Circuit safety management method based on Internet of Things low-voltage safety comprehensive protection module

Abstract

The invention relates to the technical field of power equipment operation and maintenance, and provides a circuit safety management method based on an internet-of-things low-voltage safety comprehensive protection module, and the method comprises the steps: obtaining multi-dimensional data of low-voltage electrical equipment, carrying out the preprocessing, building a time-phased database, and respectively constructing circuit safety prediction models suitable for different time periods. According to the method, an original prediction result and a time-phased prediction result are integrated, a data set is periodically updated, and a prediction model is trained, so that low-voltage safety comprehensive protection module circuit safety prediction which is updated in real time is realized. According to the method, the prediction accuracy is improved, the adaptability and flexibility of the system are enhanced, comprehensive circuit safety management is realized, and the development of operation and maintenance of the power system to data-driven decision is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment operation and maintenance, and in particular to a circuit safety management method based on a low-voltage safety comprehensive protection module of the Internet of Things. Background Art

[0002] The low-voltage safety comprehensive protection module based on the Internet of Things plays a vital role in circuit safety. It integrates modern information technology, sensor technology, and artificial intelligence technology, providing a strong guarantee for the safe and stable operation of the power system. The low-voltage safety comprehensive protection module based on the Internet of Things can collect multiple parameters such as voltage, current, active power, reactive power, power factor, frequency, etc. in the circuit in real time, and monitor them through high-precision sensors. When abnormal conditions such as overcurrent, overvoltage, undervoltage, short circuit, overload, etc. are detected in the circuit, the low-voltage safety comprehensive protection module of the Internet of Things can respond quickly, and effectively prevent the expansion of faults by cutting off power or adjusting circuit parameters, and protect the safety of circuits and equipment. The low-voltage safety comprehensive protection module based on the Internet of Things can also perform intelligent control according to the preset protection strategy to improve the automation level and operation efficiency of the power system.

[0003] In the process of circuit safety management of low-voltage safety comprehensive protection modules in the Internet of Things, there are the following technical pain points: since a fixed circuit safety risk model is used to predict the risk of the low-voltage safety comprehensive protection module, but the devices in the Internet of Things are dynamically increased or decreased, and the data processed by the low-voltage safety comprehensive protection module is gradually increasing, in this case, the original circuit safety risk model cannot be applied to the dynamically changing low-voltage safety comprehensive protection module in the Internet of Things, resulting in a decrease in the accuracy of the circuit safety risk model's prediction of the low-voltage safety comprehensive protection module. In order to improve the accuracy of circuit safety prediction of the low-voltage safety comprehensive protection module of the Internet of Things, the present invention provides a circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a circuit safety management method based on a low-voltage safety comprehensive protection module of the Internet of Things, which solves the problem that the original circuit safety risk model cannot be applied to the dynamically changing low-voltage safety comprehensive protection module in the Internet of Things, resulting in a decrease in the prediction accuracy of the circuit safety risk model for the low-voltage safety comprehensive protection module.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] The present invention provides a circuit safety management method based on a low-voltage safety comprehensive protection module of the Internet of Things, comprising:

[0007] Step S101, obtaining the corresponding equipment data of the low-voltage safety comprehensive protection module, the corresponding equipment data of the low-voltage safety comprehensive protection module includes low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment failure, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data, pre-processing the corresponding equipment data of the low-voltage safety comprehensive protection module and storing it in the real-time data repository;

[0008] Step S102, establishing a time period database, which includes a first time period database, a second time period database, and a third time period database, performing time configuration on the first time period database, the second time period database, and the third time period database, and transmitting data in the real-time data storage library to the first time period database, the second time period database, and the third time period database of the corresponding time period for storage according to the preset time;

[0009] Step S103, constructing a knowledge base of a circuit safety prediction model of a low-voltage safety comprehensive protection module, wherein the knowledge base of the circuit safety prediction model of the low-voltage safety comprehensive protection module includes a circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to establish a first period circuit safety prediction model, a second period circuit safety prediction model, and a third period circuit safety prediction model;

[0010] Step S104, using the circuit safety prediction model of the original low-voltage safety comprehensive protection module to predict the device data corresponding to the low-voltage safety comprehensive protection module, to obtain the original circuit safety prediction result, according to the first time period database, the second time period database and the third time period database, the time configuration is performed to analyze the device data corresponding to the low-voltage safety comprehensive protection module in turn, to obtain the circuit safety prediction result, the circuit safety prediction result includes the first time period circuit safety prediction result, the second time period circuit safety prediction result and the third time period circuit safety prediction result;

[0011] Step S105, integrate the original circuit safety prediction results and the circuit safety prediction results of the three time periods to obtain a circuit safety management data set, update the circuit safety management data set according to a preset time period, and re-preprocess and feature extract the circuit safety management data set, and then train the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain a real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module, and use the real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module to predict the circuit safety of the low-voltage safety comprehensive protection module of the Internet of Things.

[0012] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S101 includes:

[0013] Obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment fault, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data of the corresponding equipment, and obtain the corresponding equipment data of the low-voltage safety comprehensive protection module;

[0014] Preprocess the equipment data corresponding to the low-voltage safety comprehensive protection module to obtain the preprocessed equipment data corresponding to the low-voltage safety comprehensive protection module.

