CN117523934A - Distribution network operation and maintenance simulation training system based on big data - Google Patents
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Abstract
The invention discloses a distribution network operation and maintenance simulation training system based on big data, which comprises a data acquisition module, a data processing module, a simulation engine, a user interface module and a big data analysis module, wherein the simulation engine comprises a simulation unit and a simulation unit, a simulation scene generated by the data processing module is used for simulating the operation of various power equipment, the user interface module comprises a presentation unit, a receiving unit and a display unit, and the big data analysis module comprises an analysis unit and a feedback unit. The invention utilizes the data acquisition module to be responsible for acquiring data, the data processing module is used for preparing a simulation scene, the simulation engine simulates operation and maintenance operation, the user interface module enables a user to interact with the simulation, the big data analysis module is used for extracting valuable information from the simulation data to support operation and maintenance decision-making and training, and the modules cooperate to form a comprehensive training and analysis system to help power distribution network operation and maintenance personnel to improve the skills and decision-making capability of the power distribution network operation and maintenance personnel.
Description
Technical Field
The invention relates to the technical field of distribution network operation and maintenance, in particular to a distribution network operation and maintenance simulation training system based on big data.
Background
The operation and maintenance of the distribution network is performed in the operation and maintenance management activities of the power distribution network, the operation and maintenance work of the distribution network mainly comprises equipment maintenance, fault removal, equipment upgrading and replacement, distribution network planning, safety management, data monitoring and analysis and the like, but operation and maintenance staff can encounter different problems in the actual operation and maintenance work of the distribution network, and part of the problems can be the first encountered problems of the operation and maintenance staff, so that the operation and maintenance staff can solve the problems very troublesome, and therefore, the large-data-based operation and maintenance simulation training system for the distribution network is provided.
Disclosure of Invention
The invention aims to provide a distribution network operation and maintenance simulation training system based on big data, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a distribution network operation and maintenance simulation training system based on big data comprises:
the data acquisition module is used for acquiring and storing related data of operation and maintenance of the power distribution network and is a plurality of sensors in a power distribution network station;
the data processing module is used for processing the acquired data and generating a simulation scene related to the operation and maintenance of the distribution network, and the data processing module receives the data from the data acquisition module;
the simulation engine comprises a simulation unit and a simulation unit, and the simulation scene simulates the operation of various power equipment by using the simulation scene generated by the data processing module;
the device comprises a user interface module, a display unit and a display unit, wherein the user interface module comprises a presentation unit, a receiving unit and a display unit;
and the big data analysis module comprises an analysis unit and a feedback unit.
Preferably, the data collected by the data collection module comprises sensor data, equipment state data and operation log data, and the data collection module transmits the collected data to a database of the system.
Preferably, the data processing module processes the data from the data acquisition module, including analyzing historical data, determining typical distribution network operation and maintenance scenarios, and generating simulation scenarios based on real data.
Preferably, the simulation unit is used for simulating operation and maintenance of the power distribution network, the simulation unit generates simulation data based on simulation scenes, the various power equipment operations simulated by the simulation engine comprise a power switch, a breaker and a transformer, and the simulation data generated by the simulation engine comprise current, voltage and equipment states.
Preferably, the presenting unit is used for presenting the simulation scene to the user, the receiving unit is used for receiving operation input of the user, the display unit is used for displaying simulation results, the user interface module can provide a way for the user to interact with the simulation scene, and the user can interact with the simulation scene through the virtual reality and enhanced display interface.
Preferably, the analysis unit is used for analyzing the simulated distribution network operation and maintenance data and identifying problems and trends, the working method of the analysis unit comprises a machine learning and data mining method, and the feedback unit provides advice and training feedback for operation and maintenance personnel.
Preferably, the user interface module may further provide a plurality of training modes to the user, the plurality of training modes including an interactive mode, a simulation mode, and an evaluation mode.
Preferably, the machine learning method includes: data collection and preparation, feature engineering, data partitioning, model selection, model training, model evaluation, model optimization, model deployment and model maintenance.
Preferably, the data mining method includes: data collection and preparation, data exploratory analysis, feature engineering, data mining algorithm selection, model training and mining, model evaluation, result interpretation and application, and continuous monitoring and updating.
