Power monitoring Scada system

Abstract

The invention discloses an electric power monitoring Scada system, and particularly relates to the technical field of electric power monitoring. The system comprises an environment monitoring and safety protection module used for monitoring the environment and safety state of the power distribution cabinet in real time, an autonomous maintenance and intelligent learning module used for optimizing a maintenance strategy through self-diagnosis and learning, and a user interface and augmented reality module used for providing a visual user interface and an augmented reality technology. According to the invention, comprehensive monitoring and optimization of the power distribution cabinet are realized through the environment monitoring and safety protection module, the autonomous maintenance and intelligent learning module, the user interface and augmented reality module and the intelligent energy management and sharing module; the environment monitoring and safety protection module ensures the internal environment and the safety state of the power distribution cabinet through a microbial sensing module, a gas analysis module, a humidity and temperature control module, a human body perception sensing module and the like, and once potential threats are detected, the system can automatically start emergency braking and carry out identity verification so as to ensure that only authorized personnel can operate the power distribution cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and in particular to a power monitoring Scada system. Background Art

[0002] In modern power systems, distribution cabinets are one of the important equipment to ensure the stability of power distribution and transmission. The normal operation of distribution cabinets directly affects the reliability and safety of power supply. Therefore, real-time monitoring and maintenance of distribution cabinets are particularly important. Traditional distribution cabinet monitoring systems mainly rely on manual inspections and basic sensor data. There are problems such as low monitoring accuracy, slow response speed, and inability to fully grasp the status of equipment, which makes it difficult to meet the needs of efficient and reliable operation of modern power systems. With the development of smart grids, power monitoring SCADA (Supervisory Control and Data Acquisition) systems have emerged. Through real-time monitoring, fault warning and intelligent management of distribution cabinets, efficient, safe and intelligent management of power systems can be achieved.

[0003] The existing power monitoring SCADA system has some shortcomings in practical applications. First, the traditional system does not monitor environmental factors comprehensively enough, making it difficult to grasp the internal environmental status of the distribution cabinet in real time, resulting in the inability to provide timely warnings of potential risks. Second, the maintenance of the existing system relies on manual operation, which is inefficient and error-prone, and cannot achieve intelligent and autonomous maintenance management. In addition, the user interface is not friendly enough, the operation is complicated, and it is unable to provide intuitive operation guidance and remote assistance, which affects the system's efficiency and fault handling speed. In order to solve these problems, we proposed a power monitoring SCADA system. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a power monitoring Scada system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A power monitoring Scada system, comprising an environmental monitoring and safety protection module for real-time monitoring of the environment and safety status of a power distribution cabinet, an autonomous maintenance and intelligent learning module for optimizing maintenance strategies through self-diagnosis and learning, a user interface and augmented reality module for providing an intuitive user interface and augmented reality technology, and an intelligent energy management and sharing module for optimizing energy distribution and utilization;

[0007] The environmental monitoring and safety protection module includes a microbial sensor module for monitoring the microbial conditions inside the power distribution cabinet, a gas analysis module for detecting harmful gases, a humidity and temperature control module for adjusting the internal environment of the power distribution cabinet, a human perception sensor module for detecting whether someone is close to the power distribution cabinet, an emergency brake module for automatically enabling safety protection measures, and an identity authentication module for ensuring that only authorized personnel can operate the power distribution cabinet;

[0008] The autonomous maintenance and intelligent learning module includes an inspection machine module for automatically inspecting the interior of the distribution cabinet to identify potential fault points, a cleaning machine module for cleaning the interior of the distribution cabinet, a maintenance machine module for performing simple replacement and repair work, a usage pattern learning module for learning the usage pattern and load changes of the distribution cabinet to optimize power distribution, a failure pattern recognition module for identifying and predicting possible failure modes, and a performance optimization suggestion module for providing maintenance and optimization suggestions.

[0009] The present invention is further configured as follows: the user interface and augmented reality module include a real-time AR display module for displaying the internal circuit diagram, equipment status and operation guide of the power distribution cabinet through AR technology, a fault location guidance module for intuitively displaying the fault location and maintenance steps using AR technology, and a remote assistance module for enabling experts to remotely guide maintenance personnel to perform operations;

[0010] The intelligent energy management and sharing module includes a dynamic load management module for intelligently adjusting the load of the distribution cabinet to optimize the distribution of electric energy, an energy sharing platform module for realizing the sharing and allocation of electric energy within the community or industrial park, and a new energy access management module for supporting and managing the access of new energy equipment such as solar energy and electric vehicle charging piles.