[0015] The pre-processed equipment data corresponding to the low-voltage safety comprehensive protection module is stored in the real-time data repository.

[0016] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S102 includes:

[0017] Creating a time-division database structure, the time-division database structure includes a first time-division database, a second time-division database, and a third time-division database;

[0018] Set the fields and data types for each period database in the period database structure to match the data transmitted from the real-time data repository;

[0019] The time configuration of the first period database is to store data stored in the real-time data repository for more than 24 hours, the second period database is to store data stored in the real-time data repository for more than 24 to 48 hours, and the third period database is to store data stored in the real-time data repository for more than 48 to 72 hours.

[0020] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S102 includes:

[0021] Extract data from the real-time data repository at a preset time and transfer the data to the database of the corresponding time period according to the current time;

[0022] Check the data storage integrity of each period database according to the preset time period and remove the erroneous data in each period database;

[0023] Records operation information during data transmission and storage to monitor faults during data transmission and storage.

[0024] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S103 includes:

[0025] Create the structure of the circuit safety prediction model knowledge base for the low-voltage safety comprehensive protection module, and determine the model type, format, and metadata required to be stored in the knowledge base;

[0026] Obtaining a circuit safety prediction model of an original low-voltage safety comprehensive protection module, wherein the circuit safety prediction model of the original low-voltage safety comprehensive protection module is used to predict the safety state of the circuit;

[0027] In the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to generate three new copies of the circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the first period, the circuit safety prediction model of the second period and the circuit safety prediction model of the third period are obtained.

[0028] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S103 includes:

[0029] Each copied model will retain the structure and algorithm of the original model, but will be used for data prediction in different time periods;

[0030] The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure and algorithm as the circuit safety prediction model of the original low-voltage comprehensive safety protection module;

[0031] The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure as the circuit safety prediction model of the original low-voltage safety comprehensive protection module and stored in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module. The circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module is tested and verified to obtain the model verification result in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module.

[0032] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S104 includes:

[0033] When the data in the real-time data repository reaches the preset time, the data in the real-time data repository is transferred to the first period database, and the first period database and the corresponding equipment data of the low-voltage safety comprehensive protection module obtained in real time are analyzed using the first period database corresponding to the first period circuit safety prediction model to obtain the first period circuit safety prediction result.

[0034] When the data in the first period database reaches the preset time, the data of the first period database is transferred to the second period database, and the second period database, the circuit safety prediction result of the first period, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time are analyzed using the second period circuit safety prediction model corresponding to the second period database to obtain the circuit safety prediction result of the second period.

[0035] The data of the second time period database is transferred to the third time period database, and the third time period circuit safety prediction model corresponding to the third time period database is used to analyze the second time period database, the second time period circuit safety prediction results, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time to obtain the third time period circuit safety prediction results.

[0036] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S104 includes:

[0037] The circuit safety prediction results of the third period are subjected to data preprocessing and data feature extraction to obtain real-time updated low-voltage electrical equipment operation characteristics, low-voltage electrical equipment load characteristics, low-voltage electrical equipment energy consumption characteristics, low-voltage electrical equipment fault characteristics, low-voltage electrical equipment external environment characteristics and low-voltage electrical equipment user behavior operation characteristics, and the feature data are substituted into the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain the circuit safety prediction results of historical data within 72 hours;

[0038] Filter out the prediction result data of the circuit safety prediction results of historical data within 72 hours and the prediction results of the circuit safety prediction model of the original low-voltage safety comprehensive protection module on the real-time data repository in the same period, and the prediction result data of the same period includes the first prediction result of the period and the second prediction result of the period;

[0039] The first prediction result of the time period is part of the circuit safety prediction result of the historical data within 72 hours, and the second prediction result of the time period is part of the prediction result of the circuit safety prediction model of the original low-voltage safety comprehensive protection module for the real-time data repository;

[0040] The first prediction result of the time period is compared with the second prediction result of the time period. If the comparison results are inconsistent, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is optimized.

[0041] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S105 includes:

[0042] The corresponding circuit safety prediction results and equipment data of the low-voltage safety comprehensive protection module are extracted from the real-time data repository, the first period database, the second period database and the third period database, the extracted data are merged according to the time series, a circuit safety management data set is constructed, and the data in the circuit safety management data set is stored in the data set management database.

[0043] Furthermore, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S105 includes:

[0044] When the timer reaches the update time point, the circuit safety management data set data update process is triggered. According to the time period, new data is extracted from the database of each time period and the real-time data storage library for integration, and the data in the circuit safety management data set is updated;

[0045] Preprocess the updated circuit safety management data set, and extract low-voltage electrical equipment operation characteristics, load characteristics, energy consumption characteristics, fault characteristics, external environment characteristics, and user behavior operation characteristics from the preprocessed data;

[0046] The extracted feature data is used to train the circuit safety prediction model of the original low-voltage comprehensive safety protection module to obtain a real-time updated circuit safety prediction model of the low-voltage comprehensive safety protection module.

