Distribution network operation and maintenance test method and device based on micro modular processing

Abstract

The invention discloses a distribution network operation and maintenance test method and device based on micro modular processing, and relates to the technical field of distribution network operation and maintenance tests.The distribution network system is comprehensively monitored and covered through distributed monitoring, so that monitoring tasks can be distributed to different micro modules, the monitoring comprehensiveness and timeliness are improved, decision making is more flexible, and the distribution network operation and maintenance test efficiency is improved. An intelligent decision-making model based on deep reinforcement learning is adopted, the flexibility and the adaptive capacity of the system are improved according to the real-time system state and environment change, autonomous learning and decision-making strategy optimization, in addition, faults can be processed in real time, a light-weight processing model is adopted in the reasoning module, rapid operation is conducted on edge equipment, and the system reliability is improved. And the real-time response capability and reliability of the system are improved, so that the operation and maintenance management of the power distribution network is more efficient and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation and maintenance testing, and in particular to a distribution network operation and maintenance testing method and device based on micro modular processing. Background Art

[0002] Distribution network operation and maintenance testing usually tests the operation and maintenance of the distribution network to ensure the reliability, safety and efficiency of the distribution system, and promptly discover and eliminate potential problems to ensure the stability and continuity of power supply. Distribution network operation and maintenance testing usually includes regular inspections of various equipment in the distribution system, such as transformers, switchgear, and distribution boards, measuring and recording electrical parameters such as voltage, current, and power factor, evaluating the electrical performance of the system, testing the reliability and response performance of protection devices, and checking the grounding resistance, grounding polarity and other parameters of the grounding system.

[0003] For example, the patent publication number CN116093806A discloses a distribution network equipment operation and maintenance method and system, the method includes obtaining the operation and maintenance data of several distribution network equipment in the distribution area, and calculating the key operation and maintenance analysis indicators such as external force damage failure rate, hidden danger rectification rate, lightning failure rate, ground lightning density, user failure rate and user boundary switch action rate based on the operation and maintenance data; establishing an operation and maintenance strategy-related indicator analysis model based on the key operation and maintenance analysis indicators, and calculating the external force damage evaluation factor of each distribution network equipment through the operation and maintenance strategy-related indicator analysis model; adjusting the operation and maintenance strategy of each distribution network equipment based on the external force damage evaluation factor of each distribution network equipment, and controlling each distribution network equipment to perform operation and maintenance according to the adjusted operation and maintenance strategy; calculating the total evaluation factor based on the proportion of various operation and maintenance failures and the external force damage evaluation factor, and evaluating the operation and maintenance quality of the distribution area.

[0004] Although the above scheme has the above advantages, the traditional distribution network operation and maintenance test method is limited to specific areas or specific types of monitoring tasks, and cannot achieve comprehensive coverage of the distribution network system, resulting in blind spots and loopholes in the system, and it is easy to miss potential faults and abnormal conditions. At the same time, the decision-making mode of the traditional method is relatively rigid and cannot be flexibly adjusted according to the real-time system status and environmental changes. The adaptability is poor. Therefore, there is an urgent need for a distribution network operation and maintenance test method and device based on micro-modular processing to solve such problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a distribution network operation and maintenance testing method and device based on micro-modular processing, which solves the problems in the prior art that the monitoring tasks are limited to specific areas or specific types, and cannot achieve comprehensive coverage of the distribution network system. The decision-making mode of the traditional method is relatively rigid and cannot be flexibly adjusted according to the real-time system status and environmental changes.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a distribution network operation and maintenance test method based on micro modular processing, comprising: Step 1. Perform distributed real-time monitoring. The distributed monitoring module distributes the monitoring tasks to different micro-modules. Each micro-module is responsible for monitoring a specific area, providing comprehensive coverage of the distribution network system. The monitoring process uses distributed message transmission to exchange and synchronize data between modules. Step 2. Heterogeneous data fusion: Based on the data from different types of sensors and monitoring equipment, including current, voltage, and temperature, heterogeneous data fusion is used to integrate and fuse multi-source data, build a distribution network system data model, and then process and filter the data; The distribution network system data model is constructed as follows: Collect data from various types of sensors and monitoring equipment at each node of the distribution network system, including current ,Voltage ,temperature ; De-noise the raw data, fill in missing values, and perform data standardization. Perform feature extraction on the data collected by each sensor to obtain a high-dimensional feature vector. Then use the autoencoder to reduce the dimension of the high-dimensional features to obtain a low-dimensional feature representation. The low-dimensional feature vectors from different sensors are fused to construct the overall feature representation of the distribution network system; Use the fused feature vectors to build a data model for the distribution network system; Based on deep reinforcement learning methods, an intelligent decision-making model for distribution network system is constructed; Step 3. Real-time data processing and reasoning: Edge computing devices are deployed at the edge nodes of the distribution network system, and edge computing is used for real-time data processing and reasoning. The real-time reasoning model here is a lightweight processing model. At the same time, for distribution network system failures and abnormal conditions, real-time reasoning rules are deployed to quickly diagnose and process the failures; Step 4. Operation status and data change monitoring: Based on the distributed monitoring results, the operation status and data changes of the distribution network system are monitored in real time, including voltage fluctuations and current anomalies. Based on heterogeneous data fusion and real-time reasoning results, real-time diagnosis and early warning of system faults and anomalies are carried out; It also provides a visual interface to display real-time monitoring data and fault diagnosis results.

