Power equipment operation monitoring method and device, equipment and storage medium

Abstract

The invention discloses a power equipment operation monitoring method and device, equipment and a storage medium, and belongs to the technical field of power equipment monitoring. The method comprises the following steps: acquiring operation data of power equipment in real time through sensing layer equipment, and performing protocol conversion, cleaning and structured processing on the operation data through an industrial internet platform to obtain processed data; analyzing and processing the processed data based on an industrial internet platform to obtain an analysis result; monitoring the power equipment according to an analysis result, and generating a real-time monitoring report; and displaying the real-time monitoring report on an industrial internet platform. The states of key equipment such as a high-voltage cabinet, a transformer and a low-voltage cabinet are monitored in real time, abnormal conditions are found in time, safety accidents caused by equipment faults are avoided, the states of the key equipment such as the high-voltage cabinet, the transformer and the low-voltage cabinet are monitored in real time, the abnormal conditions are found in time, and safety accidents caused by the equipment faults are avoided.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment monitoring, and in particular to a method, apparatus, device and storage medium for monitoring the operation of power equipment. Background Art

[0002] In today's power systems, the operating status of power equipment is directly related to the safety and stability of the entire system. However, traditional power equipment monitoring methods have many shortcomings, such as low monitoring efficiency, delayed fault detection, and inaccurate data analysis. These problems often lead to equipment failures not being addressed in a timely manner, which in turn leads to safety incidents and poses a serious threat to the operation of the power system.

[0003] Application Contents The main purpose of this application is to provide a method, device, equipment and storage medium for monitoring the operation of electric power equipment, aiming to solve the current technical problem of high cost of monitoring the operation of electric power equipment.

[0004] To achieve the above objectives, the present application provides a method for monitoring the operation of an electric power device, the method comprising the following steps: The sensing layer collects the operating data of the power equipment in real time, including environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; The operating data is subjected to protocol conversion, cleaning and structuring through the industrial Internet platform to obtain processed data; Analyze and process the processed data based on the industrial Internet platform to obtain analysis results; Monitor the power equipment according to the analysis results and generate a real-time monitoring report; The real-time monitoring report is displayed on the industrial Internet platform.

[0005] In one embodiment, the step of analyzing and processing the processed data based on a preset module in the industrial Internet platform to obtain an analysis result includes: Based on the industrial Internet platform, a time series analysis strategy is used to perform trend analysis on the operating data to predict the equipment operating status; Performing fault diagnosis on the equipment operating state by using a preset fault diagnosis model to obtain a fault diagnosis result; Analyzing the processed data using a linear regression strategy to analyze the load state of the power equipment and obtain a load state analysis result; An analysis result is obtained according to the fault diagnosis result and the load status analysis result.

[0006] In one embodiment, the step of performing trend analysis on the operating data using a time series analysis strategy based on the industrial Internet platform to predict the operating status of the equipment includes: Time-align the operation data using a time series analysis strategy on the industrial Internet platform to construct a time series data set; Performing a stationarity check on the time series data set; When the stationarity check of the time series data set fails, performing differential processing on the time series data set to obtain a processed time series data set; The processed time series data set is subjected to trend analysis through the autoregressive integrated moving average model to predict the equipment operation status.

[0007] In one embodiment, the step of analyzing the processed data using a linear regression strategy to analyze the load state of the power equipment and obtaining a load state analysis result includes: The processed data is analyzed using a linear regression strategy to obtain the current and voltage data of the power equipment; Obtain the linear relationship between load, voltage and current; Constructing a target loss function based on the linear relationship; Solving the target loss function based on the current data and the voltage data to obtain a load state of the power equipment; A load state analysis result is obtained according to the load state.

[0008] In one embodiment, the step of performing protocol conversion, cleaning, and structuring on the operating data through the industrial Internet platform to obtain processed data includes: Performing protocol conversion on the operating data through the industrial Internet platform, converting data in different formats in the operating data into the same format to obtain converted operating data; performing data cleaning on the conversion operation data, filling missing values ​​in the conversion operation data, and removing abnormal values ​​in the conversion operation data to obtain cleaned operation data; The cleaning operation data is structured to obtain processed data.

