A safety early warning method and system for power distribution cabinet

Abstract

The present invention provides a distribution cabinet safety early warning method and system, which belongs to the field of distribution cabinet safety early warning technology. The present invention realizes in-depth understanding and intelligent prediction of the operating status of the distribution cabinet by accurately dividing the monitoring area and monitoring the operating data of the core components in real time, combining the comprehensive analysis of historical data and the construction of the associated influence network diagram. It can timely discover and warn the fault area, and automatically identify the monitoring area associated with it as a safety hazard monitoring area, and trigger an early warning notification. In addition, it can also dynamically adjust the detection frequency according to the operating parameters of the safety hazard monitoring area, optimize the utilization efficiency of monitoring resources, and significantly improve the operation and maintenance efficiency and safety of the distribution cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution cabinet safety early warning, and in particular to a power distribution cabinet safety early warning method and system. Background Art

[0002] In power systems, distribution cabinets (DPCs) are key equipment for power distribution and transmission. Their safe and stable operation is directly related to the reliability and safety of the entire power system. However, in practical applications, PDCs face a variety of security threats and challenges, such as equipment aging, overload, short-circuit faults, and abnormal ambient temperature and humidity. These issues can cause PDC failures, thereby affecting the normal operation of the power system.

[0003] Traditional PDC safety early warning technology relies primarily on manual inspections and regular maintenance, but this approach has numerous shortcomings. First, manual inspections cannot monitor the operating status of the PDC in real time, making it difficult to promptly identify potential safety hazards. Second, traditional PDC safety early warning methods often lack scientific and rational zoning, requiring long-term, continuous monitoring of all PDC areas. This wastes monitoring resources and is time-consuming and labor-intensive. It also makes it difficult to accurately reflect the actual operating status of each component over the long term, resulting in low early warning efficiency.

[0004] Therefore, it is necessary to provide a power distribution cabinet safety early warning method and system to solve the above technical problems. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a distribution cabinet safety warning method and system for solving the problems of traditional distribution cabinet safety warning methods, which require long-term and continuous monitoring of all area monitoring data of the distribution cabinet, resulting in waste of monitoring resources, time-consuming and labor-intensive monitoring, and low warning efficiency.

[0006] The present invention provides a power distribution cabinet safety early warning method, which includes the following steps:

[0007] The interior of the power distribution cabinet is divided into multiple monitoring areas, and the monitoring area of ​​the core components of the power distribution cabinet is divided into the primary core monitoring area;

[0008] Collect historical data from the power distribution cabinet and use data association analysis to obtain the mutual influence relationship between each monitoring area. The historical data includes power operation data and temperature and humidity data.

[0009] Based on the mutual influence relationship between the various monitoring areas, the final core monitoring area is further divided from the primary core monitoring area through data statistical methods;

[0010] Build a safety prediction model based on historical data of power distribution cabinets;

[0011] Monitor the operating data of each final core monitoring area in real time, input it into the safety prediction model after preprocessing, and output the estimated results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area;

[0012] The operating parameters of the safety hazard monitoring area are obtained in real time, and the operating parameters of the safety hazard monitoring area are judged according to the preset operating parameter safety standard threshold. If the judgment result exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted.

[0013] Preferably, the interior of the power distribution cabinet is divided into multiple monitoring areas, and the monitoring area to which the core components of the power distribution cabinet belong is divided into primary core monitoring areas, and the specific steps are:

[0014] According to the functional layout and component distribution of the distribution cabinet, the interior of the distribution cabinet is divided into multiple independent monitoring areas. The divided monitoring areas are specifically divided based on factors such as electrical connection, heat conduction or physical location;

[0015] After the divided monitoring areas are obtained, the monitoring areas to which the core components of the power distribution cabinet belong are determined, and the determined monitoring areas to which the core components of the power distribution cabinet belong are divided into primary core monitoring areas.

[0016] Preferably, the historical data of the power distribution cabinet is collected, and the mutual influence relationship between each monitoring area is obtained by a data association analysis method, wherein the historical data includes power operation data and temperature and humidity data, and the specific steps are:

[0017] Determine the type of historical data to be collected from the power distribution cabinet and the time span of the historical data. The types of historical data specifically include power operation data and temperature and humidity data. The time span of the historical data specifically includes the past six months or one year.

