Distribution network maintenance plan optimization system

Abstract

The invention discloses a plan optimization system for distribution network maintenance, which relates to the technical field of power grid management and comprises an intelligent maintenance scheduling optimization module, an equipment monitoring and prediction module, a panoramic maintenance plan simulation module, a dynamic risk assessment module and an inspection robot management module. The intelligent maintenance scheduling optimization module is used for analyzing power distribution network data, and the equipment monitoring and prediction module is used for monitoring the operation state of power distribution network equipment and predicting the time and position of future faults. And the panoramic maintenance plan simulation module dynamically simulates the maintenance scheme. By introducing various models, the defects of the traditional technology in the aspects of flexibility, real-time performance and accuracy are effectively overcome, so that the system has adaptive learning and dynamic adjustment capabilities, can quickly respond to faults and optimize resource scheduling in a complex and changeable working environment, and remarkably reduces the maintenance cost and time; according to the system, the stability and reliability of distribution network equipment are greatly improved, and a solid technical support is provided for intelligent power operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to a distribution network maintenance planning optimization system. Background Art

[0002] In the operation and maintenance process of distribution networks, traditional maintenance plans often rely on manual experience and regular maintenance strategies. This method lacks flexibility in dealing with sudden failures, resulting in long fault location and repair time, 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 distribution networks has gradually become a research hotspot. However, existing technologies still have shortcomings in many aspects.

[0003] Traditional maintenance scheduling systems usually use fixed rules or static algorithms to schedule tasks, which makes it difficult to adapt to complex and changing working environments in real time.

[0004] Most current predictive maintenance systems rely on fixed threshold monitoring mechanisms that trigger alarms only when equipment status exceeds a predetermined range. This approach has a lag in detecting sudden faults and cannot provide accurate fault predictions.

[0005] Most existing risk assessment methods use static risk level classification, which is less sensitive to changes in the external environment and difficult to respond quickly in 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 technology.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: a distribution network maintenance planning optimization system, comprising: an intelligent maintenance scheduling optimization module, which is used to analyze distribution network data and optimize the scheduling arrangement of maintenance tasks; an equipment monitoring and prediction module, which is based on a sensor network and an Internet of Things platform, monitors the operating status of distribution network equipment in real time, and adopts a predictive maintenance model to predict the time and location of future failures; a panoramic maintenance plan simulation module, which provides a maintenance plan simulation environment for operation and maintenance personnel through virtual reality technology and simulation models, and dynamically simulates different maintenance plans; a dynamic risk assessment module, which is used to assess risks during the maintenance process and dynamically adjust the risk level according to real-time conditions. Based on the risk assessment model, the failure probability of equipment, the operating proficiency of maintenance personnel, and the variables of external environmental factors are modeled to calculate the risk level in real time; an inspection robot management module, which is used to control the inspection robot deployed at the distribution network site, and adopts machine vision and deep learning algorithms. The robot conducts autonomous inspections without human operation, automatically identifies equipment failures and potential risks, and transmits data to the planning optimization system in real time.

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

[0010] As a preferred solution of the distribution network maintenance planning optimization system described in the present invention, the intelligent maintenance scheduling optimization module optimizes the allocation of resources, fault priority and cost-effectiveness based on the scheduling model of the machine learning algorithm, and adopts an adaptive neural network optimization algorithm to perform self-learning and improvement according to the actual maintenance situation.

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

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

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

[0014]

[0015] Among them, T i represents the estimated completion time of the ith maintenance task; C i The cost of the ith maintenance task includes labor cost and equipment cost; P i represents the failure probability of the ith maintenance task; L irepresents the location weight of the ith maintenance task, which is set according to the geographical location and importance of the task location; R i The number of resources required for the i-th task includes the number of maintenance teams and the number of equipment; S i Indicates the health score of the device, ranging from 0 to 1. The closer to 1, the healthier the device status. t , W c , W p , W s Represent the weights of time, cost, failure probability and equipment health score respectively;

[0016] Dynamically adjust the weight parameter W based on historical data and current system conditions t , W c , W p , W s , the weight adjustment is performed by the following formula:

[0017] W t，c，p，s =W t，c，p，s ×(1+α·ΔV);

[0018] Among them, α is the adaptive adjustment coefficient, and its value range is 0<α<1; ΔV is the deviation value of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high;

[0019] According to the above adjustments, a dynamic optimization scheduling strategy is implemented to always maintain the optimal state under different circumstances.