[0007] The present invention is further configured as follows: in the distributed monitoring step, the monitoring area is first divided, and the task amount of each monitoring area is estimated: The monitoring area of ​​the distribution network system is divided into sub-areas, each of which corresponds to a micromodule , Represents the module number, for each sub-area Estimated monitoring workload , Indicates The monitoring task volume of each micromodule is Estimate based on regional power demand and line density factors; Establish a task allocation optimization model to minimize the system monitoring task volume and evenly distribute the monitoring tasks of each micro-module; The optimization objective function is to minimize the weighted sum of the monitoring tasks of each micro-module, which means minimizing the overall monitoring task volume of the system. Then: ,in, Represents each sub-region The weights of the , and at the same time establish constraints; The constraints are: Each micromodule The monitoring task volume cannot exceed the maximum task volume it can carry, that is, ,in Each micromodule The maximum amount of tasks; Each monitoring task is assigned to only one micromodule, i.e. , , here , Both represent the index of micromodules, that is, two different micromodules. Represents a set of monitoring tasks and The intersection of is an empty set, that is, the task set and There is no common mission; Then, the heuristic algorithm is used to solve the task allocation optimization model and obtain the task allocation plan; Based on the optimization results, the monitoring tasks are dynamically assigned to each micromodule; It uses task scheduling algorithm and distributed message passing mechanism to exchange and synchronize data between modules; The present invention is further configured as follows: the step of constructing an intelligent decision-making model for a distribution network system comprises: The distribution network system is modeled as a reinforcement learning environment, including an agent, which performs different actions in different states and obtains corresponding rewards; Let state represent the current state of the distribution network system. Indicates the current moment, action Indicates that the agent is in state Rewards for actions taken Representing an Agent Execute an action Feedback obtained afterwards; Use a deep neural network as the agent’s policy network, taking the state as input and outputting the probability distribution of the action; Assume that the agent's policy network is represented as ,in represents the network parameters, Indicates that the agent is in state Take action The probability of Represents the parameters of the policy network, and softmax represents the activation function; The present invention is further configured as follows: the step of constructing the distribution network system intelligent decision model also includes: Building the Reward Function Evaluate the agent at different states Take different actions How good or bad is it? Use deep Q learning to optimize the agent's policy network. By interacting with the environment, the agent adjusts its strategy based on the reward signal and gradually learns the optimized distribution network decision strategy. The Q value is updated in the following way: ,in, represents the learning rate, represents the discount factor, Represents the action performed by the agent Then transfer to the next state, The next state The best action to take in The present invention is further configured as follows: in step 3, the real-time reasoning rule deployment method is: Use autoencoders, isolation forests, and deep neural networks to build anomaly detection models; The deployment loss function measures the difference between the original data and the reconstructed data; Then, based on the output results of the anomaly detection model, deploy real-time inference rules and set thresholds. When the anomaly detection model outputs an abnormal score, the real-time inference rules are triggered; Select the corresponding processing plan and trigger the corresponding maintenance operation according to the abnormality type, equipment information and historical data; The present invention also provides a distribution network operation and maintenance test device based on micro modular processing, comprising: Real-time reasoning system, responsible for real-time monitoring of the operating status of the distribution network system, and rapid diagnosis and processing of system failures and abnormal conditions; The real-time reasoning system includes a distributed monitoring module and a data processing module; Operation status monitoring and early warning module, real-time monitoring of the operation status and data changes of the distribution network system; Distributed monitoring module, responsible for distributing monitoring tasks to different micro-modules, to provide comprehensive coverage of the distribution network system; Distributed monitoring modules include: The task division unit divides the monitoring area into sub-areas and estimates the monitoring task volume of each sub-area; Allocation optimization unit, establishes task allocation optimization model and provides balanced task allocation; Task dynamic unit, dynamically allocates monitoring tasks according to optimization results, and realizes data exchange and synchronization between modules through message passing mechanism; The data processing module is responsible for fusing and processing data from different sensors and building a data model for the distribution network system; The data processing module includes: Extract dimension reduction units, extract features from each sensor data, reduce the dimension and fuse them into an overall feature representation, and build a data model of the distribution network system based on the fused feature vector; The present invention is further configured as a distribution network operation and maintenance test device based on micro modular processing, further comprising: Intelligent decision-making module, used to build an intelligent decision-making model for the distribution network system, and to intelligently manage and optimize the distribution network system; The intelligent decision-making module models the distribution network system as a reinforcement learning environment, sets the state, action and reward, uses a deep neural network to build the agent's strategy network, and realizes the mapping from state to action; Then, the deep Q-learning algorithm is used to optimize the policy network and gradually learn the optimized distribution network decision-making strategy; The present invention also discloses an electronic device, comprising: a processor, and a memory connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement a distribution network operation and maintenance test method based on micro-modular processing; The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement a distribution network operation and maintenance test method based on micro-modular processing; The present invention also discloses a computer program product, including a computer program, which implements a distribution network operation and maintenance test method based on micro-modular processing when executed by a processor.

