based on the current state of the distribution network and changes in the external environment. The optimization model includes a forward propagation formula, a dynamic learning rate adjustment formula, a weight and bias update formula, and a reinforcement learning feedback mechanism. The forward propagation formula uses the input feature vector Calculate network output : ； in, For the first The output of the layer, It is the total number of layers in the neural network. For the first The layer output is the weight matrix for the neurons in the l-th layer. The bias vector added to the linearly transformed layer l; The formula for adjusting the dynamic learning rate is shown below: ； in, This represents the rate of change of error between the network's prediction and the actual result. By adjusting the learning rate, To adjust the coefficient for the magnitude of change in the learning rate, The learning rate is dynamic. The weight and bias update formula uses a weight and bias update mechanism with an adaptive adjustment factor, as shown below: ； in, and These are the loss functions. The gradient of the weights and biases, These are parameters used for weight regularization, which maintains the sparsity of weights through L1 regularization. loss function The overall change in error in the current training round. For the first The layer output is the weight matrix for neurons in the l-th layer; A reinforcement learning feedback mechanism is introduced, employing reinforcement learning algorithms to optimize task scheduling strategies. ； in, It is a state Take action below Value assessment It's the learning rate. This is the current reward. It is a discount factor. For the next state Under these circumstances, the system schedules all available actions. The highest expected return that can be achieved.

5. The distribution network maintenance planning optimization system as described in claim 4, characterized in that: The optimization model includes the following steps when in use: Input distribution network data and convert it into a feature vector. Input the optimization model; In each layer of the neural network, the forward propagation calculation is performed layer by layer according to the formula. The output of each layer is passed to the next layer after passing through the activation function, and finally outputs the priority of the maintenance task and resource allocation suggestions. During training, the learning rate is dynamically adjusted based on the current error and changes in the external environment. In a stable state, fine-tuning is maintained to avoid over-adjustment. By utilizing the gradient information of the forward propagation error and combining it with an adaptive adjustment mechanism, the weights and biases of the neural network are updated in real time, through an adaptive adjustment factor. This allows for control over sensitivity to environmental changes, thereby enabling efficient decision-making in task scheduling; By utilizing the feedback results of the current scheduling decision, the scheduling strategy is continuously optimized. Based on the current task status and the adopted scheduling strategy, the output of the neural network is adjusted so that the scheduling choice at each step tends to the global optimum. The neural network outputs the priority and optimal scheduling scheme for maintenance tasks. The system allocates resources according to the scheduling scheme and feeds the results back into the model for further reinforcement learning and optimization.

6. A method for optimizing the planning of distribution network maintenance using the system described in any one of claims 1 to 5, characterized in that, include: Collect power distribution network data, which includes historical maintenance data, equipment failure probability prediction, geographical location, and current equipment health status data; Based on the power distribution network data, a scheduling model is constructed using an adaptive neural network optimization algorithm to generate a preliminary maintenance plan with the goal of minimizing maintenance time and cost while maximizing task completion rate and equipment health. Using a predictive maintenance model based on LSTM architecture, the system predicts the time and location of potential equipment failures based on real-time monitored equipment operating status data, and dynamically updates the priority of maintenance plans. A dynamic adaptive Bayesian network is used to assess the risk of maintenance plans. The risk level is calculated based on the probability of equipment failure, the proficiency of maintenance personnel, and external environmental factors, and a risk score is generated. The maintenance plan is adjusted based on the risk score, and the inspection robot is controlled to perform autonomous inspections. The machine vision and deep learning algorithms are used to identify equipment faults and potential risks, and the data is fed back in real time to optimize the maintenance plan.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the distribution network maintenance planning optimization system as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distribution network maintenance planning optimization system as described in any one of claims 1 to 5.

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