[0020] As a preferred solution of the distribution network maintenance planning optimization system described in the present invention, the adaptive neural network optimization algorithm has a built-in optimization model suitable for distribution network maintenance tasks, which intelligently optimizes maintenance task scheduling by adjusting network weights and learning rates in real time according to the current state of the distribution network and changes in the external environment.

[0021] 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.

[0022] The forward propagation formula calculates the network output Y by inputting the feature vector X:

[0023] a l =f l (W l ·a l-1 +b l ) for l = 1, 2, ..., L;

[0024] Among them, a l-1 is the output of the l-1th layer, and L is the total number of layers in the neural network.

[0025] The dynamic learning rate adjustment formula is as follows:

[0026] η(t+1)=η(t)·(1+γ·ΔE);

[0027] Among them, ΔE represents the error change rate between the network prediction result and the actual result. By adjusting the learning rate, the neural network can speed up the learning pace when the error is large and slow down the adjustment speed when it is stable.

[0028] The weight and bias update formula uses the weight and bias update mechanism with adaptive adjustment factors as follows:

[0029]

[0030] in, and are the gradients of the loss function \(E\) with respect to weights and biases, respectively, and λ is the parameter used for weight regularization, which maintains weight sparsity through L1 regularization.

[0031] Reinforcement learning feedback mechanism, introduce reinforcement learning algorithm to optimize task scheduling strategy:

[0032]

[0033] Where Q(s, a) is the value of taking action a in state s, α is the learning rate, r is the current reward, and γ is the discount factor.

[0034] As a preferred solution of the distribution network maintenance planning optimization system described in the present invention, the optimization model includes the following steps when used: inputting distribution network data, converting it into a feature vector X, and inputting it into the optimization model; in each layer of the neural network, the forward propagation calculation is performed layer by layer according to the formula, and the output of each layer is passed to the next layer after the activation function, and finally the priority and resource allocation suggestion of the maintenance task are output; during the training process, the learning rate is dynamically adjusted according to the current error and the changes in the external environment, and fine-tuning is maintained in a stable state to avoid excessive adjustment; using the gradient information of the forward propagation error, combined with the adaptive adjustment mechanism, the weight and bias of the neural network are updated in real time, and the sensitivity to environmental changes is controlled by the adaptive adjustment factor γ, so as to achieve efficient decision-making in task scheduling; using the feedback results of the current scheduling decision, continuously optimizing the scheduling strategy, adjusting the output of the neural network according to the current task status and the scheduling strategy adopted, so that the scheduling selection of each step tends to the global optimum; the neural network outputs the priority and optimal scheduling plan of the maintenance task, the system allocates resources according to the scheduling plan, and feeds the results back to the model to further strengthen learning and optimization.

[0035] As a preferred solution of the distribution network maintenance planning optimization system described in the present 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.

[0036] The hidden state update formula uses an LSTM structure with a time-aware factor to calculate the current hidden state h t , based on the influence of input characteristics and environmental factors:

[0037] h t =f(W h ·h t-1 +W x ·X t +W e ·E t +b h );

[0038] Among them, W h represents the weight of the hidden state at the previous time step, W x is the weight of the input feature, W e The weight of the environmental variables is used to dynamically adjust the internal representation of the device state based on historical data and the current external environment.

[0039] The time-aware adjustment formula enhances the adaptability of the model in different time periods, introduces a time-aware coefficient, and dynamically adjusts the weight of the hidden state update according to the trend of historical data:

[0040]

[0041] Among them, Δt is the time difference between the current time point and the target prediction time point. When the prediction target time is far away, the model pays more attention to historical data; when the time is close, the model pays more attention to the current status and short-term trends.

[0042] The output prediction formula is based on the hidden state h t , calculate the predicted value Y of the equipment health status t :

[0043] Y t =σ(W o ·h t +b o );

[0044] Among them, σ is the sigmoid function, which is used to map the output to the (0, 1) interval, indicating the probability of future equipment failure.