[0008] Compared with the prior art, the present invention has the following beneficial effects: The present invention modularizes the distribution network operation and maintenance test device. First, the distributed monitoring module distributes the monitoring tasks to different micro modules. Each micro module is responsible for monitoring a specific area, thereby achieving comprehensive coverage of the distribution network system. Through task division, task allocation optimization and task dynamic allocation steps, the balanced allocation and efficient execution of monitoring tasks are ensured, making the monitoring work of the distribution network system more comprehensive and effective. Secondly, the data processing module is responsible for fusing and processing data from different sensors, building a data model for the distribution network system, preprocessing, feature extraction and dimensionality reduction fusion of the original data, and providing data support for intelligent decision-making. Then, the intelligent decision-making module models the distribution network system as a reinforcement learning environment, and uses deep neural networks to build a policy network for intelligent agents. By interacting with the environment, it gradually learns the optimized distribution network decision-making strategy, so that the distribution network system can autonomously learn and optimize decision-making strategies according to different environments and needs, thereby improving the intelligence level and adaptability of the system. The present invention adopts a lightweight processing model in real-time reasoning, runs quickly on edge devices, realizes rapid diagnosis and processing of distribution network system faults and abnormal conditions, deploys real-time reasoning rules to realize rapid fault diagnosis and processing, and improves the reliability and stability of the system; In summary, the present invention provides comprehensive monitoring coverage of the distribution network system through distributed monitoring, so that the monitoring tasks can be distributed to different micro-modules, thereby improving the comprehensiveness and timeliness of monitoring and making decisions more flexible. It adopts an intelligent decision-making model based on deep reinforcement learning, so that the system can autonomously learn and optimize decision-making strategies according to real-time system status and environmental changes, thereby improving the flexibility and adaptability of the system. In addition, faults can be processed in real time. The reasoning module adopts a lightweight processing model and runs quickly on edge devices, thereby improving the real-time response capability and reliability of the system, making the operation and maintenance management of the distribution network more efficient and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a flow chart of the distribution network operation and maintenance test method based on micro modular processing of the present invention; Figure 2 This is an architecture diagram of the distribution network operation and maintenance test device based on micro-modular processing of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0011] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0012] The present invention is further described in detail below in conjunction with the accompanying drawings: Example See also Figure 1-Figure 2 The present invention provides a distribution network operation and maintenance test method based on micro-modular processing, comprising: Step 1. Perform distributed real-time monitoring. The distributed monitoring module distributes the monitoring tasks to different micro-modules. Each micro-module is responsible for monitoring a specific area, providing comprehensive coverage of the distribution network system. The monitoring process uses distributed message transmission to exchange and synchronize data between modules. In the distributed monitoring step, the monitoring area is first divided and the task volume of each monitoring area is estimated: The monitoring area of ​​the distribution network system is divided into sub-areas, each of which corresponds to a micromodule , Represents the module number, for each sub-area Estimated monitoring workload , Indicates The monitoring task volume of each micromodule is Estimate based on regional power demand and line density factors; Establish a task allocation optimization model to minimize the system monitoring task volume, and evenly distribute the monitoring tasks of each micromodule to ensure comprehensive monitoring coverage of the distribution network system; The optimization objective function is to minimize the weighted sum of the monitoring tasks of each micro-module, which means minimizing the overall monitoring task volume of the system. Then: ,in, Represents each sub-region The weights of the , and at the same time establish constraints; The constraints are: Each micromodule The monitoring task volume cannot exceed the maximum task volume it can carry, that is, ,in Each micromodule The maximum amount of tasks; Each monitoring task is assigned to only one micromodule, i.e. , , here , Both represent the index of micromodules, that is, two different micromodules. Represents a set of monitoring tasks and The intersection of is an empty set, that is, the task set and There is no common mission; Then, the heuristic algorithm is used to solve the task allocation optimization model and obtain the task allocation plan; According to the optimization results, the monitoring tasks are dynamically allocated to each micro-module to achieve balanced distribution and efficient execution of monitoring tasks; The task scheduling algorithm is used to ensure that the monitoring tasks of each micro-module can be completed in time, and the data exchange and synchronization between modules are carried out through the distributed message passing mechanism; Step 2. Heterogeneous data fusion: Based on the data from different types of sensors and monitoring equipment, including current, voltage, and temperature, heterogeneous data fusion is used to integrate and fuse multi-source data, build a distribution network system data model, and then process and filter the data; The distribution network system data model is constructed as follows: Collect data from various types of sensors and monitoring equipment at each node of the distribution network system, including current ,Voltage ,temperature ; De-noise the raw data, fill in missing values, and perform data standardization. Perform feature extraction on the data collected by each sensor to obtain a high-dimensional feature vector. Then use the autoencoder to reduce the dimension of the high-dimensional features to obtain a low-dimensional feature representation. The low-dimensional feature vectors from different sensors are fused to construct the overall feature representation of the distribution network system; Use the fused feature vectors to build a data model for the distribution network system; Based on deep reinforcement learning methods, an intelligent decision-making model for distribution network system is constructed; The steps to build an intelligent decision-making model for the distribution network system include: The distribution network system is modeled as a reinforcement learning environment, including an agent, which performs different actions in different states and obtains corresponding rewards; Let state represent the current state of the distribution network system. Indicates the current moment, action Indicates that the agent is in state Rewards for actions taken Representing an Agent Execute an action Feedback obtained afterwards; Use a deep neural network as the agent’s policy network, taking the state as input and outputting the probability distribution of the action; Assume that the agent's policy network is represented as ,in represents the network parameters, Indicates that the agent is in state Take action The probability of Represents the parameters of the policy network, and softmax represents the activation function; The steps of building the intelligent decision-making model of the distribution network system also include: Building the Reward Function Evaluate the agent at different states Take different actions Degree of goodness: The reward function is used to guide the agent to learn the strategy to optimize the performance of the distribution network system; Use deep Q learning to optimize the agent's policy network. By interacting with the environment, the agent adjusts its strategy based on the reward signal and gradually learns the optimized distribution network decision strategy. The Q value is updated in the following way: ,in, represents the learning rate, represents the discount factor, Represents the action performed by the agent Then transfer to the next state, The next state The best action to take in Build an intelligent decision-making model for the distribution network system based on deep reinforcement learning, and use reinforcement learning algorithms to optimize the model so that it can adapt to different distribution network scenarios; Step 3. Real-time data processing and reasoning: Edge computing devices are deployed at the edge nodes of the distribution network system, and edge computing is used for real-time data processing and reasoning. The real-time reasoning model here is a lightweight processing model that can run quickly on edge devices. At the same time, for distribution network system failures and abnormal conditions, real-time reasoning rules are deployed to quickly diagnose and process the failures; In step 3, the real-time inference rules are deployed as follows: Use autoencoders, isolation forests, and deep neural networks to build anomaly detection models; The deployment loss function measures the difference between the original data and the reconstructed data; Then, based on the output results of the anomaly detection model, deploy real-time inference rules and set thresholds. When the anomaly detection model outputs an abnormal score, the real-time inference rules are triggered; Select the corresponding processing plan and trigger the corresponding maintenance operation according to the abnormality type, equipment information and historical data; Step 4. Operation status and data change monitoring: Based on the distributed monitoring results, the operation status and data changes of the distribution network system are monitored in real time, including voltage fluctuations and current anomalies. Based on heterogeneous data fusion and real-time reasoning results, real-time diagnosis and early warning of system faults and anomalies are carried out; It also provides a visual interface to display real-time monitoring data and fault diagnosis results, helping operation and maintenance personnel to discover and handle problems in a timely manner.