[0011] The present invention is further configured as follows: the usage mode learning module includes a multi-dimensional data fusion module for integrating data sources such as electricity, environment, and social and economic activities, a virtual environment simulation module for creating a virtual distribution cabinet environment to simulate the performance of equipment under different conditions, a bio-perception behavior analysis module for monitoring and analyzing the impact of operators' behaviors and habits on the operation of the distribution cabinet, a blockchain data assurance module for using blockchain technology to ensure the authenticity and non-tamperability of data, a neural network self-optimization module for optimizing learning algorithms in real time through deep learning neural networks to adapt to complex patterns, a situational cognition determination module for determining the best operating mode of the distribution cabinet in different usage scenarios through machine learning and situational perception technology, and a biological evolution algorithm module for selecting the optimal strategy for multi-objective optimization using a biological evolution algorithm;

[0012] The present invention is further configured as follows: the scenario recognition and determination module includes a real-time scenario monitoring module for collecting the operation data of the power distribution cabinet in real time through sensors and an environmental monitoring system, a scenario classification and identification module for analyzing the collected data using a machine learning algorithm, and classifying and identifying the current usage scenario, and a scenario determination module for entering two different determination branches according to the identified scenario:

[0013] A. an operation strategy determination module for determining conventional scenarios and recommending the best operation strategy, an application optimization control module for executing specific optimization and control operations according to the determined best operation strategy, and a biological evolution algorithm module for performing multi-objective optimization using a biological evolution algorithm;

[0014] B. An emergency response strategy module for determining an emergency scenario and initiating an emergency response strategy, and an initiating emergency measure module for rapidly initiating emergency measures according to the determined emergency response strategy.

[0015] The present invention is further configured as follows: the multi-dimensional data fusion module integrates data sources such as electricity, environment, and social and economic activities to provide data support for the virtual environment simulation module to create a simulated environment for equipment performance under different conditions; the virtual environment simulation module simulates different operating conditions and environments for the bio-perception behavior analysis module to monitor and analyze the impact of operators' behaviors and habits under these conditions on the operation of the distribution cabinet; the data generated by the bio-perception behavior analysis module is recorded and stored through a blockchain data assurance module to ensure the authenticity and non-tamperability of the data; the blockchain data assurance module provides reliable data to the neural network self-optimization module to help it adapt to complex patterns through real-time optimization of the learning algorithm through deep learning; the learning algorithm optimized by the neural network self-optimization module is applied to the scenario recognition judgment module to determine the optimal operating mode of the distribution cabinet under different usage scenarios; the scenario recognition judgment module relies on the real-time scenario monitoring module to collect real-time distribution cabinet operation data through sensors and environmental monitoring systems.

[0016] The present invention is further configured as follows: the data collected by the real-time scenario monitoring module is analyzed and classified by the scenario classification and identification module to identify the current usage scenario; the usage scenario information identified by the scenario classification and identification module is transmitted to the scenario determination module to decide to enter the determination branch; in normal scenarios, the scenario determination module transmits information to the operation strategy determination module, which determines and recommends the best operation strategy; the best operation strategy recommended by the operation strategy determination module is executed by the application optimization control module for specific optimization and control operations; the optimization strategy of the application optimization control module can be further multi-objective optimized to select the optimal strategy through the biological evolution algorithm module; the scenario determination module transmits information to the emergency response strategy module in an emergency scenario, which determines and starts the emergency response strategy; the emergency response strategy module quickly starts specific emergency measures according to the determined emergency response strategy to ensure the safe and stable operation of the system.

[0017] The present invention is further configured as follows: the microbial sensor module monitors the microbial conditions inside the power distribution cabinet and provides environmental data for the gas analysis module to detect harmful gases; the results of the harmful gas detection by the gas analysis module are fed back to the humidity and temperature control module for adjusting the internal environment of the power distribution cabinet; the humidity and temperature control module adjusts the internal environment of the power distribution cabinet and provides data to the human perception sensor module to monitor whether someone is approaching; when the human perception sensor module detects that someone is approaching, it transmits the information to the emergency braking module to automatically enable safety protection measures; when enabling safety protection measures, the emergency braking module verifies the identity information to ensure that only authorized personnel can operate the power distribution cabinet.

[0018] The present invention is further configured as follows: the inspection machine module automatically inspects the inside of the distribution cabinet to identify potential fault points and notifies the cleaning machine module to clean the inside of the distribution cabinet; after the cleaning machine module completes the cleaning, it passes the information to the maintenance machine module for performing simple replacement and maintenance work; during the maintenance process, the maintenance machine module collects usage pattern data and passes it to the usage pattern learning module to optimize power distribution; the usage pattern learning module optimizes power distribution through deep learning and provides data to the fault pattern recognition module to identify and predict possible faults; after the fault pattern recognition module identifies and predicts possible faults, it provides suggestions to the performance optimization suggestion module; the performance optimization suggestion module provides maintenance and optimization suggestions to the usage pattern learning module for further optimization based on the identification and prediction.

[0019] The present invention is further configured as follows: the real-time AR display module displays the internal circuit diagram, equipment status and operation guide of the distribution cabinet to the fault location guidance module through AR technology; the fault location guidance module uses AR technology to intuitively display the fault location and maintenance steps and cooperates with the remote assistance module to realize expert remote guidance; the remote assistance module realizes expert remote guidance of maintenance personnel to perform operations and provides data to the dynamic load management module to intelligently adjust the load of the distribution cabinet.