[0018] Based on determining the type of historical data to be collected from the power distribution cabinet and the time span of the historical data, the historical data of the power distribution cabinet is collected and preprocessed to obtain preprocessed historical data, wherein the preprocessing specifically includes data cleaning and data conversion;

[0019] By using the correlation coefficient method in the data association analysis method, the mutual influence relationship between each monitoring area is analyzed to obtain the influence relationship between each monitoring area;

[0020] The obtained impact relationships between the various monitoring areas are output in the form of charts or reports.

[0021] Preferably, the method of further dividing the final core monitoring area from the primary core monitoring area by a data statistical method based on the mutual influence relationship between the obtained monitoring areas comprises the following specific steps:

[0022] Utilize the correlation coefficient, covariance analysis or regression analysis in the data statistical method to quantify the mutual influence degree between each monitoring area and obtain the quantified mutual influence degree between each monitoring area;

[0023] Based on the quantified mutual influence between each monitoring area, a correlation influence network diagram between each primary core monitoring area and other monitoring areas is constructed, and each primary core monitoring area is associated with other monitoring areas respectively;

[0024] In the constructed correlation impact network diagram, the total impact of each primary core monitoring area on other monitoring areas is evaluated by calculating the sum of their impact degrees, and the total impact of each monitoring area is obtained;

[0025] An impact threshold is set, and each primary core monitoring area whose total impact exceeds the impact threshold is determined as the final core monitoring area.

[0026] Preferably, the safety prediction model is constructed based on the historical data of the power distribution cabinet, and the specific steps are:

[0027] Obtain historical data of the power distribution cabinet, including current, voltage, temperature, operating time, or maintenance records, and pre-process it;

[0028] Identify the mapping relationship between current, voltage, temperature, operating time and corresponding fault problems in maintenance records;

[0029] A neural network model is selected as the architecture, and the mapping relationship between current, voltage, temperature, operating time and the corresponding fault problems in the maintenance records is used as the training set to train the neural network model to obtain a safety prediction model.

[0030] Preferably, the real-time monitoring of the operating data of each final core monitoring area is performed, and after pre-processing, the data is input into the safety prediction model to output the estimated results of each final core monitoring area, so as to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area, and the specific steps are as follows:

[0031] Obtain the operating data within the final core monitoring area through sensors and perform preprocessing;

[0032] The pre-processed operating data within the final core monitoring area is standardized and converted into a format recognized by the safety prediction model before being input into the safety prediction model to output the prediction results of each final core monitoring area;

[0033] The final core monitoring area with a predicted fault result is identified, and the monitoring areas associated with it are marked as safety hazard monitoring areas.

[0034] Preferably, the real-time acquisition of operating parameters of the safety hazard monitoring area and the determination of the operating parameters of the safety hazard monitoring area according to a preset safety standard threshold of the operating parameters are performed. If the determination result shows that the safety standard threshold is exceeded, an early warning notification is triggered and the detection frequency is dynamically adjusted. The specific steps are:

[0035] By collecting the operating parameters of the safety hazard monitoring area in real time and comparing them with the safety standard threshold after pre-processing, it is determined whether the operating parameters of each safety hazard monitoring area exceed the safety range and obtain the judgment result;

[0036] When the result of the judgment is that the operating parameters exceed the safety standard threshold, the safety hazard monitoring area where the operating parameters exceed the safety standard threshold is marked, and an early warning notification is triggered at the same time. The early warning notification specifically includes an audible alarm, flashing light, SMS reminder or email notification;

[0037] Increase the inspection frequency of safety hazard monitoring areas where operating parameters exceed safety standard thresholds, and continuously inspect the operating parameters of safety hazard monitoring areas.

[0038] A power distribution cabinet safety warning system, the safety warning system comprising:

[0039] The area division module is used to divide the interior of the power distribution cabinet into multiple monitoring areas, and divide the monitoring area to which the core components of the power distribution cabinet belong into the primary core monitoring area;

[0040] The data analysis module is used to collect historical data of the power distribution cabinet and obtain the mutual influence relationship between each monitoring area through data association analysis method. The historical data includes power operation data and temperature and humidity data;

[0041] The region identification module is used to further divide the final core monitoring area from the primary core monitoring area through data statistical methods based on the mutual influence relationship between the various monitoring areas;

[0042] Model building module, used to build a safety prediction model based on historical data of the distribution cabinet;

[0043] The estimation processing module is used to monitor the operating data of each final core monitoring area in real time, input the data into the safety prediction model after pre-processing, and output the estimation results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area;

[0044] The early warning adjustment module is used to obtain the operating parameters of the safety hazard monitoring area in real time, and judge the operating parameters of the safety hazard monitoring area based on the preset operating parameter safety standard threshold. If the judgment result exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted.