[0013] The present invention also provides a distribution network operation and maintenance test device based on micro modular processing. The distribution network operation and maintenance test device in this application is intended to achieve real-time monitoring, data fusion, intelligent decision-making and fault processing of the distribution network, and adopts a modular design, including: Real-time reasoning system, responsible for real-time monitoring of the operating status of the distribution network system, and rapid diagnosis and processing of system failures and abnormal conditions; The real-time reasoning system includes a distributed monitoring module and a data processing module; Operation status monitoring and early warning module, real-time monitoring of the operation status and data changes of the distribution network system; Distributed monitoring module, responsible for distributing monitoring tasks to different micro-modules, to provide comprehensive coverage of the distribution network system; Distributed monitoring modules include: The task division unit divides the monitoring area into sub-areas and estimates the monitoring task volume of each sub-area; Allocation optimization unit, establishes task allocation optimization model and provides balanced task allocation; Task dynamic unit, dynamically allocates monitoring tasks according to optimization results, and realizes data exchange and synchronization between modules through message passing mechanism; The data processing module is responsible for fusing and processing data from different sensors and building a data model for the distribution network system; The data processing module includes: Extract dimension reduction units, extract features from each sensor data, reduce the dimension and fuse them into an overall feature representation, and build a data model of the distribution network system based on the fused feature vector; Intelligent decision-making module, used to build an intelligent decision-making model for the distribution network system, and to intelligently manage and optimize the distribution network system; The intelligent decision-making module models the distribution network system as a reinforcement learning environment, sets the state, action and reward, uses a deep neural network to build the agent's strategy network, and realizes the mapping from state to action; Then, the deep Q-learning algorithm is used to optimize the policy network and gradually learn the optimized distribution network decision-making strategy.