[0020] The present invention is further configured as follows: the dynamic load management module optimizes the distribution of electric energy and transmits data to the energy sharing platform module to realize the sharing and allocation of electric energy within the community or industrial park; the energy sharing platform module realizes the sharing and allocation of electric energy and supports the new energy access management module to manage the access of new energy equipment such as solar energy and electric vehicle charging piles.

[0021] The beneficial effects of the present invention are:

[0022] 1. The present invention realizes comprehensive monitoring and optimization of the power distribution cabinet through the environmental monitoring and safety protection module, the autonomous maintenance and intelligent learning module, the user interface and augmented reality module, and the intelligent energy management and sharing module. The environmental monitoring and safety protection module ensures the internal environment and safety status of the power distribution cabinet through modules such as microbial sensing, gas analysis, humidity and temperature control, and human perception sensing. Once a potential threat is detected, the system will automatically enable emergency braking and perform identity authentication to ensure that only authorized personnel can operate;

[0023] 2. The present invention uses inspection machines, cleaning machines and repair machines for daily maintenance and troubleshooting through autonomous maintenance and intelligent learning modules, while optimizing power distribution through deep learning and usage patterns. The user interface and augmented reality modules provide intuitive operation guides and fault location guidance through AR technology, and collaborate with the remote assistance module to achieve expert remote guidance;

[0024] 3. The present invention realizes the optimal distribution and sharing of electric energy through the intelligent energy management and sharing module through dynamic load management and energy sharing platform, and supports the access management of new energy equipment. On the whole, the system realizes comprehensive monitoring, fault prevention, energy optimization and intelligent maintenance of distribution cabinets through multi-module collaboration, ensuring efficient, safe and stable operation of the system.

[0025] 4. The present invention integrates multi-dimensional data sources such as electricity, environment, and social and economic activities to create a virtual distribution cabinet environment to simulate the performance of equipment under different conditions, and monitor and analyze the impact of operators' behaviors and habits on the operation of the distribution cabinet. The data generated by the bio-perception behavior analysis module uses blockchain technology to ensure the authenticity and immutability of the data, and provide reliable data to the neural network self-optimization module to help it optimize the learning algorithm in real time through deep learning to adapt to complex patterns. The optimized learning algorithm is applied to the situational cognition judgment module;

[0026] 5. The present invention collects the distribution cabinet operation data through the real-time scenario monitoring module, and the scenario classification and identification module analyzes and classifies the current usage scenario and enters the scenario determination branch. In the conventional scenario, the system recommends the best operation strategy through the operation strategy determination module, and the application optimization control module performs specific optimization and control operations, and further performs multi-objective optimization through the biological evolution algorithm module. In an emergency scenario, the system starts the emergency response strategy and quickly takes emergency measures to ensure the safe and stable operation of the system. Through this multi-level monitoring and optimization strategy, the system improves the operating efficiency and adaptability of the power monitoring SCADA system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the system in the present invention.

[0028] Figure 2 It is a partial system flow diagram of the usage mode learning module in the present invention.

[0029] Figure 3 Schematic diagram of the biological evolution algorithm module in the present invention. DETAILED DESCRIPTION

[0030] The technical solutions 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, rather than all the embodiments.

[0031] Example 1

[0032] like Figure 1 As shown, a power monitoring Scada system includes an environmental monitoring and safety protection module for real-time monitoring of the environment and safety status of a power distribution cabinet, an autonomous maintenance and intelligent learning module for optimizing maintenance strategies through self-diagnosis and learning, a user interface and augmented reality module for providing an intuitive user interface and augmented reality technology, and an intelligent energy management and sharing module for optimizing energy distribution and utilization;

[0033] The environmental monitoring and safety protection module includes a microbial sensor module for monitoring the microbial conditions inside the power distribution cabinet, a gas analysis module for detecting harmful gases, a humidity and temperature control module for adjusting the internal environment of the power distribution cabinet, a human perception sensor module for detecting whether someone is close to the power distribution cabinet, an emergency brake module for automatically enabling safety protection measures, and an identity authentication module for ensuring that only authorized personnel can operate the power distribution cabinet;

[0034] The autonomous maintenance and intelligent learning module includes an inspection machine module for automatically inspecting the inside of the power distribution cabinet to identify potential fault points, a cleaning machine module for cleaning the inside of the power distribution cabinet, a maintenance machine module for performing simple replacement and maintenance work, a usage pattern learning module for learning the usage pattern and load changes of the power distribution cabinet to optimize power distribution, a failure pattern recognition module for identifying and predicting possible failure modes, and a performance optimization suggestion module for providing maintenance and optimization suggestions;

[0035] The user interface and augmented reality module includes a real-time AR display module for displaying the internal circuit diagram, equipment status and operation guide of the power distribution cabinet through AR technology, a fault location guidance module for intuitively displaying the fault location and maintenance steps using AR technology, and a remote assistance module for enabling experts to remotely guide maintenance personnel to perform operations;

[0036] The intelligent energy management and sharing module includes a dynamic load management module for intelligently adjusting the load of the distribution cabinet to optimize the distribution of electric energy, an energy sharing platform module for realizing the sharing and allocation of electric energy in the community or industrial park, and a new energy access management module for supporting and managing the access of new energy equipment such as solar energy and electric vehicle charging piles;