[0009] In one embodiment, the step of performing structured processing on the cleaning operation data to obtain processed data includes: Structuring the cleaning operation data to obtain time series data; Creating metadata tags for each data in the time series data; The time series data is classified according to the metadata tags to obtain processed data.

[0010] In one embodiment, the step of monitoring the power equipment according to the analysis result and generating a real-time monitoring report includes: Obtaining fault diagnosis data and load status data of the power equipment according to the analysis results; determining a target electric device by using the fault diagnosis data and the load status data; The operating data of the target power equipment is monitored and a real-time monitoring report is generated.

[0011] In one embodiment, the step of monitoring the operating data of the target power equipment and generating a real-time monitoring report includes: When any one of the operating temperature, operating voltage and load status in the operating data of the target power equipment exceeds a set threshold, an abnormal monitoring report is generated and an alarm signal is triggered.

[0012] In one embodiment, the method further comprises: Upon receiving a query instruction sent by a user, determining target viewing content according to the query instruction; Determining a display page and display content on the industrial Internet platform according to the target viewing content; The operating data of the power equipment is displayed on the industrial Internet platform according to the display page and the display content.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes an electric power equipment operation monitoring X device, the electric power equipment operation monitoring X device comprising: The acquisition module is used to collect the operating data of the power equipment in real time through the perception layer equipment. The operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; A processing module, configured to perform protocol conversion, cleaning, and structure processing on the operating data through the industrial Internet platform to obtain processed data; An analysis module is used to analyze and process the processed data based on the industrial Internet platform to obtain analysis results; A monitoring module, configured to monitor the power equipment according to the analysis results and generate a real-time monitoring report; A display module is used to display the real-time monitoring report on the industrial Internet platform.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes an electric power equipment operation monitoring device, which includes: a memory, a processor, and an electric power equipment operation monitoring program stored on the memory and executable on the processor, and the electric power equipment operation monitoring program is configured to implement the steps of the electric power equipment operation monitoring method as described above.

[0015] In addition, to achieve the above objectives, the present application also proposes a storage medium, on which a power equipment operation monitoring program is stored. When the power equipment operation monitoring program is executed by a processor, the steps of the power equipment operation monitoring method described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the power equipment operation monitoring method as described above.

[0017] One or more technical solutions proposed in this application collect the operating data of power equipment in real time through perception layer equipment, and the operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; the operating data is converted into a protocol, cleaned, and structured through an industrial Internet platform to obtain processed data; the processed data is analyzed and processed based on the industrial Internet platform to obtain analysis results; the power equipment is monitored based on the analysis results to generate a real-time monitoring report; and the real-time monitoring report is displayed on the industrial Internet platform. By real-time monitoring of the status of key equipment such as high-voltage cabinets, transformers, and low-voltage cabinets, abnormal conditions can be discovered in a timely manner to avoid safety accidents caused by equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of the first embodiment of the method for monitoring the operation of electric power equipment provided in this application; Figure 2 This is a functional architecture diagram of a power equipment operation monitoring system in an embodiment of the power equipment operation monitoring method of the present application; Figure 3 This is a schematic diagram of a monitoring report generated in an embodiment of the power equipment operation monitoring method of the present application; Figure 4 A flow chart of the second embodiment of the method for monitoring the operation of electric power equipment provided in this application; Figure 5 This is a schematic diagram of the module structure of the power equipment operation monitoring device according to an embodiment of the present application; Figure 6This is a schematic diagram of the device structure of the hardware operating environment involved in the power equipment operation monitoring method in the embodiment of the present application.

[0019] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] The main solution of the embodiment of the present application is: to collect the operating data of the power equipment in real time through the perception layer equipment, and the operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; to convert the operating data into a protocol, clean it, and structure it through the industrial Internet platform to obtain processed data; to analyze and process the processed data based on the industrial Internet platform to obtain analysis results; to monitor the power equipment according to the analysis results and generate a real-time monitoring report; and to display the real-time monitoring report on the industrial Internet platform.