[0045] Compared with related technologies, the power distribution cabinet safety early warning method and system provided by the present invention have the following beneficial effects:

[0046] The present invention achieves an in-depth understanding and intelligent prediction of the operating status of the distribution cabinet by accurately dividing the monitoring area and monitoring the operating data of the core components in real time, combining the comprehensive analysis of historical data with the construction of the associated impact network diagram. It can timely discover and warn of fault areas, and automatically identify the monitoring areas associated with them as safety hazard monitoring areas, and trigger early warning notifications. In addition, it can also dynamically adjust the detection frequency according to the operating parameters of the safety hazard monitoring area, optimize the utilization efficiency of monitoring resources, and significantly improve the operation and maintenance efficiency and safety of the distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a power distribution cabinet safety early warning method of the present invention.

[0048] Figure 2 This is a system block diagram of a power distribution cabinet safety early warning system of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments. Example

[0050] like Figure 1 As shown, a power distribution cabinet safety early warning method includes the following steps:.

[0051] S1. Divide the interior of the distribution cabinet into multiple monitoring areas, and divide the monitoring area to which the core components of the distribution cabinet belong into the primary core monitoring area.

[0052] In the specific implementation process, the specific steps of step S1 are:

[0053] S101. Divide the interior of the distribution cabinet into multiple independent monitoring areas according to the functional layout and component distribution of the distribution cabinet to obtain the divided monitoring areas, specifically by factors such as electrical connection, heat conduction or physical location.

[0054] Specifically, in this embodiment, components with similar electrical characteristics are divided into the same monitoring area. For example, all circuit breakers, disconnectors, etc. directly connected to the bus are divided into one monitoring area to monitor the operating status of the bus and its connected components; adjacent or similar components on the heat conduction path are divided into the same monitoring area. For example, transformers, capacitors and other components with large heat generation and the heat dissipation devices around them are divided into one monitoring area to monitor the temperature changes of these components; considering the physical position relationship of the components inside the distribution cabinet, adjacent or similar components are divided into the same monitoring area. For example, circuit breakers, mutual inductors and other components located on the upper part of the distribution cabinet are divided into one monitoring area to monitor the physical status of these components.

[0055] S102: After obtaining the divided monitoring areas, determine the monitoring areas to which the core components of the power distribution cabinet belong, and divide the determined monitoring areas to which the core components of the power distribution cabinet belong into primary core monitoring areas.

[0056] Specifically, in this embodiment, the core components of the distribution cabinet are determined to include busbars, main circuit breakers or transformers. According to the divided monitoring areas, the monitoring area to which the core components belong is found, and the monitoring area to which the core components belong is divided into primary core monitoring areas.

[0057] S2. Collect historical data of the distribution cabinet and obtain the mutual influence relationship between each monitoring area through data association analysis method. The historical data includes power operation data and temperature and humidity data.

[0058] In the specific implementation process, the specific steps of step S2 are:

[0059] S201: Determine the type of historical data to be collected from the power distribution cabinet and the time span of the historical data, wherein the type of historical data specifically includes power operation data and temperature and humidity data, and the time span of the historical data specifically is the past six months or one year;

[0060] S202: Based on the determined type of historical data to be collected from the power distribution cabinet and the time span of the historical data, collect the historical data from the power distribution cabinet and perform preprocessing to obtain preprocessed historical data, wherein the preprocessing specifically includes data cleaning and data conversion;

[0061] Specifically, after collecting the historical data of the distribution cabinet, data cleaning is mainly to remove outliers, missing values ​​and duplicate values ​​in the data. Outliers are caused by sensor failure or data transmission errors, and they need to be eliminated or corrected; missing values ​​need to be filled according to the specific situation, by using interpolation or averaging method; duplicate values ​​need to be deleted to avoid interference with the analysis results.

[0062] S203, analyzing the mutual influence relationship between each monitoring area by the correlation coefficient method in the data association analysis method, and obtaining the influence relationship between each monitoring area;

[0063] Specifically, the correlation coefficient matrix between each monitoring area is calculated, and then the correlation between each monitoring area is determined according to the values ​​in the correlation coefficient matrix.