[0047] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0048] Improve prediction accuracy: By establishing a time period database and a corresponding multi-period prediction model, the present invention can accurately analyze the data characteristics of different time periods, thereby improving the accuracy of circuit safety predictions, fully considering the timeliness and dynamics of the data, and making the prediction results closer to the actual situation.

[0049] Enhance system adaptability and flexibility:

[0050] In response to the continuous increase and dynamic changes in the low-voltage safety comprehensive protection module data under the Internet of Things environment, the method of the present invention can timely adjust the prediction model to adapt to the new data environment. By periodically updating the data set and training model, the prediction model is always in the optimal state.

[0051] Achieve comprehensive safety management: The present invention comprehensively considers multiple data dimensions of low-voltage electrical equipment, including operating data, load, energy consumption, faults, external environment and user behavior operation data, providing comprehensive data support for circuit safety management. Comprehensive data analysis helps to identify potential safety hazards and improve the overall safety management level.

[0052] Improve operation and maintenance efficiency and decision-making level: Through real-time updated prediction models, the present invention can timely discover circuit safety problems and give early warnings, helping operation and maintenance personnel to respond and handle them quickly. At the same time, rich data sets provide strong support for decision-making, improving the scientificity and accuracy of decision-making.

[0053] Promote data-driven decision-making: The present invention emphasizes the core role of data in circuit safety management. By continuously accumulating and analyzing data, it promotes the development of power system operation and maintenance towards data-driven decision-making. The present invention not only improves operation and maintenance efficiency, but also lays the foundation for the intelligent upgrade of the power system.

[0054] In summary, the beneficial effects of the present invention are to improve prediction accuracy, enhance system adaptability and flexibility, achieve comprehensive safety management, improve operation and maintenance efficiency and decision-making level, and promote the development of power system operation and maintenance towards data-driven decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0056] Figure 1 A schematic diagram of a circuit safety management method based on a low-voltage safety comprehensive protection module of the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. 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 are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0058] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0059] The present invention provides a circuit safety management method based on a low-voltage safety comprehensive protection module of the Internet of Things, comprising:

[0060] Step S101, obtaining the corresponding equipment data of the low-voltage safety comprehensive protection module, the corresponding equipment data of the low-voltage safety comprehensive protection module includes low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment failure, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data, pre-processing the corresponding equipment data of the low-voltage safety comprehensive protection module and storing it in the real-time data repository;

[0061] In step S101, the core of the present invention is to comprehensively and precisely obtain various data of the equipment corresponding to the low-voltage safety comprehensive protection module, and store the data after preprocessing for subsequent analysis and prediction.

[0062] Obtain the corresponding equipment data from the low-voltage safety comprehensive protection module. Obtaining the corresponding equipment data from the low-voltage safety comprehensive protection module covers multiple key aspects, including:

[0063] Low-voltage electrical equipment operating data: such as voltage, current, active power, reactive power, power factor, frequency, etc. These data reflect the real-time operating status of the electrical equipment.

[0064] Low-voltage electrical equipment load: indicates the load currently borne by the equipment, which is of great significance for understanding the working status of the equipment and whether the equipment capacity matches.

[0065] Energy consumption data of low-voltage electrical equipment: reflects the energy consumption of the equipment and helps to evaluate the energy efficiency and energy-saving potential of the equipment.

[0066] Low-voltage electrical equipment failure: Recording equipment failure information and failure types is essential for failure prediction and preventive maintenance.

[0067] External environmental data of low-voltage electrical equipment: such as temperature, humidity, vibration, etc. These data may affect the operating stability and life of the equipment.

[0068] Low-voltage electrical equipment user behavior operation data: Recording user operation behaviors, such as power on / off operations, parameter adjustments, etc., is very helpful for analyzing the impact of user behavior on equipment status.

[0069] The obtained raw data has problems such as noise, missing values ​​or outliers, so preprocessing is required, including:

[0070] Data cleaning: remove or correct erroneous, duplicate or invalid data, and handle missing values.

[0071] Data conversion: Standardize and normalize the data as needed for subsequent analysis.

[0072] Data integration: Integrate data from different sources to ensure data consistency and integrity.

[0073] The preprocessed data is stored in a real-time data repository. The role of the repository is to ensure the freshness and availability of the data and provide timely and accurate data support for subsequent data analysis and prediction.

[0074] Through step S101, the present invention not only obtains comprehensive data of the equipment corresponding to the low-voltage safety comprehensive protection module, but also improves the data quality through preprocessing, laying a solid foundation for the subsequent multi-period prediction model construction and prediction analysis.

[0075] Step S102, establishing a time period database, which includes a first time period database, a second time period database, and a third time period database, performing time configuration on the first time period database, the second time period database, and the third time period database, and transmitting data in the real-time data storage library to the first time period database, the second time period database, and the third time period database of the corresponding time period for storage according to the preset time;

[0076] In step S102, the present invention effectively manages the low-voltage safety comprehensive protection module equipment data in different time periods by establishing a time-divided database. The time-divided storage strategy helps to analyze and predict the circuit safety status more carefully. The specific implementation method is as follows:

[0077] Time-divided database structure creation: Create three independent time-division databases, the first time-division database, the second time-division database, and the third time-division database. Each database is used to store data within a specific time period.