Preferably, the data mining algorithm in the data mining method comprises cluster analysis, association rule mining, time sequence analysis and anomaly detection.
The invention has the technical effects and advantages that:
(1) The invention utilizes the data acquisition module to be responsible for acquiring data, the data processing module is used for preparing a simulation scene, the simulation engine simulates operation and maintenance operation, the user interface module enables a user to interact with the simulation, the big data analysis module is used for extracting valuable information from the simulation data to support operation and maintenance decision-making and training, and the modules cooperate to form a comprehensive training and analysis system to help power distribution network operation and maintenance personnel to improve the skills and decision-making capability of the power distribution network operation and maintenance personnel;
(2) The invention utilizes the setting of an interactive mode, a simulation mode and an evaluation mode, the combination of the three modes enables the system to adapt to the training requirements of different users, the interactive mode emphasizes the training of actual operation skills, the simulation mode is used for scene training and understanding, the evaluation mode is used for measuring the knowledge level and skills of the users and providing feedback, and the proper mode can be selected or switched between different modes according to the requirements and training targets of the users so as to improve the operation and maintenance capability of the users to the greatest extent.
Drawings
FIG. 1 is a schematic diagram of a training system of the present invention.
FIG. 2 is a schematic diagram of a simulation engine according to the present invention.
FIG. 3 is a schematic diagram of a user interface module according to the present invention.
FIG. 4 is a schematic diagram of a big data analysis module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a distribution network operation and maintenance simulation training system based on big data, which is shown in figures 1-4, and comprises the following components:
the data acquisition module is used for acquiring and storing related data of operation and maintenance of the power distribution network, the data acquisition module is a plurality of sensors in power distribution network stations, the data acquired by the data acquisition module comprise sensor data, equipment state data and operation log data, the data acquisition module transmits the acquired data to a database of the system, and the element tool acquisition module can be connected with the data of a plurality of power distribution network stations to realize centralized simulation training of a plurality of stations;
the data processing module is used for processing the acquired data to generate a simulation scene related to the operation and maintenance of the distribution network, receiving the data from the data acquisition module, and processing the data from the data acquisition module, wherein the data processing module comprises the steps of analyzing historical data, determining a typical operation and maintenance scene of the distribution network and generating a simulation scene based on real data;
the simulation engine comprises a simulation unit and a simulation unit, the simulation scene generated by the simulation engine through the data processing module simulates operation of various power equipment, the simulation unit is used for simulating operation and maintenance of a power distribution network, the simulation unit generates simulation data based on the simulation scene, the various power equipment operation simulated by the simulation engine comprises a power switch, a circuit breaker and a transformer, the simulation data generated by the simulation engine comprises current, voltage and equipment states, the simulation unit is used for simulating operation and maintenance of the power distribution network, the simulation unit generates simulation data based on the simulation scene, the various power equipment operation simulated by the simulation engine comprises a power switch, a circuit breaker and a transformer, and the simulation data generated by the simulation engine comprises current, voltage and equipment states;
the user interface module comprises a presentation unit, a receiving unit and a display unit, wherein the presentation unit is used for presenting a simulation scene to a user, the receiving unit is used for receiving operation input of the user, the display unit is used for displaying simulation results, the user interface module can provide a way for the user to interact with the simulation scene, the user can interact with the simulation scene through a virtual reality and enhanced display interface, the user interface module can also provide a plurality of training modes for the user, and the plurality of training modes comprise an interactive mode, a simulation mode and an evaluation mode;
the interactive mode is a training mode, allows users to interact in real time and participate in simulation scenes, and can execute various operation and maintenance operations, such as control equipment, switching circuits, coping faults and the like, the system can feed back in real time according to the operation of the users and simulate the result of the operation, and the mode can be used for training the actual operation skills of the users and helping the users to understand the operation and maintenance flow better;
the simulation mode is a training mode, allows a user to simulate different operation and maintenance scenes in a simulation environment, but does not need actual operation equipment, in the mode, the user can select a specific operation and maintenance scene or scene and observe the result of how the system simulates operation, and the mode can be used for enabling the user to be familiar with various operation and maintenance conditions, know possible challenges and solutions and does not need actual operation;
wherein the assessment mode is a training mode for assessing the user's fortune and maintenance skills and knowledge, in which the system will provide a series of fortune and maintenance tasks or scenarios and ask the user to solve a problem or perform an operation based on their knowledge and skills, the system will assess the user's performance, provide feedback and scores to assist the user in understanding the level of his fortune and maintenance capabilities, which can be used to assess the effectiveness of the training, identify the user's weaknesses and provide personalized training advice;
the combination of the three modes enables the system to adapt to training requirements of different users, the interactive mode emphasizes training of actual operation skills, the simulation mode is used for scene training and understanding, the evaluation mode is used for measuring knowledge level and skills of the users and providing feedback, and according to the requirements of the users and training targets, the system can select a proper mode or switch between different modes so as to improve the operation and maintenance capability of the users to the greatest extent;
the big data analysis module comprises an analysis unit and a feedback unit, the analysis unit is used for analyzing simulated distribution network operation and maintenance data and identifying problems and trends, the working method of the analysis unit comprises a machine learning and data mining method feedback unit for providing advice and training feedback for operation and maintenance personnel, and the big data analysis module can be integrated with other power distribution network management systems so as to realize support of real-time operation and maintenance decisions.