[0064] In this embodiment, if the correlation coefficient is greater than 0.8, it means that the mutual influence between the two monitoring areas is strong; if the correlation coefficient is less than 0.3, it means that the mutual influence between the two monitoring areas is weak. In addition, the direction of the mutual influence can be judged by observing the positive and negative signs in the correlation coefficient matrix: positive correlation indicates that the change trend of one monitoring area is the same as the change trend of another monitoring area; negative correlation indicates that the change trend of one monitoring area is opposite to the change trend of another monitoring area.

[0065] S204: Output the obtained influence relationship between the monitoring areas in the form of a chart or report.

[0066] Specifically, various chart types are used to display the mutual influence relationship between the monitoring areas. In this embodiment, a heat map can be used to display the size and distribution of the values ​​in the correlation coefficient matrix; a network diagram can be used to display the connection relationship and the degree of mutual influence between the various monitoring areas; a scatter plot or a line graph can also be used to display the changing trend between different monitoring areas over time; in addition, the chart can be combined with text descriptions to form a complete report to better present the analysis results and conclusions.

[0067] S3. Based on the mutual influence relationship between the various monitoring areas, the final core monitoring area is further divided from the primary core monitoring area through data statistical methods.

[0068] In the specific implementation process, the specific steps of step S3 are:

[0069] S301. Quantify the mutual influence between the monitoring areas using a correlation coefficient, covariance analysis, or regression analysis in a data statistical method to obtain a quantified mutual influence between the monitoring areas.

[0070] In this embodiment, there are three monitoring areas A, B, and C, each of which records a certain indicator, such as temperature, humidity, or current. First, by calculating the correlation coefficients between A and B, A and C, and B and C, a series of numerical values ​​are obtained. Then, covariance analysis can be used to obtain whether the correlations between these monitoring areas are different in different time periods, such as daytime and nighttime. Finally, through regression analysis, a model is established to quantify how changes in the indicators of area A affect the indicators of areas B or C.

[0071] S302: Based on the quantified mutual influence degree between each monitoring area, construct a correlation influence network diagram between each primary core monitoring area and other monitoring areas, and associate each primary core monitoring area with other monitoring areas respectively.

[0072] Specifically, each monitoring area is treated as a node in the network diagram, and the connection relationship and influence degree between the nodes are determined based on the quantified mutual influence between the monitoring areas. An edge with a larger weight indicates that the mutual influence between the two monitoring areas is stronger. In this embodiment, the three monitoring areas A, B, and C are used as nodes, and the connection relationship and influence degree are determined based on the correlation coefficient or regression analysis results between them. Then, a network diagram is drawn using graphics software, in which the nodes represent the monitoring areas, the edges represent the connection relationship between them, and the thickness or color of the edges represent the magnitude of the influence.

[0073] S303. In the constructed correlation influence network diagram, the total influence of each primary core monitoring area on other monitoring areas is evaluated by calculating the sum of their influence degrees, and the total influence of each monitoring area is obtained.

[0074] Specifically, for each primary core monitoring area, the sum of the impact degrees between it and all other monitoring areas is calculated to obtain the total impact degree of the area. For example, in the associated impact network diagram, all edges connected to monitoring area A and the corresponding impact degrees can be found. Then, these impact degrees are added together to obtain the total impact degree of area A.

[0075] S304: Set an influence threshold, and determine each primary core monitoring area whose total influence exceeds the influence threshold as a final core monitoring area.

[0076] Specifically, in this embodiment, the influence threshold is set to 10, and then the total influence of each primary core monitoring area is compared with this threshold. If the total influence of a primary core monitoring area is greater than 10, it is determined as the final core monitoring area; otherwise, it is excluded.

[0077] S4. Build a safety prediction model based on the historical data of the distribution cabinet.

[0078] In the specific implementation process, the specific steps of step S4 are:

[0079] S401. Obtain historical data of a power distribution cabinet, specifically including current, voltage, temperature, operating time, or maintenance records, and pre-process the data.

[0080] S402: Identify the mapping relationship between current, voltage, temperature, operating time and corresponding fault problems in the maintenance record.

[0081] Specifically, association rule mining or cluster analysis methods in data mining technology are used to identify the mapping relationship between the combination of monitoring parameters among current, voltage, temperature, and operating time and the corresponding fault problems in the maintenance records.