[0014] The present invention modularizes the distribution network operation and maintenance test device. First, the distributed monitoring module distributes the monitoring tasks to different micro modules. Each micro module is responsible for monitoring a specific area, thereby achieving comprehensive coverage of the distribution network system. Through task division, task allocation optimization and task dynamic allocation steps, the balanced allocation and efficient execution of monitoring tasks are ensured, making the monitoring work of the distribution network system more comprehensive and effective. Secondly, the data processing module is responsible for fusing and processing data from different sensors, building a data model for the distribution network system, preprocessing, feature extraction and dimensionality reduction fusion of the original data, and providing data support for intelligent decision-making. Then, the intelligent decision-making module models the distribution network system as a reinforcement learning environment, and uses deep neural networks to build a policy network for intelligent agents. By interacting with the environment, it gradually learns the optimized distribution network decision-making strategy, so that the distribution network system can autonomously learn and optimize decision-making strategies according to different environments and needs, thereby improving the intelligence level and adaptability of the system. The present invention adopts a lightweight processing model in real-time reasoning, runs quickly on edge devices, realizes rapid diagnosis and processing of distribution network system faults and abnormal conditions, deploys real-time reasoning rules to realize rapid fault diagnosis and processing, and improves the reliability and stability of the system; In summary, the distributed monitoring of the present invention has comprehensive monitoring coverage of the distribution network system, so that the monitoring tasks can be distributed to different micro-modules, thereby improving the comprehensiveness and timeliness of monitoring and making decisions more flexible. The intelligent decision-making model based on deep reinforcement learning is adopted, so that the system can autonomously learn and optimize decision-making strategies according to real-time system status and environmental changes, thereby improving the flexibility and adaptability of the system. In addition, faults can be processed in real time. The reasoning module adopts a lightweight processing model and runs quickly on edge devices, thereby improving the real-time response capability and reliability of the system, making the operation and maintenance management of the distribution network more efficient and reliable.