[0022] Since the existing technology for the operation and maintenance of power equipment mainly relies on manual staffing and manual monitoring, and regular inspections, there are blind spots in the inspections, the inspection quality is not high, and the workload is large and the time is long during special time periods, and data is missing or difficult to collect, and the analysis report is difficult to reflect the actual operation status.

[0023] This application provides a solution that applies technologies such as artificial intelligence to the maintenance of distribution rooms. By conducting comprehensive and intelligent operation and maintenance management of various signals and operating data in the distribution room, safe and efficient intelligent electricity management is achieved, and remote duty and hosting services are provided for users' electricity systems, making the management of users' electricity systems more intelligent.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, such as a power equipment operation monitoring device. The following uses the power equipment operation monitoring device as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, the embodiment of the present application provides a method for monitoring the operation of an electric power device, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for monitoring the operation of electric power equipment of the present application.

[0026] In this embodiment, the power equipment operation monitoring method includes steps S10 to S50: Step S10: The operating data of the power equipment is collected in real time through the perception layer equipment. The operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status.

[0027] It should be noted that the operating data of power equipment is collected in real time through the perception layer equipment. The perception layer equipment includes but is not limited to various sensors and monitors, which can monitor and record the environmental parameters (such as temperature and humidity) of power equipment, the status of high-voltage cabinets, transformers, low-voltage cabinets, cable trays and cable trenches in real time. These data are the basis for subsequent analysis and monitoring. Figure 2 As shown, Figure 2 This is a functional architecture diagram of the power equipment operation monitoring system, including the presentation layer, application layer, platform layer, and perception layer. The presentation layer is used to display messages from multiple channels, including owners, command centers, emergency rescue, equipment manufacturers, government agencies, unified portals, mini-programs, monitoring screens, data interfaces, service interfaces, and unified messaging. The application layer includes power monitoring, power quality, auxiliary control, intelligent operation and maintenance, energy management, data collection, real-time monitoring, fault prediction, fault diagnosis, equipment optimization, and auxiliary control. The platform layer includes the Industrial Internet platform and customer-specific deployment, including simplified versions and data localization. The perception layer includes various power equipment and their sub-equipment, such as distribution rooms, box-type substations, cables, and distribution cabinets. Distribution room sub-equipment includes environmental monitoring equipment, video linkage equipment, high-voltage cabinets, transformers, low-voltage power supplies, and other equipment. Box-type substation sub-equipment includes high-voltage cabinets, transformers, and low-voltage cabinets. Cable sub-equipment includes cable trays, cable trenches, and other equipment. Distribution cabinet equipment includes secondary cabinets / cabinets, terminal cabinets / cabinets, and power cabinets / cabinets.

[0028] The perception layer devices may include edge gateways, video servers, temperature and humidity sensors, smoke sensors, and water immersion sensors. The power equipment may include switch cabinets, high-voltage cabinets, transformers, cables, low-voltage cabinets, and control cabinets. Data collection is performed through the perception layer devices to obtain the operating data of the power equipment.

[0029] Step S20: The operating data is subjected to protocol conversion, cleaning and structured processing through the industrial Internet platform to obtain processed data.

[0030] In practice, since perception-layer devices may use different communication protocols and data formats, protocol conversion is required to ensure uniformity and readability. Furthermore, data cleansing removes useless or erroneous data, such as missing values ​​and outliers, to improve data quality. Finally, structuring processing organizes the cleaned data into a format that is easy to analyze and process.

[0031] In a feasible implementation, step S20 may include steps A11 to A13: Step A11: performing protocol conversion on the operating data through the industrial Internet platform, converting data in different formats in the operating data into the same format to obtain converted operating data; It's important to note that devices or systems from different sources may use different data formats. For example, some devices output data in JSON format, while others may use XML or CSV. The Industrial Internet platform converts these data formats into a unified format (for example, converting all data to JSON) based on a predefined protocol. This facilitates subsequent processing and analysis. For example, a temperature sensor may output data in XML format, while a pressure sensor may output data in CSV format. After protocol conversion, both data formats are converted to JSON format.