[0037] The microbial sensor module monitors the microbial conditions inside the power distribution cabinet and provides environmental data for the gas analysis module to detect harmful gases; the results of the gas analysis module detecting harmful gases are fed back to the humidity and temperature control module for adjusting the internal environment of the power distribution cabinet; the humidity and temperature control module adjusts the internal environment of the power distribution cabinet and provides data to the human perception sensor module to monitor whether someone is approaching; when the human perception sensor module detects someone approaching, it transmits the information to the emergency brake module to automatically enable safety protection measures; when enabling safety protection measures, the emergency brake module verifies the identity information to ensure that only authorized personnel can operate the power distribution cabinet;

[0038] The inspection machine module automatically inspects the inside of the power distribution cabinet to identify potential fault points and notifies the cleaning machine module to clean the inside of the power distribution cabinet; after the cleaning machine module completes the cleaning, it passes the information to the maintenance machine module for performing simple replacement and maintenance work; during the maintenance process, the maintenance machine module collects usage pattern data and passes it to the usage pattern learning module to optimize power distribution; the usage pattern learning module optimizes power distribution through deep learning and provides data to the fault pattern recognition module to identify and predict possible faults; after the fault pattern recognition module identifies and predicts possible faults, it provides suggestions to the performance optimization suggestion module; the performance optimization suggestion module provides maintenance and optimization suggestions to the usage pattern learning module for further optimization based on the identification and prediction;

[0039] The real-time AR display module uses AR technology to display the internal circuit diagram, equipment status and operation guide of the distribution cabinet to the fault location guidance module; the fault location guidance module uses AR technology to intuitively display the fault location and maintenance steps and cooperates with the remote assistance module to realize expert remote guidance; the remote assistance module realizes expert remote guidance of maintenance personnel to perform operations and provides data to the dynamic load management module to intelligently adjust the load of the distribution cabinet;

[0040] The dynamic load management module optimizes the distribution of electric energy and transmits data to the energy sharing platform module to realize the sharing and deployment of electric energy within the community or industrial park; the energy sharing platform module realizes the sharing and deployment of electric energy and supports the new energy access management module to manage the access of new energy equipment such as solar energy and electric vehicle charging piles.

[0041] In the above embodiment, the comprehensive monitoring and optimization of the power distribution cabinet is realized through the environmental monitoring and safety protection module, the autonomous maintenance and intelligent learning module, the user interface and augmented reality module, and the intelligent energy management and sharing module. The environmental monitoring and safety protection module monitors the microbial conditions inside the power distribution cabinet through the microbial sensor module, provides environmental data to the gas analysis module to detect harmful gases, and feeds back the detection results to the humidity and temperature control module to adjust the internal environment of the power distribution cabinet. The data of the humidity and temperature control module is then passed to the human perception sensor module to monitor whether someone is approaching. Once someone is detected approaching, the information is passed to the emergency brake module, which automatically enables safety protection measures and performs identity authentication to ensure that only authorized personnel can operate the power distribution cabinet. The autonomous maintenance and intelligent learning module automatically inspects the inside of the power distribution cabinet through the inspection machine module, identifies potential fault points, and notifies the cleaning machine module to clean the inside of the power distribution cabinet. After cleaning is completed, the information is passed to the maintenance machine module for performing simple replacement and maintenance work. During the maintenance process, the maintenance machine module collects usage mode data and passes it to the usage mode learning module to optimize power distribution. The usage mode learning module optimizes power distribution through deep learning and provides data to the fault mode recognition module to identify and predict possible faults. After the fault pattern recognition module identifies and predicts, it provides suggestions to the performance optimization suggestion module to further optimize the maintenance strategy. The user interface and augmented reality module displays the internal circuit diagram, equipment status and operation guide of the distribution cabinet to the fault location guidance module through the real-time AR display module. The fault location guidance module uses AR technology to intuitively display the fault location and maintenance steps, and collaborates with the remote assistance module to achieve expert remote guidance. The remote assistance module provides data to the dynamic load management module to intelligently adjust the load of the distribution cabinet. The intelligent energy management and sharing module optimizes the distribution of electric energy through the dynamic load management module, and passes the data to the energy sharing platform module to realize the sharing and allocation of electric energy in the community or industrial park. The energy sharing platform module supports the new energy access management module to manage the access of new energy equipment such as solar energy and electric vehicle charging piles. Overall, the system realizes comprehensive monitoring, fault prevention, energy optimization and intelligent maintenance of the distribution cabinet through multi-module collaboration to ensure efficient, safe and stable operation of the system.