[0045] The health score is used to quantify the health of the device and provide a predicted time to failure:

[0046] HS=100·(1-Yt );

[0047] The closer the HS score is to 100, the healthier the device is, and vice versa, the higher the risk of failure.

[0048] As a preferred solution of the distribution network maintenance planning optimization system described in the present invention, the risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network, dynamically updates the conditional probability according to the real-time situation, and continuously updates the conditional probability of each node during the maintenance process to reflect the real-time changes of the equipment status and the external environment; the risk assessment model includes a joint probability distribution function, a conditional probability update formula of the risk factor, and a risk score calculation formula.

[0049] The joint probability distribution function, risk level R, is the joint probability function of multiple conditional variables:

[0050]

[0051] The conditional probability update formula of the risk factor is as follows:

[0052]

[0053] Among them, R t is the risk level at the current moment, P(R t-1 ) is the risk level at the previous moment, and data represents the latest sensor data or maintenance information.

[0054] The risk score calculation formula converts the overall risk level R into a visual scoring system:

[0055]

[0056] Among them, α is the adjustment coefficient, which is used to control the sensitivity of the risk score; the risk level is standardized to a value between 0 and 10)0), and the risk level is scored visually.

[0057] To further solve the above technical problems, the present invention provides the following technical solutions: a distribution network maintenance planning optimization method, comprising:

[0058] Collecting distribution network data, including historical maintenance data, equipment failure probability prediction, geographic location, and current equipment health status data;

[0059] Based on the 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;

[0060] Use a predictive maintenance model based on the LSTM structure to predict the time and location of possible equipment failures based on real-time monitoring of equipment operating status data, and dynamically update the priority of the maintenance plan;

[0061] A dynamic adaptive Bayesian network is used to conduct risk assessment on maintenance plans, calculate the risk level based on equipment failure probability, maintenance personnel's proficiency and external environmental factors, and generate a risk score.

[0062] The maintenance plan is adjusted based on the risk score, and the inspection robot is controlled to perform autonomous inspections. Equipment failures and potential risks are identified through machine vision and deep learning algorithms, and real-time feedback data is used to optimize the maintenance plan.

[0063] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the distribution network maintenance planning optimization system as described above when executing the computer program.

[0064] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the planning optimization system for distribution network maintenance as described above are implemented.

[0065] Beneficial effects of the invention: The invention effectively overcomes the deficiencies of traditional technologies in flexibility, real-time and accuracy by introducing multiple models, so that the system has adaptive learning and dynamic adjustment capabilities, can quickly respond to faults in complex and changeable working environments, optimize resource scheduling, and significantly reduce maintenance costs and time. The system greatly improves the stability and reliability of distribution network equipment, and provides solid technical support for intelligent power operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0067] Figure 1 A schematic diagram of the structure of a distribution network maintenance planning optimization system proposed by the present invention;

[0068] Figure 2 A schematic diagram of the flow of a scheduling model in a distribution network maintenance planning optimization system proposed by the present invention;

[0069] Figure 3 A schematic diagram of the composition and flow of an optimization model in a distribution network maintenance planning optimization system proposed by the present invention;

[0070] Figure 4 A schematic diagram of the composition and flow of a predictive maintenance model in a distribution network maintenance planning optimization system proposed by the present invention;

[0071] Figure 5 The present invention provides a schematic diagram of the composition and flow of a risk assessment model in a distribution network maintenance planning optimization system. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0073] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0074] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a distribution network maintenance planning optimization system.

[0075] The present application provides a method for effectively solving the above-mentioned problems. Next, a plurality of embodiments will be combined to explain in detail how to implement the distribution network maintenance planning optimization system. The system includes:

[0076] The intelligent maintenance scheduling optimization module is used to analyze distribution network data and optimize the scheduling of maintenance tasks.

[0077] Specifically, distribution network data includes historical maintenance data, equipment failure probability prediction, geographic location, and data on current equipment health status.

[0078] Furthermore, the intelligent maintenance scheduling optimization module optimizes the allocation of resources with fault priority and cost-effectiveness based on the scheduling model of the machine learning algorithm, and adopts an adaptive neural network optimization algorithm to perform self-learning and improvement according to the actual maintenance situation.