[0032] When performing protocol conversion, you can first parse the input in each format, then extract key information (such as timestamps, values, etc.), and finally convert it into JSON format.

[0033] Step A12: performing data cleaning on the conversion operation data, filling missing values ​​in the conversion operation data, and removing abnormal values ​​in the conversion operation data to obtain cleaned operation data; It's important to note that the purpose of data cleaning is to ensure data quality and make it suitable for further analysis. First, missing values ​​are imputed. For example, if temperature data for a particular time point is missing, it might be filled with the average or median of the data from that time period. Second, outliers are removed. Outliers are values ​​that are clearly unreasonable, such as a temperature sensor reading suddenly showing -100°C or 200°C. These values ​​are clearly abnormal and need to be removed or corrected.

[0034] Suppose the transformation run data is a temperature column with missing values. The current values ​​of the temperature column are [22.5, NaN, 23.1, NaN, 24.0]. You can choose to fill the missing values ​​with the mean or median. Specifically, the mean of temperature is (22.5 + 23.1 + 24.0) / 3 = 23.2. In this case, 23.2 is used to fill the missing values, and the sorted data is [22.5, 23.1, 23.2, 24.0].

[0035] Outliers are values ​​that are far from the normal data range, usually caused by data entry errors, sensor failure, etc. Outliers interfere with data analysis and modeling and therefore need to be removed.

[0036] Outliers are calculated as follows: Outlier = {x||x−μ|>3σ} μ is the mean of the data, and σ is the standard deviation of the data. If a data point deviates from the mean by more than three standard deviations, it is generally considered an outlier. Outliers identified using the above method can be deleted directly, or filled or corrected as needed.

[0037] Step A13: Structural processing is performed on the cleaning operation data to obtain processed data.

[0038] It's important to note that structured processing refers to converting data into a format suitable for further analysis, typically in a tabular format (such as a data table or database records). For example, processed data such as temperature, pressure, and humidity can be organized into rows in chronological order, with each row representing a measurement at a specific moment. This processed data can then be easily used for modeling, analysis, visualization, and other operations.

[0039] In a feasible embodiment, step A13 may include: structuring the cleaning operation data to obtain time series data; creating metadata tags for each data in the time series data; and classifying the time series data according to the metadata tags to obtain processed data.

[0040] In practice, the raw cleaned data can be organized according to time series, ensuring that the data is arranged in chronological order and that each time point has a clear identifier (such as a timestamp). This process requires determining the time sequence of each data point and converting each data item into a structured format (such as a table or database). Time series data typically contains a timestamp column and multiple time-related numerical columns (such as temperature, pressure, humidity, etc.). Each row represents a data record at a time point.

[0041] Metadata tags are additional descriptions of data, used to indicate information such as its characteristics, source, and purpose. In time series data, metadata tags can provide more context for data points, aiding subsequent data analysis and classification.

[0042] Metadata tags can include time range, data collection device, data source, unit, data quality, etc. For time series data, metadata can help understand the context of the data, such as which sensor collected the data, whether the data is missing, or certain special data tags (such as outliers, measurement range, etc.).

[0043] Assume that metadata tags are created for "temperature" and "pressure" data in time series data. The tags include "unit", "collection device", and "data quality".

[0044] Classification is the process of dividing data into different categories based on certain criteria or labels. For time series data classification, data can be grouped and analyzed based on metadata labels, or data can be divided based on certain characteristics (such as temperature exceeding a certain threshold or pressure reaching an abnormal value). Additional labels can be added to each column of data, such as: unit (unit), sensor_id (sensor ID), location (location), data_quality (data quality), etc. Data can be divided into different categories based on metadata labels (such as sensor type, data quality, etc.). For example, data collected by different sensors can be classified separately, or data quality can be divided into high-quality, medium-quality, and low-quality data.