[0082] S403 , selecting a neural network model as the architecture, and training the neural network model using the mapping relationship between current, voltage, temperature, operating time and corresponding fault problems in the maintenance records as a training set to obtain a safety prediction model.

[0083] Specifically, a multi-layer perceptron (MLP) model in the neural network model is selected for training. During the training process, the mapping relationship between the combination of monitoring parameters such as current, voltage, temperature, and operating time and the corresponding fault problems in the maintenance records is used as input features and target output. The multi-layer perceptron (MLP) model is trained using the back propagation algorithm. By continuously adjusting the parameters and structure of the model, a safe prediction model for predicting distribution cabinet failures is finally obtained.

[0084] S5. Monitor the operating data of each final core monitoring area in real time, input it into the safety prediction model after preprocessing, and output the estimated results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area.

[0085] In the specific implementation process, the specific steps of step S5 are:

[0086] S501: Acquire the operating data in the final core monitoring area through sensors and perform pre-processing.

[0087] Specifically, the operating data in the final core monitoring area is obtained through sensors and preprocessed, and the process includes data cleaning, data filtering, and data conversion steps.

[0088] S502: Standardize the pre-processed operating data within the final core monitoring area, convert it into a format recognized by the safety prediction model, and then input it into the safety prediction model to output the prediction results of each final core monitoring area.

[0089] Specifically, in this embodiment, before the pre-processed operating data within the final core monitoring area is input into the safety prediction model, the operating data within the final core monitoring area is first standardized, such as converting the temperature data and vibration data into numerical values ​​between 0 and 1. Then, the operating data is converted into a format that the model can recognize, such as a CSV file or a database table. Finally, the converted data is input into the trained safety prediction model. The safety prediction model predicts the operating status of the equipment based on the input operating data, and outputs the prediction results of each final core monitoring area.

[0090] S503: Identify the final core monitoring area with a prediction result of fault, and mark the monitoring area associated with the final core monitoring area as a potential safety hazard monitoring area.

[0091] Specifically, if the prediction result is a fault or abnormal state, the corresponding final core monitoring area needs to be identified as a safety hazard monitoring area. At the same time, its associated monitoring areas also need to be identified as safety hazard monitoring areas. The specific identification should be based on the associated impact network diagram to facilitate further monitoring and analysis.

[0092] S6. Obtain the operating parameters of the safety hazard monitoring area in real time, and judge the operating parameters of the safety hazard monitoring area based on the preset operating parameter safety standard threshold. If the judgment exceeds the safety standard threshold, trigger an early warning notification and dynamically adjust the detection frequency.

[0093] In the specific implementation process, the specific steps of step S6 are:

[0094] S601. By collecting the operating parameters of the safety hazard monitoring area in real time and comparing them with the safety standard threshold after pre-processing, it is determined whether the operating parameters of each safety hazard monitoring area exceed the safety range, and a judgment result is obtained.

[0095] Specifically, first, the operating parameters in the safety hazard monitoring area are collected in real time, including temperature, humidity, pressure, current, voltage and other types. It should be noted that the collected data should ensure accuracy and real-time performance so as to timely reflect the actual operating status of the equipment or area. After the operating parameters in the safety hazard monitoring area are collected, they need to be preprocessed. The preprocessing process includes data cleaning, data filtering, data conversion and other steps. After the preprocessing is completed, the operating parameters in the safety hazard monitoring area are compared with the safety standard thresholds respectively. According to the comparison results, it is judged whether the operating parameters of each safety hazard monitoring area exceed the safety range, and a judgment result is generated.

[0096] S602. When the judgment result is that the operating parameters exceed the safety standard threshold, the safety hazard monitoring area where the operating parameters exceed the safety standard threshold is marked, and an early warning notification is triggered at the same time, where the early warning notification specifically includes a sound alarm, flashing lights, SMS reminder or email notification.

[0097] Specifically, when an operating parameter is determined to have exceeded a safety threshold, the corresponding safety hazard monitoring area must be immediately identified. This identification can be achieved by displaying a red warning box or flashing icon on the monitoring interface, so that operators can quickly notice the problem. At the same time, an early warning notification is triggered. This can be achieved through a variety of means, including an audible alarm (such as a beep or alarm), a flashing light (such as a red warning light), a text message reminder, or an email notification. The content of the early warning notification should include the name of the safety hazard monitoring area and the type of parameter that exceeded the safety threshold.

[0098] S603: Increase the detection frequency of the potential safety hazard monitoring area whose operating parameters exceed the safety standard threshold, and continuously detect the operating parameters of the potential safety hazard monitoring area.