Further, the machine learning method includes:
data collection and preparation: firstly, data related to operation and maintenance of a power distribution network needs to be collected, wherein the data may comprise historical operation records, equipment sensor data, maintenance records and the like, and the data should be organized into a format suitable for machine learning to ensure the quality and consistency of the data;
characteristic engineering: feature engineering is the process of converting raw data into features that can be understood by a machine learning model, which may include selecting appropriate features, performing feature scaling, encoding classification variables, processing missing data, etc., with the goal of feature engineering being to provide useful information for the model;
dividing data: the data is typically divided into training and testing sets, with the training set being used to train the machine learning model and the testing set being used to evaluate model performance, sometimes also taking into account cross-validation to better evaluate the model;
model selection: selecting an appropriate machine learning algorithm or model, depending on the nature of the problem to be solved, for example, for anomaly detection problems, it may be considered to use an unsupervised learning algorithm, such as a cluster or anomaly detection algorithm, which is also a common choice for problem classification or trend prediction, such as decision trees, random forests, neural networks, etc.;
model training: training a selected machine learning model using the training set, the model learning patterns and relationships in the training data so that abnormal conditions or trends in the new data can be identified later;
model evaluation: using a test set to evaluate model performance, typically, a series of performance metrics are used to measure the performance of the model, such as accuracy, precision, recall, F1 score, etc., which can help determine the feasibility of the model and whether further optimization is required;
model optimization: if the performance of the model is not good enough, different hyper-parameters, feature selection methods, or even different algorithms can be tried to improve the model performance;
model deployment: once the model is trained and optimized, it can be deployed into a power grid operation and maintenance system to monitor real-time data and identify anomalies or trends, which may involve embedding the model into the actual system for continuous monitoring and decision support;
model maintenance: over time, data distribution and traffic demands may change, thus requiring periodic updates and maintenance of the model to ensure that its performance is still valid.
Further, the data mining method includes:
data collection and preparation: firstly, large-scale data related to operation and maintenance of a power distribution network, including historical operation records, equipment sensor data, maintenance records and the like, are required to be tidied, cleaned, denoised and preprocessed so as to ensure the quality and consistency of the data;
data exploratory analysis: visualization and statistical analysis of the data to find patterns, trends, and anomalies in the data, which help determine features in the data, such as outlier data points or outlier periods;
characteristic engineering: extracting and converting characteristics of the original data, wherein the characteristics comprise proper characteristics, characteristic scaling, coding classification variables, processing missing data and the like, and the aim of characteristic engineering is to provide useful input characteristics for a data mining algorithm;
data mining algorithm selection: the selection of an appropriate data mining algorithm, depending on the particular problem to be solved;
model training and mining: training and mining the prepared data set by using a selected data mining algorithm, wherein the algorithm can find modes, associations and abnormal conditions in the data;
model evaluation: evaluating the performance of a data mining model, typically using metrics such as accuracy, precision, recall, F1 score, etc. to measure the performance of the model, which helps determine whether the model is sufficiently trustworthy to further analysis and decision-making;
interpretation and application of results: analyzing data mining results, interpreting identified anomalies, problems, or trends, which may be used to formulate operational strategies, plan maintenance activities, predict equipment failures, or improve operational flows;
continuously monitoring and updating: data mining is not a one-time operation and as new data accumulates, the data mining model needs to be monitored and updated periodically to ensure that it is continually active.