[0078] Configure the time for each time period database to clarify their respective data storage time range:

[0079] First period database: stores data in the real-time data repository that has a storage period of more than 24 hours.

[0080] Second period database: stores data in the real-time data repository that is stored for more than 24 to 48 hours.

[0081] The third period database: stores data in the real-time data repository that is stored for more than 48 to 72 hours.

[0082] At preset time intervals (such as every hour, every day, etc.), data is extracted from the real-time data repository and transferred to the database of the corresponding period according to the storage time of the data. For example, if a piece of data is stored in the real-time data repository for 30 hours, it will be transferred to the second period database.

[0083] Regularly check the data storage integrity of the database for each period to identify and remove erroneous or inconsistent data. This helps ensure the accuracy of subsequent analysis and forecasting.

[0084] Record operational information during data transmission and storage, including transmission time, data volume, any potential data errors or processing delays, etc. This information is very useful for monitoring failures during data transmission and storage, and also helps with subsequent audits and troubleshooting.

[0085] Through the implementation of step S102, the present invention can achieve refined management of low-voltage safety comprehensive protection module equipment data. Data of different time periods are stored in corresponding databases, which helps to analyze the change trend and periodic characteristics of the data and provide richer and more accurate data support for circuit safety prediction. At the same time, regular inspection of data integrity and recording of operation information also help to improve the reliability and traceability of data management.

[0086] Step S103, constructing a knowledge base of a circuit safety prediction model of a low-voltage safety comprehensive protection module, wherein the knowledge base of the circuit safety prediction model of the low-voltage safety comprehensive protection module includes a circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to establish a first period circuit safety prediction model, a second period circuit safety prediction model, and a third period circuit safety prediction model;

[0087] In step S103, the present invention realizes accurate prediction of the circuit safety status by constructing a knowledge base of the circuit safety prediction model of the low-voltage safety comprehensive protection module. The following is a detailed analysis of this step:

[0088] Create a special low-voltage safety comprehensive protection module circuit safety prediction model knowledge base to store and manage models and data related to circuit safety prediction. The knowledge base mainly contains the circuit safety prediction model of the original low-voltage safety comprehensive protection module, which is the basis for predictive analysis.

[0089] The circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to generate three independent copies, corresponding to the circuit safety prediction needs of the first period, the second period, and the third period. Each copied model retains the structure and algorithm of the original model, but is configured specifically according to the corresponding time period to ensure that they can accurately reflect the changing rules of the data in that period.

[0090] The first period circuit safety prediction model: analyzes and predicts data within the last 24 hours, and is suitable for quickly responding to changes in circuit safety status in the short term.

[0091] The second period circuit safety prediction model: analyzes and predicts data within 24 to 48 hours, and can capture circuit safety trends on a longer time scale.

[0092] The third period circuit safety prediction model: analyzes and predicts data within 48 to 72 hours, further extending the prediction time range and helping to discover potential long-term safety hazards.

[0093] The knowledge base management configures the circuit safety prediction models of the first period, the second period and the third period with the same structure as the original model, and stores them in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module.

[0094] The models in the knowledge base are regularly tested and verified to ensure their accuracy and effectiveness. The verification results will be used to guide further optimization and adjustment of the models.

[0095] Through the implementation of step S103, the present invention establishes a comprehensive circuit safety prediction model system. The prediction models of different time periods can respectively make accurate predictions for short-term, medium-term and long-term data changes, thereby improving the accuracy and reliability of the prediction. At the same time, the management and verification mechanism of the knowledge base also ensures the continuous optimization and updating of the prediction model to adapt to the ever-changing circuit safety requirements.

[0096] Step S104, using the circuit safety prediction model of the original low-voltage safety comprehensive protection module to predict the device data corresponding to the low-voltage safety comprehensive protection module, to obtain the original circuit safety prediction result, according to the first time period database, the second time period database and the third time period database, the time configuration is performed to analyze the device data corresponding to the low-voltage safety comprehensive protection module in turn, to obtain the circuit safety prediction result, the circuit safety prediction result includes the first time period circuit safety prediction result, the second time period circuit safety prediction result and the third time period circuit safety prediction result;

[0097] The circuit safety prediction model of the original low-voltage safety comprehensive protection module is used to predict the corresponding equipment data of the low-voltage safety comprehensive protection module in the real-time data repository to obtain the original circuit safety prediction results. This step provides a benchmark or starting point for the entire prediction process.

[0098] Next, according to the time configuration of the first time period database, the second time period database and the third time period database, the corresponding equipment data of the low-voltage safety comprehensive protection module are analyzed in turn to obtain more refined circuit safety prediction results.

[0099] Circuit safety prediction for the first period: When the data in the real-time data repository accumulates to a certain amount (for example, exceeding a preset time threshold, such as 24 hours), these data and new data acquired in real time are transmitted to the first period database. Then, the circuit safety prediction model for the first period corresponding to the first period database is used to analyze these data in detail to obtain the circuit safety prediction results for the first period, which reflects the changing trend of the circuit safety status in the short term.