[0045] Data can be classified based on specific values ​​or characteristics. For example, data with a temperature value greater than a certain threshold can be classified as "high temperature" data, and data with a pressure value outside the normal range can be marked as "abnormal pressure".

[0046] For example, based on the metadata tags of temperature and pressure, the data is classified as follows: High temperature: data with temperature greater than 25°C; Low temperature: data with temperature less than or equal to 25°C; Abnormal pressure: data with pressure outside the range of 101.0 to 102.0 hPa; Each type of data can be stored in different data tables or different files to facilitate subsequent analysis.

[0047] Step S30: Analyze and process the processed data based on the industrial Internet platform to obtain analysis results.

[0048] In practice, data can be deeply mined and analyzed to identify issues such as the operating status of power equipment, potential failures, and performance bottlenecks. This analysis process may employ a variety of algorithms and models, such as time series analysis and machine learning, to provide accurate results.

[0049] Step S40: Monitor the power equipment according to the analysis results and generate a real-time monitoring report.

[0050] It should be noted that the platform can include analysis and modeling modules. The analysis module can monitor power equipment based on its analysis results. If an abnormality or potential failure is detected in the power equipment, the monitoring module will immediately initiate a monitoring program to track and record the operating data of the target power equipment in real time. Furthermore, the monitoring module will generate corresponding monitoring reports and early warning signals based on preset thresholds and rules, allowing operations and maintenance personnel to take timely action.

[0051] The monitoring report contains important information such as the real-time operating status, fault information, performance parameters, etc. of the power equipment. Operation and maintenance personnel can view and download these reports at any time through the industrial Internet platform.

[0052] In a feasible implementation, step S40 may include: obtaining fault diagnosis data and load status data of the power equipment based on the analysis results; determining the target power equipment through the fault diagnosis data and the load status data; monitoring the operating data of the target power equipment and generating a real-time monitoring report.

[0053] It should be noted that real-time monitoring reports can include information such as the current operating status of power equipment, historical data comparisons, fault warnings, and performance evaluations. These reports allow operations and maintenance personnel to quickly understand the overall status of power equipment, identify potential problems promptly, and take appropriate measures to ensure the stable operation of the power system.

[0054] First, the analysis results can be used to obtain fault diagnosis data and load status data for power equipment. Fault diagnosis data may include information such as the type, location, and severity of the equipment fault, while load status data reflects performance indicators such as the equipment's operating load and efficiency.

[0055] The target power equipment is then identified based on the fault diagnosis data and load status data. This could be equipment with a fault or potential fault, or equipment operating at excessive load or with low efficiency. These devices require specific attention and monitoring.

[0056] Continuous monitoring of the target power equipment's operating data involves real-time collection, analysis, and processing of various operating parameters to obtain the latest status information. This data is then compared with pre-set thresholds and rules to determine if any abnormalities exist.

[0057] In practice, monitoring reports should include real-time device status information, historical data comparisons, fault warnings, and performance evaluations. Reports should be intuitive and easy to understand, allowing operators to quickly understand and respond. For example, reports could use charts to display the device's operating status and load, while also providing alarms and warning signals to help operators identify and address issues promptly.

[0058] Suppose a transformer in a power system issues a fault warning. Using fault diagnosis data, maintenance personnel discovered that the transformer's oil temperature was abnormally high and the load current exceeded the rated value. Therefore, the transformer was identified as the target power equipment. The maintenance personnel then continuously monitored the transformer's operating data and found that the oil temperature continued to rise and the load current did not decrease. Based on the monitoring results, the maintenance personnel generated a real-time monitoring report that detailed the transformer's real-time status, historical data comparisons, and fault warning status. Based on the report's content, the maintenance personnel promptly implemented appropriate measures, preventing the fault from further escalating and impacting the stable operation of the power system.