[0015] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A distribution network operation and maintenance test method based on micro-modular processing, characterized in that: include: Step 1. Perform distributed real-time monitoring. The distributed monitoring module distributes the monitoring tasks to different micro-modules. Each micro-module is responsible for monitoring a specific area, providing comprehensive coverage of the distribution network system. The monitoring process uses distributed message transmission to exchange and synchronize data between modules. Step 2. Heterogeneous data fusion: Based on the data from different types of sensors and monitoring equipment, including current, voltage, and temperature, heterogeneous data fusion is used to integrate and fuse multi-source data, build a distribution network system data model, and then process and filter the data; The distribution network system data model is constructed as follows: Collect data from various types of sensors and monitoring equipment at each node of the distribution network system, including current ,Voltage ,temperature ; De-noise the raw data, fill in missing values, and perform data standardization. Perform feature extraction on the data collected by each sensor to obtain a high-dimensional feature vector. Then use the autoencoder to reduce the dimension of the high-dimensional features to obtain a low-dimensional feature representation. The low-dimensional feature vectors from different sensors are fused to construct the overall feature representation of the distribution network system; Use the fused feature vectors to build a data model for the distribution network system; Based on deep reinforcement learning methods, an intelligent decision-making model for distribution network system is constructed; Step 3. Real-time data processing and reasoning: Edge computing devices are deployed at the edge nodes of the distribution network system, and edge computing is used for real-time data processing and reasoning. The real-time reasoning model here is a lightweight processing model. At the same time, for distribution network system failures and abnormal conditions, real-time reasoning rules are deployed to quickly diagnose and process the failures; Step 4. Operation status and data change monitoring: Based on the distributed monitoring results, the operation status and data changes of the distribution network system are monitored in real time, including voltage fluctuations and current anomalies. Based on heterogeneous data fusion and real-time reasoning results, real-time diagnosis and early warning of system faults and anomalies are carried out; It also provides a visual interface to display real-time monitoring data and fault diagnosis results.

2. The distribution network operation and maintenance test method based on micro modular processing according to claim 1 is characterized in that: In the distributed monitoring step, the monitoring area is first divided and the task volume of each monitoring area is estimated: The monitoring area of ​​the distribution network system is divided into sub-areas, each of which corresponds to a micromodule , Represents the module number, for each sub-area Estimated monitoring workload , Indicates The monitoring task volume of each micromodule is Estimate based on regional power demand and line density factors; Establish a task allocation optimization model to minimize the system monitoring task volume and evenly distribute the monitoring tasks of each micro-module; The optimization objective function is to minimize the weighted sum of the monitoring tasks of each micro-module, which means minimizing the overall monitoring task volume of the system. Then: ,in, Represents each sub-region The weights of the , and at the same time establish constraints; The constraints are: Each micromodule The monitoring task volume cannot exceed the maximum task volume it can carry, that is, ,in Each micromodule The maximum amount of tasks; Each monitoring task is assigned to only one micromodule, i.e. , , here , Both represent the index of micromodules, that is, two different micromodules. Represents a set of monitoring tasks and The intersection of is an empty set, that is, the task set and There is no common mission; Then, the heuristic algorithm is used to solve the task allocation optimization model and obtain the task allocation plan; Based on the optimization results, the monitoring tasks are dynamically assigned to each micromodule; A task scheduling algorithm is adopted, and data exchange and synchronization between modules are carried out through a distributed message passing mechanism.