[0079] The dispatching model in a dynamic environment aims to minimize maintenance time and cost while maximizing task completion rate and equipment health by learning historical maintenance data of the distribution network and current equipment status. The specific steps include the following:

[0080] Set the variables and parameters of the scheduling model, including:

[0081] T i represents the estimated completion time of the i-th maintenance task;

[0082] C i The cost of the i-th maintenance task includes labor cost and equipment use cost;

[0083] P i represents the failure probability of the i-th maintenance task;

[0084] L i represents the location weight of the ith maintenance task, which is set according to the geographical location and importance of the task location;

[0085] R i The number of resources required for the i-th task includes the number of maintenance teams and the number of equipment;

[0086] S i Indicates the health score of the device, ranging from 0 to 1. The closer to 1, the healthier the device status;

[0087] W t , W c , W p , W s They represent the weights of time, cost, failure probability and equipment health score respectively.

[0088] Define a multi-objective optimization function Z as the objective of the scheduling model, the formula is:

[0089]

[0090] Among them, T i represents the estimated completion time of the ith maintenance task; C i The cost of the ith maintenance task includes labor cost and equipment cost; P i represents the failure probability of the ith maintenance task; L i represents the location weight of the ith maintenance task, which is set according to the geographical location and importance of the task location; R i The number of resources required for the i-th task includes the number of maintenance teams and the number of equipment; S i Indicates the health score of the device, ranging from 0 to 1. The closer to 1, the healthier the device status. t , W c , W p , W s They represent the weights of time, cost, failure probability and equipment health score respectively.

[0091] It is used to minimize the time cost of repair tasks. It takes geographical location into consideration. If the task location is more important, that is, the weight is higher, the model will prioritize it to reduce the total time.

[0092] Used to minimize cost expenditure, consider the number of resources required, and reduce maintenance costs by optimizing resource efficiency;

[0093] Used to maximize the completion rate of tasks and improve the reliability of the overall network by reducing the impact of equipment failure probability;

[0094] It is used to maximize the health status of the equipment, prioritize the scheduling of key equipment that has a greater impact on the system, and improve the overall equipment health score.

[0095] Dynamically adjust the weight parameter W based on historical data and current system conditions t , W c , W p , W s , the weight adjustment is performed by the following formula:

[0096] W t，c，p，s =W t，c，p，s ×(1+α·ΔV);

[0097] Among them, α is the adaptive adjustment coefficient, and its value range is 0<α<1; ΔV is the deviation value of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high;

[0098] According to the above adjustments, a dynamic optimization scheduling strategy is implemented to always maintain the optimal state under different circumstances.

[0099] Furthermore, 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 intelligently optimizes the scheduling of maintenance tasks according to the current state of the distribution network and changes in the external environment.

[0100] Among them, the optimization model includes the forward propagation formula, the dynamic learning rate adjustment formula, the weight and bias update formula, and the reinforcement learning feedback mechanism.

[0101] First, set the variables and parameters of the optimization model, including:

[0102] X={x1,x2,...,x n} is the input feature vector, including historical maintenance data, equipment failure probability, equipment health status, and geographic location;

[0103] Y={y1,y2,...,y m} is the output vector, which represents the priority, resource allocation and estimated completion time of each maintenance task;

[0104] W l is the weight matrix of the l-th layer neural network;

[0105] b l is the bias vector of the l-th layer neural network;

[0106] f l (·) is the activation function of the lth layer of the neural network;

[0107] η(t) is the dynamic learning rate, which is adjusted as time t changes;

[0108] ΔW l and Δb l is the adjustment amount of weight and bias;

[0109] γ is the adaptive adjustment coefficient, which is used to control the response speed of the network to changes in the external environment.