[0059] like Figure 3 As shown, Figure 3 The generated monitoring report diagram is statistically analyzed by warning type. For example, there were 36 safety limit exceeding incidents this month, a decrease of 12 from the previous month. According to the circuit statistics, there were 13 incidents involving the 6# transformer, 11 incidents involving the 1st floor electric well main distribution box, 4 incidents involving the 5th floor electric well main distribution box, 4 incidents involving the 2nd floor electric well main distribution box, and 4 incidents involving the 3rd floor electric well main distribution box. According to the warning category statistics, there were 15 power outages, 13 over-temperature warnings, and 8 overloads. It is recommended to strengthen the daily management of the 6# transformer, the 1st floor electric well main distribution box, and the 5th floor electric well main distribution box to avoid downtime and accidents caused by circuit failure.

[0060] In a specific implementation, when any one of the operating temperature, operating voltage and load status in the operating data of the target power equipment exceeds a set threshold, an abnormal monitoring report is generated and an alarm signal is triggered.

[0061] Abnormal monitoring reports detail parameters exceeding set thresholds, the time of occurrence, the severity of the abnormality, and other information, allowing operations and maintenance personnel to quickly locate the problem and take emergency measures. Alarm signals can be sent to relevant personnel via SMS, email, and system notifications to ensure timely information delivery.

[0062] For example, if the operating temperature of a high-voltage cabinet exceeds a set threshold, the system immediately generates an abnormality monitoring report and triggers an alarm. The report clearly identifies the cabinet's operating temperature, the time of occurrence, and the severity of the abnormality. It also provides historical data for comparison, allowing operations personnel to determine whether the abnormality is a sudden emergency or a persistent trend. Upon receiving the alarm, operations personnel can quickly conduct on-site investigations or remotely log in to the system to ensure safe operation of the cabinet.

[0063] Furthermore, the system can perform intelligent analysis based on abnormal monitoring reports and alarm signals, providing possible fault causes and suggested solutions, further assisting operations and maintenance personnel in quickly resolving issues. Through continuous monitoring and early warning, abnormalities in power equipment operation can be promptly detected and addressed, ensuring the stable and safe operation of the power system.

[0064] Step S50: Display the real-time monitoring report on the industrial Internet platform.

[0065] In practice, the Industrial Internet platform provides a user interface for displaying real-time monitoring reports. Operations and maintenance personnel can access the platform via a browser or mobile device to view real-time status, historical data, analysis results, and other information about power equipment. The intuitive interface offers a variety of display formats, including charts and alarm prompts, enabling operators to quickly understand and respond.

[0066] In addition, the platform also supports data visualization functions, which can intuitively display the operating status and change trends of power equipment in the form of charts, curves, etc., providing strong support for operation and maintenance decisions.

[0067] In a feasible implementation, in addition to automatically monitoring the operation of motor equipment and generating reports, personalized displays can also be performed according to user needs. Therefore, the power equipment operation monitoring method also includes: when receiving a query instruction sent by a user, determining the target viewing content according to the query instruction; determining the display page and display content on the industrial Internet platform according to the target viewing content; and displaying the operation data of the power equipment on the industrial Internet platform according to the display page and the display content.

[0068] In specific implementations, users can enter query commands through the Industrial Internet platform's user interface. Query commands can include information such as the specified power equipment, time period, and data type, specifying the content the user wishes to view. Upon receiving the query command, the system parses the command and determines the target content. For example, a user may wish to view the temperature and load status of a transformer over the past week. Based on the target content, the system determines the corresponding display page and content on the Industrial Internet platform. The display page can be a preset page template, and the content is the power equipment operating data that has been filtered and processed according to the query command. Based on the determined display page and content, the system displays the power equipment operating data on the Industrial Internet platform. Display formats can include charts, graphs, and data tables, presenting the data to the user in an intuitive and easy-to-understand manner. By viewing the displayed content, users can understand the real-time status of the power equipment, historical data, and analysis results, providing strong support for operation and maintenance decision-making.