The data mining algorithm in the data mining method comprises cluster analysis, association rule mining, time sequence analysis and anomaly detection, wherein the cluster analysis is used for grouping data points into similar clusters so as to find abnormal conditions or trends among devices or areas, the association rule mining is used for finding correlations among different events, such as the relationship between device faults and specific operations, the time sequence analysis is used for analyzing time sequence data to detect periodic trends or anomalies, and the anomaly detection is used for identifying algorithms of abnormal data points, such as Isolation Forest and One-Class SVM.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (10)
1. The utility model provides a join in marriage net fortune dimension simulation training system based on big data which characterized in that includes:
the data acquisition module is used for acquiring and storing related data of operation and maintenance of the power distribution network and is a plurality of sensors in a power distribution network station;
the data processing module is used for processing the acquired data and generating a simulation scene related to the operation and maintenance of the distribution network, and the data processing module receives the data from the data acquisition module;
the simulation engine comprises a simulation unit and a simulation unit, and the simulation scene simulates the operation of various power equipment by using the simulation scene generated by the data processing module;
the device comprises a user interface module, a display unit and a display unit, wherein the user interface module comprises a presentation unit, a receiving unit and a display unit;
and the big data analysis module comprises an analysis unit and a feedback unit.
2. The big data based distribution network operation and maintenance simulation training system of claim 1, wherein the data collected by the data collection module comprises sensor data, equipment status data and operation log data, and the data collection module transmits the collected data to a database of the system.
3. The big data based distribution network operation and maintenance simulation training system of claim 1, wherein the data processing module processes data from the data acquisition module, including analyzing historical data, determining typical distribution network operation and maintenance scenarios, and generating simulation scenarios based on real data.
4. The distribution network operation and maintenance simulation training system based on big data according to claim 1, wherein the simulation unit is used for simulating operation and maintenance of the power distribution network, the simulation unit generates simulation data based on simulation scenes, the various power equipment operations simulated by the simulation engine comprise a power switch, a circuit breaker and a transformer, and the simulation data generated by the simulation engine comprise current, voltage and equipment states.
5. The distribution network operation and maintenance simulation training system based on big data according to claim 1, wherein the presenting unit is used for presenting a simulation scene to a user, the receiving unit is used for receiving operation input of the user, the display unit is used for displaying simulation results, the user interface module can provide a way for the user to interact with the simulation scene, and the user can interact with the simulation scene through a virtual reality and enhancement display interface.
6. The distribution network operation and maintenance simulation training system based on big data according to claim 1, wherein the analysis unit is used for analyzing the simulated distribution network operation and maintenance data and identifying problems and trends, the working method of the analysis unit comprises a machine learning and data mining method, and the feedback unit provides advice and training feedback for operation and maintenance personnel.
7. The big data based distribution network operation and maintenance simulation training system of claim 1, wherein the user interface module is further configured to provide a plurality of training modes to the user, the plurality of training modes including an interactive mode, a simulation mode, and an evaluation mode.
8. The big data based distribution network operation and maintenance simulation training system of claim 6, wherein the machine learning method comprises: data collection and preparation, feature engineering, data partitioning, model selection, model training, model evaluation, model optimization, model deployment and model maintenance.
9. The big data based distribution network operation and maintenance simulation training system according to claim 6, wherein the data mining method comprises: data collection and preparation, data exploratory analysis, feature engineering, data mining algorithm selection, model training and mining, model evaluation, result interpretation and application, and continuous monitoring and updating.
10. The big data based distribution network operation and maintenance simulation training system according to claim 9, wherein the data mining algorithm in the data mining method comprises cluster analysis, association rule mining, time sequence analysis and anomaly detection.
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| CN118135878A (en) * | 2024-05-06 | 2024-06-04 | 江苏万炬科技股份有限公司 | Three-dimensional simulation training system for power grid automation equipment |
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| CN118135878A (en) * | 2024-05-06 | 2024-06-04 | 江苏万炬科技股份有限公司 | Three-dimensional simulation training system for power grid automation equipment |
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