[0110] The forward propagation formula calculates the network output Y by inputting the feature vector X:

[0111] a l =f l (W l ·a l-1 +b l ) for l = 1, 2, ..., L;

[0112] Among them, a l-1 is the output of the l-1th layer, L is the total number of layers of the neural network;

[0113] The dynamic learning rate adjustment formula is as follows:

[0114] η(t+1)=η(t)·(1+γ·ΔE);

[0115] Among them, ΔE represents the error change rate between the network prediction result and the actual result. By adjusting the learning rate, the neural network can speed up the learning pace when the error is large, and slow down the adjustment speed when it is stable;

[0116] The weight and bias update formula uses the weight and bias update mechanism with adaptive adjustment factors as follows:

[0117]

[0118] in, and are the gradients of the loss function \(E\) with respect to weights and biases, respectively, and λ is the parameter used for weight regularization, which maintains weight sparsity through L1 regularization;

[0119] Reinforcement learning feedback mechanism, introduce reinforcement learning algorithm to optimize task scheduling strategy:

[0120]

[0121] Where Q(s, a) is the value of taking action a in state s, α is the learning rate, r is the current reward, and γ is the discount factor.

[0122] The reinforcement learning mechanism updates the scheduling strategy in real time by interacting with the environment, thereby improving the accuracy of task priority decisions.

[0123] The optimization model includes the following steps when used:

[0124] Data preprocessing and input: input distribution network data, convert it into feature vector X, and input it into the optimization model;

[0125] 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 the activation function, and finally the priority of the maintenance task and resource allocation suggestions are output;

[0126] Dynamic learning rate adjustment: During the training process, the learning rate is dynamically adjusted according to the current error and changes in the external environment. In a stable state, fine-tuning is maintained to avoid over-adjustment.

[0127] Adaptive update of weights and biases: The gradient information of the forward propagation error is used in combination with an adaptive adjustment mechanism to update the weights and biases of the neural network in real time. The sensitivity to environmental changes is controlled by the adaptive adjustment factor γ, thereby achieving efficient decision-making in task scheduling.

[0128] Reinforcement learning feedback optimization: Utilize the feedback results of the current scheduling decision to continuously optimize the scheduling strategy. According to the current task status and the scheduling strategy adopted, adjust the output of the neural network so that the scheduling selection of each step tends to the global optimal.

[0129] Output scheduling plan and implementation: The neural network outputs the priority and optimal scheduling plan of the maintenance task. The system allocates resources according to the scheduling plan and feeds the results back to the model to further enhance learning and optimization.

[0130] The equipment monitoring and prediction module is based on sensor networks and the Internet of Things platform to monitor the operating status of distribution network equipment in real time, and uses a predictive maintenance model to predict the time and location of future failures.

[0131] The predictive maintenance model sets the following variables and parameters:

[0132] X t ={xt-1 , x t-2 , ..., x t-n Input features for time series include historical values ​​of sensor data such as vibration, temperature, current, and voltage;

[0133] Y t is the output value, which indicates the future equipment health status or the probability of failure;

[0134] E t Environmental factors, such as external temperature, humidity, and workload, affect the device status;

[0135] h t is the hidden state, representing the internal state of the model at time t;

[0136] W h , W x , W e are the weight matrices of hidden state, input features, and environment features respectively;

[0137] b h , b o is the bias term of the hidden layer and the output layer;

[0138] f(·) is the activation function, such as ReLU or sigmoid;

[0139] α is the time-aware adjustment coefficient, which is used to adjust the model's attention to historical data.

[0140] The predictive maintenance model includes a hidden state update formula, a time-aware adjustment formula, an output prediction formula, and a health status score;

[0141] The hidden state update formula uses an LSTM structure with a time-aware factor to calculate the current hidden state h t , based on the influence of input characteristics and environmental factors:

[0142] h t =f(W h ·h t-1 +W x ·X t +W e ·E t +b h );

[0143] Among them, W h represents the weight of the hidden state at the previous time step, W x is the weight of the input feature, W e The weight of the environmental variables is used to dynamically adjust the internal representation of the device state based on historical data and the current external environment.

[0144] The time-aware adjustment formula enhances the adaptability of the model in different time periods. The time-aware coefficient is introduced to dynamically adjust the weight of the hidden state update according to the trend of historical data:

[0145]

[0146] Among them, Δt is the time difference between the current time point and the target prediction time point. When the prediction target time is far away, the model pays more attention to historical data; when the time is close, the model pays more attention to the current status and short-term trends.

[0147] The output prediction formula is based on the hidden state h t , calculate the predicted value Y of the equipment health status t :

[0148] Y t =σ(W o ·h t +b o );

[0149] Among them, σ is the sigmoid function, which is used to map the output to the (0, 1) interval, indicating the probability of future equipment failure.