[0069] For example, a user might be interested in the historical temperature data for a specific transformer and can send a query through the platform. Upon receiving the query, the system determines that the target content is the historical temperature data for that transformer and then locates the corresponding display page and content on the platform, such as a temperature data graph or historical data table. Finally, the system displays the transformer's historical temperature data in charts and tables on the Industrial Internet platform for easy viewing and analysis. This personalized display feature allows users to quickly obtain the information they need based on their needs, improving monitoring and maintenance efficiency.

[0070] This embodiment uses perception layer devices to collect operating data of power equipment in real time. The operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status. The operating data is converted to a protocol, cleaned, and structured through an industrial Internet platform to obtain processed data. The processed data is analyzed and processed based on the industrial Internet platform to obtain analysis results. The power equipment is monitored based on the analysis results to generate a real-time monitoring report. The real-time monitoring report is displayed on the industrial Internet platform. By real-time monitoring of the status of key equipment such as high-voltage cabinets, transformers, and low-voltage cabinets, abnormal conditions are discovered in a timely manner to avoid safety accidents caused by equipment failures.

[0071] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 4 , step S30 includes steps S301 to S304: Step S301: Based on the industrial Internet platform, a time series analysis strategy is used to perform trend analysis on the operating data to predict the equipment operating status.

[0072] It should be understood that time series analysis is a commonly used data analysis method. By analyzing historical data, future trends and potential changes can be predicted. In this embodiment, the time series analysis function of the Industrial Internet platform is used to perform trend forecasts on the operating data of power equipment to assess the equipment's future operating status. Time series analysis strategies can include methods such as ARIMA models and exponential smoothing. The appropriate method should be selected based on the characteristics of the data and the analysis requirements.

[0073] In a feasible implementation, step S301 may include steps B11 to B14: Step B11: Time-aligning the operating data using a time series analysis strategy on the industrial Internet platform to construct a time series data set; It's important to note that time alignment ensures data consistency across the time dimension, facilitating subsequent analysis. For example, operating data, such as temperature, current, and voltage, can be aligned based on the timestamps of each data point to construct a time series dataset.

[0074] The timestamps are t1, t2, ..., tn, and the corresponding device operation data are X(t1), X(t2), ..., X(tn).

[0075] Step B12: performing a stationarity check on the time series data set; In practice, stationarity refers to the fact that the statistical properties of a data series (such as the mean and variance) do not change over time. If the data series is not stationary, the accuracy of time series analysis may be affected. Therefore, it is necessary to verify the data for stationarity. The ADF (Augmented Dickey-Fuller) test is a commonly used test method, but other verification methods are also possible and are not limited in this embodiment.

[0076] It should be noted that if the time series dataset passes the stationarity check, step B14 can be directly performed.

[0077] Step B13: When the stationarity check of the time series dataset fails, performing differential processing on the time series dataset to obtain a processed time series dataset; It is understandable that if the time series dataset fails the stationarity check, it means that there is a trend or seasonal change in the data. At this time, the time series dataset needs to be differentially processed to eliminate the trend or seasonal influence and obtain the processed time series dataset.

[0078] Differential processing is a commonly used data preprocessing method. By performing differential operations on the original data, stable time series data, that is, the processed time series data set, can be obtained.

[0079] Specifically, multiple differencing processes can be performed on the time series data set. First, perform a difference to obtain the data after the first difference. If the result of the first difference is stable, there is no need to perform the difference again. If the result of the first difference still has a trend, such as a linear trend, a second difference can be performed. The difference calculation formula is:

[0080] is the data after a difference. is the current value of the time series data, It is the previous value of the time series data. When the data after the first difference still has a trend (such as a linear trend), the second difference can be performed. The second difference is based on the first difference. Its calculation formula is:

[0081] Assume that the data sequence after a difference is: Y (1) =[2,2,2,2], perform quadratic difference on this sequence: Y (2) =[2−2,2−2,2−2]=[0,0,0], the data after the second difference becomes a constant 0, which means that the data has become stable and no further difference is needed.

[0082] After differencing, it is often necessary to verify the data's stationarity using methods such as the ADF test (Augmented Dickey-Fuller Test). If the test indicates that the data is stationary, further time series modeling (such as ARIMA) can be performed.