[0100] Circuit safety prediction for the second period: As data continues to accumulate, when the data in the first period database reaches the next time threshold (for example, more than 24 to 48 hours), it is transmitted to the second period database. At this time, the circuit safety prediction model for the second period corresponding to the second period database is used, combined with the data in the second period database, the circuit safety prediction results of the first period, and the new data acquired in real time, to perform data analysis on a longer time scale, and obtain the circuit safety prediction results for the second period, providing insights into changes in circuit safety status in the medium term.

[0101] Circuit safety prediction for the third period: Similarly, when the data in the second period database accumulates for more than 48 to 72 hours, it is transferred to the third period database. Then, the circuit safety prediction model for the third period corresponding to the third period database is used to combine the data in the third period database, the circuit safety prediction results of the second period, and the new data obtained in real time to perform long-term data analysis and obtain the circuit safety prediction results for the third period.

[0102] The original circuit safety prediction results and the circuit safety prediction results of the three time periods are integrated to form a comprehensive circuit safety management data set. This data set not only contains the prediction results on different time scales, but also provides rich data support for subsequent model optimization and updating.

[0103] Through the implementation of step S104, the present invention realizes the time-based refined analysis of the equipment data corresponding to the low-voltage safety comprehensive protection module. The prediction models of different time periods can accurately predict the data changes on their respective time scales, thereby improving the accuracy and reliability of the prediction. In addition, integrating the prediction results of multiple time periods also helps to more comprehensively understand the changing trends and potential risks of the circuit safety status, providing a strong guarantee for the safe and stable operation of the power system.

[0104] Step S105, integrate the original circuit safety prediction results and the circuit safety prediction results of the three time periods to obtain a circuit safety management data set, update the circuit safety management data set according to a preset time period, and re-preprocess and feature extract the circuit safety management data set, and then train the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain a real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module, and use the real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module to predict the circuit safety of the low-voltage safety comprehensive protection module of the Internet of Things.

[0105] In step S105, the present invention improves the continuous effectiveness and accuracy of circuit safety management by integrating multiple prediction results and periodically updating the prediction model.

[0106] The circuit safety prediction results of the original low-voltage safety comprehensive protection module and the circuit safety prediction results of the first, second and third periods are integrated to form a comprehensive circuit safety management data set. This set includes prediction results on multiple time scales and provides rich data support for subsequent model optimization and decision analysis.

[0107] Data storage: The integrated circuit safety management data set is stored in the data set management database for subsequent access, query and analysis.

[0108] Scheduled update: Set a timer to trigger the data update process when the preset time period (such as daily, weekly, etc.) is reached. According to the set time period, the latest data is extracted from the real-time data repository and the database of each time period, and integrated and updated to ensure the timeliness and accuracy of the circuit safety management data collection.

[0109] Preprocessing: Preprocess the updated circuit safety management data set, including data cleaning, missing value processing, outlier detection, etc., to improve the quality and reliability of the data.

[0110] Feature extraction: Extract key low-voltage electrical equipment operation characteristics, load characteristics, energy consumption characteristics, fault characteristics, external environment characteristics, and user behavior operation characteristics from the preprocessed data. These characteristics will be used for subsequent model training and optimization.

[0111] Model training: Use the extracted feature data to train the circuit safety prediction model of the original low-voltage safety comprehensive protection module. Through continuous iteration and optimization, a real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module is obtained. This model can more accurately reflect the actual status of the current power system and future change trends.

[0112] Model validation: During or after model training, the model is validated and tested to ensure the accuracy and reliability of its prediction results. If a large deviation is found between the prediction results and the actual status, the model needs to be optimized and adjusted.

[0113] Application update model: The circuit safety prediction model of the low-voltage safety comprehensive protection module that is updated in real time is applied to the circuit safety prediction of the low-voltage safety comprehensive protection module of the Internet of Things. Through the model, real-time data is analyzed and predicted, potential circuit safety hazards are discovered in time, and corresponding measures are taken to deal with and prevent them.

[0114] Through the implementation of step S105, the present invention ensures the timeliness and accuracy of the circuit safety management data set, and adapts to the dynamic changes of the power system state by periodically updating the prediction model. This dynamic adjustment and optimization mechanism helps to improve the accuracy and reliability of circuit safety prediction, and provides strong technical support for the safe and stable operation of the power system. At the same time, this step also promotes the application of data-driven decision-making in power system operation and maintenance, and improves operation and maintenance efficiency and decision-making level.

[0115] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S101 includes:

[0116] Obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment fault, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data of the corresponding equipment, and obtain the corresponding equipment data of the low-voltage safety comprehensive protection module;

[0117] Preprocess the equipment data corresponding to the low-voltage safety comprehensive protection module to obtain the preprocessed equipment data corresponding to the low-voltage safety comprehensive protection module.

[0118] The pre-processed equipment data corresponding to the low-voltage safety comprehensive protection module is stored in the real-time data repository.