[0150] The health score is used to quantify the health of the device and provide a predicted failure time:

[0151] HS=100·(1-Y t );

[0152] The closer the HS score is to 10)0), the healthier the device is, and vice versa, the higher the risk of failure.

[0153] The panoramic maintenance plan simulation module provides operation and maintenance personnel with a maintenance plan simulation environment through virtual reality technology and simulation models, and dynamically simulates different maintenance plans.

[0154] The dynamic risk assessment module is used to assess risks during the maintenance process and dynamically adjust the risk level according to the real-time situation. Based on the risk assessment model, the variables of equipment failure probability, maintenance personnel's operating proficiency and external environmental factors are modeled to calculate the risk level in real time.

[0155] The risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network to dynamically update 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 of the equipment status and the external environment.

[0156] The following variables and parameters are defined in the risk assessment model:

[0157] R is the overall risk level score, ranging from 0 to 1;

[0158] F is the equipment failure probability variable;

[0159] S is the operation proficiency of the maintenance personnel;

[0160] E is external environmental factors, such as weather conditions, temperature, and humidity;

[0161] C i Other related condition variables, such as historical equipment failure records and current equipment load;

[0162] P(A|B) is the probability of event A occurring under condition B.

[0163] The risk assessment model includes the joint probability distribution function, the conditional probability update formula of the risk factor and the risk score calculation formula.

[0164] Joint probability distribution function, risk level R is the joint probability function of multiple conditional variables:

[0165]

[0166] The conditional probability update formula of the risk factor is as follows:

[0167]

[0168] Among them, R t is the risk level at the current moment, P(R t-1 ) is the risk level at the previous moment, and data represents the latest sensor data or maintenance information;

[0169] The risk score calculation formula converts the overall risk level R into a visual scoring system:

[0170]

[0171] Among them, α is the adjustment coefficient, which is used to control the sensitivity of the risk score; the risk level is standardized to a value between 0 and 10)0), and the risk level is scored visually.

[0172] The inspection robot management module is used to control the inspection robots deployed at the distribution network site. It uses machine vision and deep learning algorithms. The robots can conduct autonomous inspections without human operation, automatically identify equipment failures and potential risks, and transmit data to the planning optimization system in real time.

[0173] In summary, the present invention effectively overcomes the shortcomings of traditional technologies in flexibility, real-time and accuracy by introducing multiple models, so that the system has adaptive learning and dynamic adjustment capabilities, can quickly respond to faults in complex and changeable working environments, optimize resource scheduling, and significantly reduce maintenance costs and time. The system greatly improves the stability and reliability of distribution network equipment, and provides solid technical support for intelligent power operation and maintenance.

[0174] Embodiment 2 is an embodiment of the present invention, which provides a distribution network maintenance plan optimization method, including:

[0175] S1: Collect distribution network data, including historical maintenance data, equipment failure probability prediction, geographic location, and current equipment health status data;

[0176] S2: Based on the 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;

[0177] S3: Use a predictive maintenance model based on the LSTM structure to predict the time and location of possible equipment failures based on real-time monitoring of equipment operating status data, and dynamically update the priority of the maintenance plan;

[0178] S4: Use dynamic adaptive Bayesian network to conduct risk assessment on maintenance plan, calculate risk level based on equipment failure probability, maintenance personnel operation proficiency and external environmental factors, and generate risk score;

[0179] S5: Adjust the maintenance plan based on the risk score and control the inspection robot to perform autonomous inspections. Identify equipment failures and potential risks through machine vision and deep learning algorithms, and use real-time feedback data to optimize the maintenance plan.

[0180] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0181] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0182] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0183] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0184] 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 experiments.

[0185]

[0186] As shown in Table 1, in terms of maintenance efficiency, the average response time of the present 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 that of the other two systems respectively.

[0187] Fault prediction performance: The present invention has a fault prediction accuracy of 92.4%, which is much higher than the 71.6% of the traditional system and the 83.5% of the simple machine learning system. The early warning time reaches 48.5 hours, which leaves sufficient preparation time for maintenance personnel.