[0042] Example 2

[0043] like Figure 1-3As shown, a power monitoring Scada system, the usage mode learning module includes a multi-dimensional data fusion module for integrating data sources such as power, environment, and social and economic activities, a virtual environment simulation module for creating a virtual distribution cabinet environment to simulate the performance of equipment under different conditions, a bio-perception behavior analysis module for monitoring and analyzing the impact of operators' behaviors and habits on the operation of the distribution cabinet, a blockchain data assurance module for using blockchain technology to ensure the authenticity and immutability of data, a neural network self-optimization module for optimizing learning algorithms in real time through deep learning neural networks to adapt to complex patterns, a situational recognition determination module for determining the best operating mode of the distribution cabinet in different usage scenarios through machine learning and situational perception technology, and a biological evolution algorithm module for selecting the optimal strategy for multi-objective optimization using a biological evolution algorithm;

[0044] The scenario recognition and determination module includes a real-time scenario monitoring module for collecting the operation data of the power distribution cabinet in real time through sensors and environmental monitoring systems, a scenario classification and identification module for analyzing the collected data using a machine learning algorithm, and classifying and identifying the current usage scenario, and a scenario determination module for entering two different determination branches according to the identified scenario:

[0045] A. an operation strategy determination module for determining conventional scenarios and recommending the best operation strategy, an application optimization control module for executing specific optimization and control operations according to the determined best operation strategy, and a biological evolution algorithm module for performing multi-objective optimization using a biological evolution algorithm;

[0046] B. an emergency response strategy module for determining an emergency scenario and initiating an emergency response strategy, and an initiation emergency measure for rapidly initiating emergency measures according to the determined emergency response strategy;

[0047] The multi-dimensional data fusion module integrates data sources such as electricity, environment, and social and economic activities to provide data support for the virtual environment simulation module to create a simulated environment for equipment performance under different conditions; the virtual environment simulation module simulates different operating conditions and environments for the bio-perception behavior analysis module to monitor and analyze the impact of operators' behaviors and habits under these conditions on the operation of the distribution cabinet; the data generated by the bio-perception behavior analysis module is recorded and stored by the blockchain data assurance module to ensure the authenticity and non-tamperability of the data; the blockchain data assurance module provides reliable data to the neural network self-optimization module to help it optimize the learning algorithm through deep learning in real time to adapt to complex patterns; the learning algorithm optimized by the neural network self-optimization module is applied to the scenario recognition judgment module to determine the optimal operating mode of the distribution cabinet in different usage scenarios; the scenario recognition judgment module relies on the real-time scenario monitoring module to collect the distribution cabinet operation data in real time through sensors and environmental monitoring systems;

[0048] The data collected by the real-time scenario monitoring module is analyzed and classified by the scenario classification and identification module to identify the current usage scenario; the usage scenario information identified by the scenario classification and identification module is passed to the scenario determination module to decide to enter the determination branch; in normal scenarios, the scenario determination module passes the information to the operation strategy determination module, which determines and recommends the best operation strategy; the best operation strategy recommended by the operation strategy determination module is executed by the application optimization control module for specific optimization and control operations; the optimization strategy of the application optimization control module can be further multi-objective optimized through the biological evolution algorithm module to select the optimal strategy; the scenario determination module passes the information to the emergency response strategy module in an emergency scenario, which determines and starts the emergency response strategy; the emergency response strategy module quickly starts specific emergency measures according to the determined emergency response strategy to ensure the safe and stable operation of the system.

[0049] In the above embodiment, the virtual environment simulation module simulates different operating conditions and environments for the bio-perception behavior analysis module to monitor and analyze the impact of the operator's behavior and habits under these conditions on the operation of the distribution cabinet. The data generated by the bio-perception behavior analysis module is recorded and stored by the blockchain data assurance module to ensure the authenticity and immutability of the data. The blockchain data assurance module provides reliable data to the neural network self-optimization module to help it optimize the learning algorithm in real time through deep learning to adapt to complex patterns. The optimized learning algorithm is applied to the scenario recognition judgment module to determine the optimal operating mode of the distribution cabinet in different usage scenarios. The scenario recognition judgment module relies on the real-time scenario monitoring module to collect the distribution cabinet operation data in real time through sensors and environmental monitoring systems, and pass the collected data to the scenario classification and identification module for analysis and classification to identify the current usage scenario. The identified usage scenario information is passed to the scenario judgment module to decide whether to enter the conventional or emergency scenario judgment branch. In the conventional scenario, the scenario judgment module passes the information to the operation strategy judgment module, which determines and recommends the best operation strategy, which is executed by the application optimization control module to perform specific optimization and control operations, and further performs multi-objective optimization through the biological evolution algorithm module to select the optimal strategy. In an emergency scenario, the scenario determination module passes information to the emergency response strategy module, which determines and initiates the emergency response strategy and quickly initiates specific emergency measures to ensure the safe and stable operation of the system.

[0050] Among them, the biological evolution algorithm module can optimize the system by combining the genetic algorithm and the particle swarm optimization algorithm:

[0051] The main steps and formulas of genetic algorithm are:

[0052] 1. Initialize the population:

[0053] Generate an initial population P(t), each individual (chromosome) represents a possible solution. The population size is (N).

[0054] An individual is represented by a vector x=(x 1 ,x 2 ,…,x d ), where (d) is the dimension of the decision variables.

[0055] 2. Fitness evaluation:

[0056] Calculate the fitness value (f(x)) of each individual to measure the quality of the individual.