[0119] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S102 includes:

[0120] Creating a time-division database structure, the time-division database structure includes a first time-division database, a second time-division database, and a third time-division database;

[0121] Set the fields and data types for each period database in the period database structure to match the data transmitted from the real-time data repository;

[0122] The time configuration of the first period database is to store data stored in the real-time data repository for more than 24 hours, the second period database is to store data stored in the real-time data repository for more than 24 to 48 hours, and the third period database is to store data stored in the real-time data repository for more than 48 to 72 hours.

[0123] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S102 includes:

[0124] Extract data from the real-time data repository at a preset time and transfer the data to the database of the corresponding time period according to the current time;

[0125] Check the data storage integrity of each period database according to the preset time period and remove the erroneous data in each period database;

[0126] Records operation information during data transmission and storage to monitor faults during data transmission and storage.

[0127] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S103 includes:

[0128] Create the structure of the circuit safety prediction model knowledge base for the low-voltage safety comprehensive protection module, and determine the model type, format, and metadata required to be stored in the knowledge base;

[0129] Obtaining a circuit safety prediction model of an original low-voltage safety comprehensive protection module, wherein the circuit safety prediction model of the original low-voltage safety comprehensive protection module is used to predict the safety state of the circuit;

[0130] In the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to generate three new copies of the circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the first period, the circuit safety prediction model of the second period and the circuit safety prediction model of the third period are obtained.

[0131] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S103 includes:

[0132] Each copied model will retain the structure and algorithm of the original model, but will be used for data prediction in different time periods;

[0133] The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure and algorithm as the circuit safety prediction model of the original low-voltage comprehensive safety protection module;

[0134] The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure as the circuit safety prediction model of the original low-voltage safety comprehensive protection module and stored in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module. The circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module is tested and verified to obtain the model verification result in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module.

[0135] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S104 includes:

[0136] When the data in the real-time data repository reaches the preset time, the data in the real-time data repository is transferred to the first period database, and the first period database and the corresponding equipment data of the low-voltage safety comprehensive protection module obtained in real time are analyzed using the first period database corresponding to the first period circuit safety prediction model to obtain the first period circuit safety prediction result.

[0137] When the data in the first period database reaches the preset time, the data of the first period database is transferred to the second period database, and the second period database, the circuit safety prediction result of the first period, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time are analyzed using the second period circuit safety prediction model corresponding to the second period database to obtain the circuit safety prediction result of the second period.

[0138] The data of the second time period database is transferred to the third time period database, and the third time period circuit safety prediction model corresponding to the third time period database is used to analyze the second time period database, the second time period circuit safety prediction results, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time to obtain the third time period circuit safety prediction results.

[0139] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, the step S104 includes:

[0140] The circuit safety prediction results of the third period are subjected to data preprocessing and data feature extraction to obtain real-time updated low-voltage electrical equipment operation characteristics, low-voltage electrical equipment load characteristics, low-voltage electrical equipment energy consumption characteristics, low-voltage electrical equipment fault characteristics, low-voltage electrical equipment external environment characteristics and low-voltage electrical equipment user behavior operation characteristics, and the feature data are substituted into the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain the circuit safety prediction results of historical data within 72 hours;

[0141] Filter out the prediction result data of the circuit safety prediction results of historical data within 72 hours and the prediction results of the circuit safety prediction model of the original low-voltage safety comprehensive protection module on the real-time data repository in the same period, and the prediction result data of the same period includes the first prediction result of the period and the second prediction result of the period;

[0142] The first prediction result of the time period is part of the circuit safety prediction result of the historical data within 72 hours, and the second prediction result of the time period is part of the prediction result of the circuit safety prediction model of the original low-voltage safety comprehensive protection module for the real-time data repository;

[0143] The first prediction result of the time period is compared with the second prediction result of the time period. If the comparison results are inconsistent, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is optimized.

[0144] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S105 includes:

[0145] The corresponding circuit safety prediction results and equipment data of the low-voltage safety comprehensive protection module are extracted from the real-time data repository, the first period database, the second period database and the third period database, the extracted data are merged according to the time series, a circuit safety management data set is constructed, and the data in the circuit safety management data set is stored in the data set management database.

[0146] Specifically, the circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things described in the present invention, step S105 includes:

[0147] When the timer reaches the update time point, the circuit safety management data set data update process is triggered. According to the time period, new data is extracted from the database of each time period and the real-time data storage library for integration, and the data in the circuit safety management data set is updated;

[0148] Preprocess the updated circuit safety management data set, and extract low-voltage electrical equipment operation characteristics, load characteristics, energy consumption characteristics, fault characteristics, external environment characteristics, and user behavior operation characteristics from the preprocessed data;

[0149] The extracted feature data is used to train the circuit safety prediction model of the original low-voltage comprehensive safety protection module to obtain a real-time updated circuit safety prediction model of the low-voltage comprehensive safety protection module.

[0150] The present invention solves the problem that the original circuit safety risk model cannot be applied to the low-voltage safety comprehensive protection module in the dynamically changing Internet of Things, resulting in a decrease in the prediction accuracy of the circuit safety risk model for the low-voltage safety comprehensive protection module through the following technical solutions:

[0151] Data collection and preprocessing: Obtain the corresponding equipment data from the low-voltage safety comprehensive protection module, including low-voltage electrical equipment operation data, load, energy consumption data, faults, external environment data, user behavior operation data, etc. Preprocess these data and then store them in the real-time data repository. This step ensures the accuracy and consistency of the data and provides a reliable basis for subsequent analysis and prediction.