[0099] Specifically, when it is discovered that the operating parameters of a safety hazard monitoring area exceed the safety standard threshold, its detection frequency needs to be immediately increased. It should be noted that the increased detection frequency should be determined based on the severity and urgency of the safety hazard to ensure that the operating status changes of the area can be monitored more frequently. After increasing the detection frequency, it is necessary to continuously detect the operating parameters of the safety hazard monitoring area, specifically by increasing the frequency of data collection, expanding the monitoring range or increasing monitoring parameters. This will help to promptly discover and resolve potential safety problems and prevent the situation from further deteriorating. Example

[0100] like Figure 2 As shown, a power distribution cabinet safety early warning system applied to a power distribution cabinet safety early warning method specifically includes:

[0101] The area division module is used to divide the interior of the power distribution cabinet into multiple monitoring areas, and divide the monitoring area to which the core components of the power distribution cabinet belong into the primary core monitoring area;

[0102] The data analysis module is used to collect historical data of the power distribution cabinet and obtain the mutual influence relationship between each monitoring area through data association analysis method. The historical data includes power operation data and temperature and humidity data;

[0103] The region identification module is used to further divide the final core monitoring area from the primary core monitoring area through data statistical methods based on the mutual influence relationship between the various monitoring areas;

[0104] Model building module, used to build a safety prediction model based on historical data of the distribution cabinet;

[0105] The estimation processing module is used to monitor the operating data of each final core monitoring area in real time, input the data into the safety prediction model after pre-processing, and output the estimation results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area;

[0106] The early warning adjustment module is used to obtain the operating parameters of the safety hazard monitoring area in real time, and judge the operating parameters of the safety hazard monitoring area based on the preset operating parameter safety standard threshold. If the judgment result exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0109] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A power distribution cabinet safety early warning method, characterized in that: The safety early warning method comprises the following steps: The interior of the power distribution cabinet is divided into multiple monitoring areas, and the monitoring area of ​​the core components of the power distribution cabinet is divided into the primary core monitoring area; Collect historical data from the power distribution cabinet and use data association analysis to obtain the mutual influence relationship between each monitoring area. The historical data includes power operation data and temperature and humidity data. Based on the mutual influence relationship between the various monitoring areas, the final core monitoring area is further divided from the primary core monitoring area through data statistical methods; Build a safety prediction model based on historical data of power distribution cabinets; Monitor the operating data of each final core monitoring area in real time, input it into the safety prediction model after preprocessing, and output the estimated results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area; The operating parameters of the safety hazard monitoring area are obtained in real time, and the operating parameters of the safety hazard monitoring area are judged according to the preset operating parameter safety standard threshold. If the judgment result exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted.

2. A power distribution cabinet safety early warning method according to claim 1, characterized in that: The interior of the power distribution cabinet is divided into multiple monitoring areas, and the monitoring area to which the core components of the power distribution cabinet belong is divided into primary core monitoring areas. The specific steps are as follows: According to the functional layout and component distribution of the distribution cabinet, the interior of the distribution cabinet is divided into multiple independent monitoring areas. The divided monitoring areas are specifically divided based on factors such as electrical connection, heat conduction or physical location; After the divided monitoring areas are obtained, the monitoring areas to which the core components of the power distribution cabinet belong are determined, and the determined monitoring areas to which the core components of the power distribution cabinet belong are divided into primary core monitoring areas.

3. A power distribution cabinet safety early warning method according to claim 1, characterized in that: The method collects historical data of the power distribution cabinet and obtains the mutual influence relationship between each monitoring area through a data association analysis method, wherein the historical data includes power operation data and temperature and humidity data. The specific steps are as follows: Determine the type of historical data to be collected from the power distribution cabinet and the time span of the historical data. The types of historical data specifically include power operation data and temperature and humidity data. The time span of the historical data specifically includes the past six months or one year. Based on determining the type of historical data to be collected from the power distribution cabinet and the time span of the historical data, the historical data of the power distribution cabinet is collected and preprocessed to obtain preprocessed historical data, wherein the preprocessing specifically includes data cleaning and data conversion; By using the correlation coefficient method in the data association analysis method, the mutual influence relationship between each monitoring area is analyzed to obtain the influence relationship between each monitoring area; The obtained impact relationships between the various monitoring areas are output in the form of charts or reports.