[0057] The fitness function (f(x)) can be a performance indicator of the distribution cabinet, such as energy efficiency, reliability, etc.

[0058] 3. Select:

[0059] Use methods such as roulette and tournament selection to select individuals with high fitness to form a new population.

[0060] The probability of selection (p i ):

[0061]

[0062] 4. Crossover:

[0063] Select two individuals for crossover operation to generate new individuals. The crossover rate is (p c ).

[0064] One-point crossover: [xnew=(xparent1[1:k],x parent2 [k+1:d])] where (k) is the intersection point.

[0065] 5. Mutation:

[0066] Mutation operations are performed on some genes in individuals, and the mutation rate is (p m ).

[0067] Mutation operation: [x i =x i +δ]where (δ) is a random perturbation.

[0068] 6. Generate new population:

[0069] The new individuals generated by selection, crossover and mutation operations form a new population (P(t+1)).

[0070] 7. Termination conditions:

[0071] Determine whether the termination condition is met (such as reaching the maximum number of iterations or the fitness is no longer significantly improved). If so, output the optimal solution.

[0072] The main steps and formulas of particle swarm optimization algorithm are:

[0073] 1. Initialize particle swarm:

[0074] Initialize particle swarm x=(x 1 ,x 2 ,…,x n ), each particle represents a possible solution.

[0075] Initialization speed V=(v 1 ,v 2 ,…,v N ).

[0076] 2. Speed ​​update:

[0077] Update the velocity of each particle (v i (t+1)): [v i (t+1)=ωv i (t)+c 1 r 1 (p i -x i (t))+c 2 r 2 (gx i (t))]

[0078] Among them, (ω) is the inertia weight, (c 1 ) and (c 2 ) is the acceleration constant, (r 1 ) and (r 2 ) is a random number, (p i ) is the individual optimal position of particle (i), and (g) is the global optimal position.

[0079] 3. Location Update:

[0080] Update the position of each particle (x i ):

[0081] x i (t+1)=x i (t)+v i (t+1)

[0082] 4. Individual and global optimal updates:

[0083] Update the individual optimal position of each particle (p i ):

[0084] If (f(x i(t+1))>f(p i (t)), then (p i (t+1)) take (x i (t+1)).

[0085] p i (t+1)=x i (t+1)

[0086] Otherwise, (p i (t+1)) maintain (p i The value of (t)) remains unchanged.

[0087] p i (t+1)=p i (t)

[0088] Update the global optimal position

[0089]

[0090] in: is the variable function p i (t+1) The value of the variable when it reaches its maximum value during the change process.

[0091] 5. Termination conditions:

[0092] Determine whether the termination condition is met (such as reaching the maximum number of iterations or the fitness is no longer significantly improved). If so, output the optimal solution.

[0093] Among them, 1. Use genetic algorithm to perform global search and find multiple optimal solutions as much as possible. The steps are as follows:

[0094] Initialize the population (P(t)).

[0095] Perform fitness evaluation (f(x)).

[0096] Selection, crossover and mutation generate new populations.

[0097] Iterate several generations and output the global optimal solution (x GA ).

[0098] 2. Use particle swarm optimization algorithm in (x GA ) is used for local optimization. The steps are as follows:

[0099] Initialize the particle swarm with (x GA ) is the initial solution.

[0100] Update speed and position.

[0101] Update individual and global optima.

[0102] Iterate several times and output the optimal solution after local optimization (x PSO ).

[0103] Through this combination strategy, the combination of genetic algorithm and particle swarm optimization algorithm can give full play to their respective advantages, achieve global and local comprehensive optimization, and improve the optimization effect and operation efficiency of the power monitoring SCADA system.

[0104] Working principle: When the present invention is in use, it first realizes real-time monitoring of the internal environment of the power distribution cabinet through the environmental monitoring and safety protection module. The module includes a microbial sensor module, a gas analysis module, a humidity and temperature control module, a human perception sensor module, an emergency braking module and an identity authentication module. The microbial sensor module monitors the internal microbial conditions of the power distribution cabinet, and provides the data to the gas analysis module to detect harmful gases. The results are fed back to the humidity and temperature control module to adjust the internal environment. The data from the humidity and temperature control module is then passed to the human perception sensor module to monitor whether someone is approaching. Once someone is detected approaching, the emergency braking module will automatically enable safety protection measures and verify the identity to ensure that only authorized personnel can operate the power distribution cabinet.

[0105] Next, the autonomous maintenance and intelligent learning module is responsible for the daily maintenance and intelligent optimization of the power distribution cabinet. The inspection machine module automatically inspects the inside of the power distribution cabinet, identifies potential fault points and notifies the cleaning machine module to clean the inside. After cleaning, the information is passed to the maintenance machine module to perform simple replacement and maintenance work. During this process, the maintenance machine module collects usage pattern data and passes it to the usage pattern learning module, which optimizes power distribution through deep learning and provides data to the fault pattern recognition module to identify and predict possible faults. The fault pattern recognition module provides the identification and prediction results to the performance optimization suggestion module to further optimize the maintenance strategy.