[0152] Establishment of time-divided database: Create time-divided database, including the first time period database, the second time period database and the third time period database. Configure each time period database according to the data storage time length (such as 24 hours, 24-48 hours, 48-72 hours), and transfer the data in the real-time data repository to the database of the corresponding time period at the preset time. Regularly check the database integrity, remove erroneous data, and record the operation information during data transmission and storage to ensure the integrity and traceability of the data.

[0153] Construction of multi-period prediction model: Construct the knowledge base of circuit safety prediction model of low-voltage safety comprehensive protection module to store the circuit safety prediction model of the original low-voltage safety comprehensive protection module. Duplicate the original model to establish the circuit safety prediction model of the first period, the second period and the third period. Each copied model retains the structure and algorithm of the original model, but is used for data prediction in different periods.

[0154] Multi-period prediction and result integration: Use the original prediction model and the prediction model for each period to predict the corresponding equipment data of the low-voltage safety comprehensive protection module, and obtain the original circuit safety prediction results and the circuit safety prediction results for each period respectively. Perform feature extraction on the prediction results of the third period and compare them with the prediction results of the original prediction model for real-time data. If there is any inconsistency, optimize the original prediction model.

[0155] Data integration and model update: Integrate the original prediction results and the prediction results of each period to build a circuit safety management data set, and update the data periodically. Preprocess and extract features from the updated data set, use the extracted feature data to train the original prediction model, and obtain a circuit safety prediction model for the low-voltage safety comprehensive protection module that is updated in real time.

[0156] Through the above technical solution, the present invention effectively solves the problem of reduced prediction accuracy of the original fixed circuit safety risk model in a dynamic Internet of Things environment. Through the construction of a multi-period database and the application of a multi-period prediction model, the present invention can more accurately capture the dynamic changes of data and improve the accuracy of prediction. At the same time, through regular data updates and model training, the timeliness and accuracy of the prediction model are guaranteed.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things, characterized in that: include: Step S101, obtaining the corresponding equipment data of the low-voltage safety comprehensive protection module, the corresponding equipment data of the low-voltage safety comprehensive protection module includes low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment failure, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data, pre-processing the corresponding equipment data of the low-voltage safety comprehensive protection module and storing it in the real-time data repository; Step S102, establishing a time period database, which includes a first time period database, a second time period database, and a third time period database, performing time configuration on the first time period database, the second time period database, and the third time period database, and transmitting data in the real-time data storage library to the first time period database, the second time period database, and the third time period database of the corresponding time period for storage according to the preset time; Step S103, constructing a knowledge base of a circuit safety prediction model of a low-voltage safety comprehensive protection module, wherein the knowledge base of the circuit safety prediction model of the low-voltage safety comprehensive protection module includes a circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to establish a first period circuit safety prediction model, a second period circuit safety prediction model, and a third period circuit safety prediction model; Step S104, using the circuit safety prediction model of the original low-voltage safety comprehensive protection module to predict the device data corresponding to the low-voltage safety comprehensive protection module, to obtain the original circuit safety prediction result, according to the first time period database, the second time period database and the third time period database, the time configuration is performed to analyze the device data corresponding to the low-voltage safety comprehensive protection module in turn, to obtain the circuit safety prediction result, the circuit safety prediction result includes the first time period circuit safety prediction result, the second time period circuit safety prediction result and the third time period circuit safety prediction result; Step S105, integrate the original circuit safety prediction results and the circuit safety prediction results of the three time periods to obtain a circuit safety management data set, update the circuit safety management data set according to a preset time period, and re-preprocess and feature extract the circuit safety management data set, and then train the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain a real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module, and use the real-time updated circuit safety prediction model of the low-voltage safety comprehensive protection module to predict the circuit safety of the low-voltage safety comprehensive protection module of the Internet of Things.

2. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 1 is characterized in that: The step S101 includes: Obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the corresponding equipment information from the low-voltage safety comprehensive protection module, obtain the low-voltage electrical equipment operation data, low-voltage electrical equipment load, low-voltage electrical equipment energy consumption data, low-voltage electrical equipment fault, low-voltage electrical equipment external environment data, low-voltage electrical equipment user behavior operation data of the corresponding equipment, and obtain the corresponding equipment data of the low-voltage safety comprehensive protection module; Preprocess the equipment data corresponding to the low-voltage safety comprehensive protection module to obtain the preprocessed equipment data corresponding to the low-voltage safety comprehensive protection module. The pre-processed equipment data corresponding to the low-voltage safety comprehensive protection module is stored in the real-time data repository.

3. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 1 is characterized in that: The step S102 includes: Creating a time-division database structure, the time-division database structure includes a first time-division database, a second time-division database, and a third time-division database; Set the fields and data types for each period database in the period database structure to match the data transmitted from the real-time data repository; The time configuration of the first period database is to store data stored in the real-time data repository for more than 24 hours, the second period database is to store data stored in the real-time data repository for more than 24 to 48 hours, and the third period database is to store data stored in the real-time data repository for more than 48 to 72 hours.

4. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 3 is characterized in that: The step S102 includes: Extract data from the real-time data repository at a preset time and transfer the data to the database of the corresponding time period according to the current time; Check the data storage integrity of each period database according to the preset time period and remove the erroneous data in each period database; Records operation information during data transmission and storage to monitor faults during data transmission and storage.

5. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 1 is characterized in that: The step S103 includes: Create the structure of the circuit safety prediction model knowledge base for the low-voltage safety comprehensive protection module, and determine the model type, format, and metadata required to be stored in the knowledge base; Obtaining a circuit safety prediction model of an original low-voltage safety comprehensive protection module, wherein the circuit safety prediction model of the original low-voltage safety comprehensive protection module is used to predict the safety state of the circuit; In the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is copied to generate three new copies of the circuit safety prediction model of the original low-voltage safety comprehensive protection module, and the circuit safety prediction model of the first period, the circuit safety prediction model of the second period and the circuit safety prediction model of the third period are obtained.

6. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 5 is characterized in that: The step S103 includes: Each copied model will retain the structure and algorithm of the original model, but will be used for data prediction in different time periods; The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure and algorithm as the circuit safety prediction model of the original low-voltage comprehensive safety protection module; The circuit safety prediction model for the first period, the circuit safety prediction model for the second period, and the circuit safety prediction model for the third period are configured with the same structure as the circuit safety prediction model of the original low-voltage safety comprehensive protection module and stored in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module. The circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module is tested and verified to obtain the model verification result in the circuit safety prediction model knowledge base of the low-voltage safety comprehensive protection module.

7. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 1 is characterized in that: The step S104 includes: When the data in the real-time data repository reaches the preset time, the data in the real-time data repository is transferred to the first period database, and the first period database and the corresponding equipment data of the low-voltage safety comprehensive protection module obtained in real time are analyzed using the first period database corresponding to the first period circuit safety prediction model to obtain the first period circuit safety prediction result. When the data in the first period database reaches the preset time, the data of the first period database is transferred to the second period database, and the second period database, the circuit safety prediction result of the first period, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time are analyzed using the second period circuit safety prediction model corresponding to the second period database to obtain the circuit safety prediction result of the second period. The data of the second time period database is transferred to the third time period database, and the third time period circuit safety prediction model corresponding to the third time period database is used to analyze the second time period database, the second time period circuit safety prediction results, and the corresponding equipment data of the low-voltage safety comprehensive protection module acquired in real time to obtain the third time period circuit safety prediction results.

8. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 7 is characterized in that: The step S104 includes: The circuit safety prediction results of the third period are subjected to data preprocessing and data feature extraction to obtain real-time updated low-voltage electrical equipment operation characteristics, low-voltage electrical equipment load characteristics, low-voltage electrical equipment energy consumption characteristics, low-voltage electrical equipment fault characteristics, low-voltage electrical equipment external environment characteristics and low-voltage electrical equipment user behavior operation characteristics, and the feature data are substituted into the circuit safety prediction model of the original low-voltage safety comprehensive protection module to obtain the circuit safety prediction results of historical data within 72 hours; Filter out the prediction result data of the circuit safety prediction results of historical data within 72 hours and the prediction results of the circuit safety prediction model of the original low-voltage safety comprehensive protection module on the real-time data repository in the same period, and the prediction result data of the same period includes the first prediction result of the period and the second prediction result of the period; The first prediction result of the time period is part of the circuit safety prediction result of the historical data within 72 hours, and the second prediction result of the time period is part of the prediction result of the circuit safety prediction model of the original low-voltage safety comprehensive protection module for the real-time data repository; The first prediction result of the time period is compared with the second prediction result of the time period. If the comparison results are inconsistent, the circuit safety prediction model of the original low-voltage safety comprehensive protection module is optimized.

9. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 1 is characterized in that: The step S105 includes: The corresponding circuit safety prediction results and equipment data of the low-voltage safety comprehensive protection module are extracted from the real-time data repository, the first period database, the second period database and the third period database, the extracted data are merged according to the time series, a circuit safety management data set is constructed, and the data in the circuit safety management data set is stored in the data set management database.

10. The circuit safety management method based on the low-voltage safety comprehensive protection module of the Internet of Things as claimed in claim 9 is characterized in that: The step S105 includes: When the timer reaches the update time point, the circuit safety management data set data update process is triggered. According to the time period, new data is extracted from the database of each time period and the real-time data storage library for integration, and the data in the circuit safety management data set is updated; Preprocess the updated circuit safety management data set, and extract low-voltage electrical equipment operation characteristics, load characteristics, energy consumption characteristics, fault characteristics, external environment characteristics, and user behavior operation characteristics from the preprocessed data; The extracted feature data is used to train the circuit safety prediction model of the original low-voltage comprehensive safety protection module to obtain a real-time updated circuit safety prediction model of the low-voltage comprehensive safety protection module.

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