3. The distribution network operation and maintenance test method based on micro modular processing according to claim 2 is characterized in that: The steps to build an intelligent decision-making model for the distribution network system include: The distribution network system is modeled as a reinforcement learning environment, including an agent, which performs different actions in different states and obtains corresponding rewards; Let state represent the current state of the distribution network system. Indicates the current moment, action Indicates that the agent is in state Rewards for actions taken Representing an Agent Execute an action Feedback obtained afterwards; Use a deep neural network as the agent’s policy network, taking the state as input and outputting the probability distribution of the action; Assume that the agent's policy network is represented as ,in represents the network parameters, Indicates that the agent is in state Take action The probability of represents the parameters of the policy network, and softmax represents the activation function.

4. The distribution network operation and maintenance test method based on micro modular processing according to claim 3 is characterized in that: The steps of building the intelligent decision-making model of the distribution network system also include: Building the Reward Function Evaluate the agent at different states Take different actions How good or bad is it? Use deep Q learning to optimize the agent's policy network. By interacting with the environment, the agent adjusts its strategy based on the reward signal and gradually learns the optimized distribution network decision strategy. The Q value is updated in the following way: ,in, represents the learning rate, represents the discount factor, Represents the action performed by the agent Then transfer to the next state, The next state The best action to take.

5. The distribution network operation and maintenance test method based on micro modular processing according to claim 4 is characterized in that: In step 3, the real-time inference rules are deployed as follows: Use autoencoders, isolation forests, and deep neural networks to build anomaly detection models; The deployment loss function measures the difference between the original data and the reconstructed data; Then, based on the output results of the anomaly detection model, deploy real-time inference rules and set thresholds. When the anomaly detection model outputs an abnormal score, the real-time inference rules are triggered; Select the corresponding processing plan based on the exception type, equipment information and historical data, and trigger the corresponding maintenance operation.

6. A distribution network operation and maintenance test device based on micro-modular processing, adopting a distribution network operation and maintenance test method based on micro-modular processing as claimed in any one of claims 1 to 5, characterized in that: include: Real-time reasoning system, responsible for real-time monitoring of the operating status of the distribution network system, and rapid diagnosis and processing of system failures and abnormal conditions; The real-time reasoning system includes a distributed monitoring module and a data processing module; Operation status monitoring and early warning module, real-time monitoring of the operation status and data changes of the distribution network system; Distributed monitoring module, responsible for distributing monitoring tasks to different micro-modules, to provide comprehensive coverage of the distribution network system; Distributed monitoring modules include: The task division unit divides the monitoring area into sub-areas and estimates the monitoring task volume of each sub-area; Allocation optimization unit, establishes task allocation optimization model and provides balanced task allocation; Task dynamic unit, dynamically allocates monitoring tasks according to optimization results, and realizes data exchange and synchronization between modules through message passing mechanism; The data processing module is responsible for fusing and processing data from different sensors and building a data model for the distribution network system; The data processing module includes: Extract dimensionality reduction units, extract features from each sensor data, reduce the dimension and fuse them into an overall feature representation, and build a data model of the distribution network system based on the fused feature vector.

7. The distribution network operation and maintenance test device based on micro modular processing according to claim 6, characterized in that: The distribution network operation and maintenance test device based on micro-modular processing also includes: Intelligent decision-making module, used to build an intelligent decision-making model for the distribution network system, and to intelligently manage and optimize the distribution network system; The intelligent decision-making module models the distribution network system as a reinforcement learning environment, sets the state, action and reward, uses a deep neural network to build the agent's strategy network, and realizes the mapping from state to action; Then, the deep Q-learning algorithm is used to optimize the policy network and gradually learn the optimized distribution network decision-making strategy.

8. An electronic device, characterized in that: include: A processor, and a memory connected to the processor; The memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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