[0188] Cost control: The average single maintenance cost of the present invention is 2,850 yuan, which is 32.5% less than the traditional system and 22.6% less than the simple machine learning system. At the same time, it also achieves significant results in extending the life of the equipment, with an extension rate of 25.4%.

[0189] System performance: The real-time data processing capacity of the present invention reaches 1250 pieces / second, which is 3.3 times that of the traditional system. The system response time is only 85ms, which greatly improves the maintenance efficiency.

[0190] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network maintenance planning optimization system, characterized in that: include: Intelligent maintenance scheduling optimization module, used to analyze distribution network data and optimize the scheduling of maintenance tasks; Equipment monitoring and prediction module, which is based on sensor networks and IoT platforms to monitor the operating status of distribution network equipment in real time and use predictive maintenance models 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; Dynamic risk assessment module, used to assess risks during maintenance and dynamically adjust risk levels according to real-time conditions. Based on the risk assessment model, the failure probability of equipment, the operating proficiency of maintenance personnel, and variables of external environmental factors are modeled to calculate risk levels in real time. The inspection robot management module is used to control the inspection robots deployed at the distribution network site. It uses machine vision and deep learning algorithms. The robots can conduct autonomous inspections without human operation, automatically identify equipment failures and potential risks, and transmit data to the planning optimization system in real time.

2. The distribution network maintenance planning optimization system according to claim 1, characterized in that: The distribution network data includes historical maintenance data, equipment failure probability prediction, geographic location, and data on current equipment health status.

3. The distribution network maintenance planning optimization system according to claim 2, characterized in that: The intelligent maintenance scheduling optimization module optimizes resource allocation, fault priority and cost-effectiveness based on the scheduling model of the machine learning algorithm, and adopts an adaptive neural network optimization algorithm to perform self-learning and improvement according to the actual maintenance situation; The scheduling model aims to minimize maintenance time and cost while maximizing task completion rate and equipment health by learning historical maintenance data of the distribution network and current equipment status in a dynamic environment. Specifically, it includes the following steps: Set variables and parameters of the scheduling model; Define a multi-objective optimization function Z as the objective of the scheduling model, the formula is: Among them, T i represents the estimated completion time of the ith maintenance task; C i The cost of the ith maintenance task includes labor cost and equipment cost; R i represents the failure probability of the ith maintenance task; L i represents the location weight of the ith maintenance task, which is set according to the geographical location and importance of the task location; R i The number of resources required for the i-th task includes the number of maintenance teams and the number of equipment; S i Represents the health score of the device W t , W c , W p , W s Represent the weights of time, cost, failure probability and equipment health score respectively; Dynamically adjust the weight parameter W based on historical data and current system conditions t , W c , W p , W s , the weight adjustment is performed by the following formula: W t，c，p，s =W t，c，p，s ×(1+α·ΔV); Among them, α is the adaptive adjustment coefficient, and its value range is 0<α<1; ΔV is the deviation value of each indicator from the ideal state, such as time exceeding expectations, cost exceeding budget, and failure probability being too high; According to the above adjustments, a dynamic optimization scheduling strategy is implemented to always maintain the optimal state under different circumstances.

4. The distribution network maintenance planning optimization system according to 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 rates in real time, it intelligently optimizes the scheduling of maintenance tasks according to 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 calculates the network output Y by inputting the feature vector X: a l =f l (W l ·a l-1 +b l ) for l=1,2,...,L; Among them, a l-1 is the output of the l-1th layer, L is the total number of layers of the neural network; The dynamic learning rate adjustment formula is as follows: η(t+1)=η(t)·(1+γ·ΔE); Among them, ΔE represents the error change rate between the network prediction result and the actual result. By adjusting the learning rate, the neural network can speed up the learning pace when the error is large, and slow down the adjustment speed when it is stable; The weight and bias update formula uses the weight and bias update mechanism with adaptive adjustment factors as follows: in, and are the gradients of the loss function \(E\) with respect to weights and biases, respectively, and λ is the parameter used for weight regularization, which maintains weight sparsity through L1 regularization; Reinforcement learning feedback mechanism, introduce reinforcement learning algorithm to optimize task scheduling strategy: Where Q(s, a) is the value of taking action a in state s, α is the learning rate, r is the current reward, and γ is the discount factor.