4. A power distribution cabinet safety early warning method according to claim 1, characterized in that: Based on the mutual influence relationship between the obtained monitoring areas, the final core monitoring area is further divided from the primary core monitoring area by a data statistical method, and the specific steps are: Utilize the correlation coefficient, covariance analysis or regression analysis in the data statistical method to quantify the mutual influence degree between each monitoring area and obtain the quantified mutual influence degree between each monitoring area; Based on the quantified mutual influence between each monitoring area, a correlation influence network diagram between each primary core monitoring area and other monitoring areas is constructed, and each primary core monitoring area is associated with other monitoring areas respectively; In the constructed correlation impact network diagram, the total impact of each primary core monitoring area on other monitoring areas is evaluated by calculating the sum of their impact degrees, and the total impact of each monitoring area is obtained; An impact threshold is set, and each primary core monitoring area whose total impact exceeds the impact threshold is determined as the final core monitoring area.

5. A power distribution cabinet safety early warning method according to claim 1, characterized in that: The specific steps of building a safety prediction model based on the historical data of the power distribution cabinet are as follows: Obtain historical data of the power distribution cabinet, including current, voltage, temperature, operating time, or maintenance records, and pre-process it; Identify the mapping relationship between current, voltage, temperature, operating time and corresponding fault problems in maintenance records; A neural network model is selected as the architecture, and the mapping relationship between current, voltage, temperature, operating time and the corresponding fault problems in the maintenance records is used as the training set to train the neural network model to obtain a safety prediction model.

6. A power distribution cabinet safety early warning method according to claim 1, characterized in that: The real-time monitoring of the operating data of each final core monitoring area is input into the safety prediction model after preprocessing to output the estimated results of each final core monitoring area, so as to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area. The specific steps are as follows: Obtain the operating data within the final core monitoring area through sensors and perform preprocessing; The pre-processed operating data within the final core monitoring area is standardized and converted into a format recognized by the safety prediction model before being input into the safety prediction model to output the prediction results of each final core monitoring area; The final core monitoring area with a predicted fault result is identified, and the monitoring areas associated with it are marked as safety hazard monitoring areas.

7. A power distribution cabinet safety early warning method according to claim 1, characterized in that: The operating parameters of the safety hazard monitoring area are obtained in real time, and the operating parameters of the safety hazard monitoring area are judged according to the preset operating parameter safety standard threshold. If the result of the judgment exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted. The specific steps are as follows: By collecting the operating parameters of the safety hazard monitoring area in real time and comparing them with the safety standard threshold after pre-processing, it is determined whether the operating parameters of each safety hazard monitoring area exceed the safety range and obtain the judgment result; When the result of the judgment is that the operating parameters exceed the safety standard threshold, the safety hazard monitoring area where the operating parameters exceed the safety standard threshold is marked, and an early warning notification is triggered at the same time. The early warning notification specifically includes an audible alarm, flashing light, SMS reminder or email notification; Increase the inspection frequency of safety hazard monitoring areas where operating parameters exceed safety standard thresholds, and continuously inspect the operating parameters of safety hazard monitoring areas.

8. A power distribution cabinet safety early warning system, applied to a power distribution cabinet safety early warning method according to any one of claims 1 to 7, characterized in that: The safety early warning system includes: The area division module is used to divide the interior of the power distribution cabinet into multiple monitoring areas, and divide the monitoring area to which the core components of the power distribution cabinet belong into the primary core monitoring area; The data analysis module is used to collect historical data of the power distribution cabinet and obtain the mutual influence relationship between each monitoring area through data association analysis method. The historical data includes power operation data and temperature and humidity data; The region identification module is used to further divide the final core monitoring area from the primary core monitoring area through data statistical methods based on the mutual influence relationship between the various monitoring areas; Model building module, used to build a safety prediction model based on historical data of the distribution cabinet; The estimation processing module is used to monitor the operating data of each final core monitoring area in real time, input the data into the safety prediction model after pre-processing, and output the estimation results of each final core monitoring area to predict and identify the final core monitoring area of ​​the fault and its associated safety hazard monitoring area; The early warning adjustment module is used to obtain the operating parameters of the safety hazard monitoring area in real time, and judge the operating parameters of the safety hazard monitoring area based on the preset operating parameter safety standard threshold. If the judgment result exceeds the safety standard threshold, an early warning notification is triggered and the detection frequency is dynamically adjusted.

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