[0106] In the usage mode learning module, the virtual environment simulation module simulates different operating conditions and environments for the bio-perception behavior analysis module to monitor and analyze the impact of the operator's behavior and habits under these conditions on the operation of the distribution cabinet. The data generated by the bio-perception behavior analysis module is recorded and stored through the blockchain data assurance module to ensure the authenticity and immutability of the data. The blockchain data assurance module provides reliable data to the neural network self-optimization module to help it optimize the learning algorithm in real time through deep learning to adapt to complex patterns. The optimized learning algorithm is applied to the scenario recognition judgment module to determine the optimal operation mode of the distribution cabinet in different usage scenarios. The scenario recognition judgment module relies on the real-time scenario monitoring module to collect the distribution cabinet operation data in real time through sensors and environmental monitoring systems, and pass the collected data to the scenario classification and identification module for analysis and classification to identify the current usage scenario. The identified usage scenario information is passed to the scenario judgment module to decide whether to enter the regular or emergency scenario judgment branch. In the regular scenario, the scenario judgment module passes the information to the operation strategy judgment module, which determines and recommends the best operation strategy, which is executed by the application optimization control module to perform specific optimization and control operations, and further performs multi-objective optimization through the biological evolution algorithm module to select the optimal strategy. In an emergency scenario, the scenario determination module passes information to the emergency response strategy module, which determines and initiates the emergency response strategy and quickly initiates specific emergency measures to ensure the safe and stable operation of the system.

[0107] Finally, the user interface and augmented reality module and the intelligent energy management and sharing module provide the system with an intuitive operating interface and optimized energy management. The real-time AR display module uses AR technology to display the internal circuit diagram, equipment status and operation guide of the distribution cabinet. The fault location guidance module uses AR technology to intuitively display the fault location and maintenance steps, and collaborates with the remote assistance module to achieve expert remote guidance. The remote assistance module provides data to the dynamic load management module to intelligently adjust the load of the distribution cabinet. The dynamic load management module optimizes the distribution of electric energy and passes the data to the energy sharing platform module to realize the sharing and allocation of electric energy in the community or industrial park. The energy sharing platform module supports the new energy access management module to manage the access of new energy equipment such as solar energy and electric vehicle charging piles. Overall, the system realizes comprehensive monitoring, fault prevention, energy optimization and intelligent maintenance of the distribution cabinet through multi-module collaboration to ensure efficient, safe and stable operation of the system.

[0108] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A power monitoring Scada system, characterized in that: It includes an environmental monitoring and safety protection module for real-time monitoring of the environment and safety status of the power distribution cabinet, an autonomous maintenance and intelligent learning module for optimizing maintenance strategies through self-diagnosis and learning, a user interface and augmented reality module for providing an intuitive user interface and augmented reality technology, and an intelligent energy management and sharing module for optimizing energy distribution and utilization; The environmental monitoring and safety protection module includes a microbial sensor module for monitoring the microbial conditions inside the power distribution cabinet, a gas analysis module for detecting harmful gases, a humidity and temperature control module for adjusting the internal environment of the power distribution cabinet, a human perception sensor module for detecting whether someone is close to the power distribution cabinet, an emergency brake module for automatically enabling safety protection measures, and an identity authentication module for ensuring that only authorized personnel can operate the power distribution cabinet; The autonomous maintenance and intelligent learning module includes an inspection machine module for automatically inspecting the interior of the distribution cabinet to identify potential fault points, a cleaning machine module for cleaning the interior of the distribution cabinet, a maintenance machine module for performing simple replacement and repair work, a usage pattern learning module for learning the usage pattern and load changes of the distribution cabinet to optimize power distribution, a failure pattern recognition module for identifying and predicting possible failure modes, and a performance optimization suggestion module for providing maintenance and optimization suggestions.

2. A power monitoring Scada system according to claim 1, characterized in that: The user interface and augmented reality module includes a real-time AR display module for displaying the internal circuit diagram, equipment status and operation guide of the power distribution cabinet through AR technology, a fault location guidance module for intuitively displaying the fault location and maintenance steps using AR technology, and a remote assistance module for enabling experts to remotely guide maintenance personnel to perform operations; The intelligent energy management and sharing module includes a dynamic load management module for intelligently adjusting the load of the distribution cabinet to optimize the distribution of electric energy, an energy sharing platform module for realizing the sharing and allocation of electric energy within the community or industrial park, and a new energy access management module for supporting and managing the access of new energy equipment such as solar energy and electric vehicle charging piles.

3. A power monitoring Scada system according to claim 2, characterized in that: The usage pattern learning module includes a multi-dimensional data fusion module for integrating data sources such as electricity, environment, and social and economic activities, a virtual environment simulation module for creating a virtual distribution cabinet environment to simulate the performance of equipment under different conditions, a bio-perception behavior analysis module for monitoring and analyzing the impact of operators' behaviors and habits on the operation of the distribution cabinet, a blockchain data assurance module for using blockchain technology to ensure the authenticity and immutability of data, a neural network self-optimization module for optimizing learning algorithms in real time through deep learning neural networks to adapt to complex patterns, a situational recognition determination module for determining the optimal operating mode of the distribution cabinet in different usage scenarios through machine learning and situational perception technology, and a biological evolution algorithm module for using biological evolution algorithms to perform multi-objective optimization and select the optimal strategy.