5. The distribution network maintenance planning optimization system according to claim 4, characterized in that: The optimization model comprises the following steps when used: Input distribution network data, convert it into a feature vector X, and input it into 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 the activation function, and finally the priority of the maintenance task and the resource allocation suggestion are output; During the training process, the learning rate is dynamically adjusted according to the current error and changes in the external environment. In a stable state, fine-tuning is maintained to avoid over-adjustment. By using 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. The sensitivity to environmental changes is controlled by the adaptive adjustment factor γ, thereby achieving efficient decision-making in task scheduling. Using the feedback results of the current scheduling decision, the scheduling strategy is continuously optimized. According to the current task status and the scheduling strategy adopted, the output of the neural network is adjusted so that the scheduling selection of each step tends to the global optimum. The neural network outputs the priority and optimal scheduling plan of the maintenance task. The system allocates resources according to the scheduling plan and feeds the results back to the model to further enhance learning and optimization.

6. The distribution network maintenance planning optimization system according to claim 5, characterized in that: The predictive maintenance model includes a hidden state update formula, a time-aware adjustment formula, an output prediction formula, and a health state score; The hidden state update formula uses an LSTM structure with a time-aware factor to calculate the current hidden state h t , based on the influence of input characteristics and environmental factors: h t =f(W h ·h t-1 +W x ·X t +W e ·E t +b h ); Among them, W h represents the weight of the hidden state at the previous time step, W x is the weight of the input feature, W e The weights of environmental variables dynamically adjust the internal representation of the device state based on historical data and the current external environment; The time-aware adjustment formula enhances the adaptability of the model in different time periods, introduces a time-aware coefficient, and dynamically adjusts the weight of the hidden state update according to the trend of historical data: Among them, Δt is the time difference between the current time point and the target prediction time point. When the prediction target time is far away, the model pays more attention to historical data; when the time is close, the model pays more attention to the current state and short-term trends; The output prediction formula is based on the hidden state h t , calculate the predicted value Y of the equipment health status t : Y t =σ(W o ·h t +b o ); Among them, σ is the sigmoid function, which is used to map the output to the (0,1) interval, indicating the probability of future equipment failure; The health score is used to quantify the health of the device and provide a predicted time to failure: HS=100·(1-Y t )。 7. The distribution network maintenance planning optimization system according to claim 6, characterized in that: The risk assessment model in the dynamic risk assessment module adopts a dynamic adaptive Bayesian network, dynamically updates the conditional probability according to the real-time situation, and continuously updates the conditional probability of each node during the maintenance process to reflect the real-time changes of the equipment status and the external environment; the risk assessment model includes a joint probability distribution function, a conditional probability update formula of the risk factor, and a risk score calculation formula; The joint probability distribution function, risk level R, is the joint probability function of multiple conditional variables: The conditional probability update formula of the risk factor is as follows: Among them, R t is the risk level at the current moment, P(R t-1 ) is the risk level at the previous moment, and data represents the latest sensor data or maintenance information; The risk score calculation formula converts the overall risk level R into a visual scoring system: Among them, α is the adjustment coefficient, which is used to control the sensitivity of the risk score; the risk level is standardized to a value between 0 and 100, and the risk level is scored visually.

8. A method using the distribution network maintenance planning optimization system as claimed in any one of claims 1 to 7, characterized in that: include: Collecting distribution network data, including historical maintenance data, equipment failure probability prediction, geographic location, and current equipment health status data; Based on the 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; Use a predictive maintenance model based on the LSTM structure to predict the time and location of possible equipment failures based on real-time monitoring of equipment operating status data, and dynamically update the priority of the maintenance plan; A dynamic adaptive Bayesian network is used to conduct risk assessment on maintenance plans, calculate the risk level based on equipment failure probability, maintenance personnel's proficiency and external environmental factors, and generate a risk score. The maintenance plan is adjusted based on the risk score, and the inspection robot is controlled to perform autonomous inspections. Equipment failures and potential risks are identified through machine vision and deep learning algorithms, and real-time feedback data is used to optimize the maintenance plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution network maintenance planning optimization system described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network maintenance planning optimization system described in any one of claims 1 to 7 are implemented.

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