4. A power monitoring Scada system according to claim 3, characterized in that: The scenario recognition and judgment module includes a real-time scenario monitoring module for collecting distribution cabinet operation data in real time through sensors and environmental monitoring systems, a scenario classification and recognition module for analyzing the collected data using machine learning algorithms, and classifying and identifying the current usage scenarios, and a scenario judgment module for entering two different judgment branches according to the identified scenarios.

5. A power monitoring Scada system according to claim 4, characterized in that: The multi-dimensional data fusion module integrates data sources such as electricity, environment, and socio-economic activities to provide data support for the virtual environment simulation module to create a simulated environment for equipment performance under different conditions; the virtual environment simulation module simulates different operating conditions and environments for the bio-perception behavior analysis module to monitor and analyze the impact of operators' behaviors and habits under these conditions on the operation of the distribution cabinet; the data generated by the bio-perception behavior analysis module is recorded and stored by the blockchain data assurance module to ensure the authenticity and non-tamperability of the data; the blockchain data assurance module provides reliable data to the neural network self-optimization module to help it adapt to complex patterns through real-time optimization of learning algorithms through deep learning; the learning algorithm optimized by the neural network self-optimization module is applied to the scenario recognition judgment module to determine the optimal operating mode of the distribution cabinet under different usage scenarios; the scenario recognition judgment module relies on the real-time scenario monitoring module to collect real-time distribution cabinet operation data through sensors and environmental monitoring systems.

6. A power monitoring Scada system according to claim 5, characterized in that: The data collected by the real-time scenario monitoring module is analyzed and classified by the scenario classification and identification module to identify the current usage scenario; the usage scenario information identified by the scenario classification and identification module is transmitted to the scenario determination module to determine the entry into the determination branch; In normal scenarios, the scenario determination module passes information to the operation strategy determination module, which determines and recommends the best operation strategy; the best operation strategy recommended by the operation strategy determination module is executed by the application optimization control module for specific optimization and control operations; the optimization strategy of the application optimization control module can be further multi-objective optimized through the biological evolution algorithm module to select the best strategy; in emergency scenarios, the scenario determination module passes information to the emergency response strategy module, which determines and initiates the emergency response strategy; the emergency response strategy module quickly initiates specific emergency measures according to the determined emergency response strategy to ensure the safe and stable operation of the system.

7. A power monitoring Scada system according to claim 2, characterized in that: The microbial sensor module monitors the microbial conditions inside the power distribution cabinet and provides environmental data to the gas analysis module to detect harmful gases; the results of the harmful gas detection by the gas analysis module are fed back to the humidity and temperature control module for adjusting the internal environment of the power distribution cabinet; the humidity and temperature control module adjusts the internal environment of the power distribution cabinet and provides data to the human perception sensor module to monitor whether someone is approaching; When the human perception sensor module detects that someone is approaching, it transmits the information to the emergency brake module to automatically enable safety protection measures; when enabling safety protection measures, the emergency brake module verifies the identity information to ensure that only authorized personnel can operate the distribution cabinet.

8. The power monitoring Scada system according to claim 2, characterized in that: The inspection machine module automatically inspects the inside of the power distribution cabinet to identify potential fault points and notifies the cleaning machine module to clean the inside of the power distribution cabinet; after the cleaning machine module completes the cleaning, it transmits the information to the maintenance machine module for performing simple replacement and maintenance work; during the maintenance process, the maintenance machine module collects usage pattern data and transmits it to the usage pattern learning module to optimize power distribution; the usage pattern learning module optimizes power distribution through deep learning and provides data to the fault pattern recognition module to identify and predict possible faults; The fault mode identification module identifies and predicts possible faults and then provides suggestions to the performance optimization suggestion module; The performance optimization suggestion module provides maintenance and optimization suggestions to the usage pattern learning module for further optimization based on identification and prediction.

9. The power monitoring Scada system according to claim 2, characterized in that: The real-time AR display module uses AR technology to display the internal circuit diagram, equipment status and operation guide of the distribution cabinet to the fault location guidance module; the fault location guidance module uses AR technology to intuitively display the fault location and maintenance steps and collaborates with the remote assistance module to realize expert remote guidance; the remote assistance module realizes expert remote guidance of maintenance personnel to perform operations and provides data to the dynamic load management module to intelligently adjust the distribution cabinet load.

10. The power monitoring Scada system according to claim 2, characterized in that: The dynamic load management module optimizes the distribution of electric energy and transmits data to the energy sharing platform module to realize the sharing and deployment of electric energy within the community or industrial park; the energy sharing platform module realizes the sharing and deployment of electric energy and supports the new energy access management module to manage the access of new energy equipment such as solar energy and electric vehicle charging piles.

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