[0083] Step B14: Perform trend analysis on the processed time series data set using an autoregressive integrated moving average model to predict the equipment operating status.

[0084] In a specific implementation, the autoregressive integrated moving average (ARIMA) model is used to perform trend analysis on the processed time series dataset and predict the equipment operating status. The ARIMA model is a commonly used time series analysis method that can be used to describe and predict data series with time correlation. By analyzing historical data, the ARIMA model can capture trends and cyclical changes in the data, thereby predicting future data points. In this embodiment, the ARIMA model is used to analyze the processed time series dataset to assess the future operating status of power equipment, providing strong support for operation and maintenance decisions.

[0085] The order (p, d, q) of the ARIMA model can be selected based on the ACF (autocorrelation function) and PACF (partial autocorrelation function). p is the autoregressive order; d is the differencing order; and q is the sliding average order. The ARIMA model is fitted using historical data to determine the model parameters (autoregressive coefficients, sliding average coefficients, etc.). The fitted ARIMA model is then used to predict future data and device status at future times.

[0086] in, are the parameters of the AR and MA models, (t−j) is the residual term, and the predicted value is .

[0087] Through the above calculation process, the equipment operation status at the future moment is predicted.

[0088] Step S302: performing fault diagnosis on the operating state of the device using a preset fault diagnosis model to obtain a fault diagnosis result.

[0089] Fault diagnosis models are built based on historical fault data and expert experience to identify potential equipment failures. In this embodiment, the fault diagnosis function of the Industrial Internet platform is utilized to input the predicted equipment operating status into the pre-set fault diagnosis model to obtain fault diagnosis results. These results may include information such as fault type, location, and severity, providing a basis for subsequent equipment maintenance and repair.

[0090] Step S303: using a linear regression strategy to analyze the processed data, analyze the load state of the power equipment, and obtain a load state analysis result.

[0091] In a specific implementation, linear regression is a simple statistical analysis method used to describe the linear relationship between independent variables and dependent variables.

[0092] In this embodiment, a linear regression strategy is used to analyze the operating data of the power equipment to obtain load status analysis results. The load status analysis results may include performance indicators such as the equipment's operating load and efficiency, providing a basis for reasonable scheduling and optimization of the equipment.

[0093] In a feasible implementation, step S303 may include steps B21 to B25: Step B21: Analyze the processed data using a linear regression strategy to obtain current data and voltage data of the power equipment; In practice, linear regression, a statistical analysis method, can be used to analyze power equipment operating data that has undergone preprocessing (such as cleaning, alignment, and differencing). Linear regression reveals the linear relationship between independent variables (in this case, time and environmental parameters) and dependent variables (current and voltage). This step allows the extraction of current and voltage data for power equipment from the data, providing a foundation for subsequent analysis.

[0094] Assume that there is a set of operating data of power equipment, including information such as timestamp, ambient temperature, equipment current and equipment voltage, so as to obtain current data and voltage data of the power equipment.

[0095] Step B22: Obtaining the linear relationship between the load and the voltage and current; It should be noted that a linear regression model can be constructed using time as the independent variable and current and voltage as the dependent variables. Through model training, a linear relationship between current and voltage over time can be obtained, thereby predicting the current and voltage values ​​at any time point.

[0096] The linear relationship between load, voltage and current is as follows: L=β0+β1I+β2V In the above formula, β0 is the intercept, β1 and β2 are regression coefficients, V is voltage, I is current, and L is load.

[0097] Step B23: constructing a target loss function based on the linear relationship; In practice, the target loss function is usually used to measure the difference between the model's predicted value and the actual value. Specifically, the regression coefficients can be estimated using the least squares method (OLS) to minimize the following loss function:

[0098] In the above formula, L i is the load, I i is the current, V i is the voltage.

[0099] Step B24: solving the target loss function based on the current data and the voltage data to obtain the load state of the power equipment; In a specific implementation, the target loss function can be solved through current data and voltage data, and the target loss function can be minimized to obtain the load state prediction value.

[0100] Step B25: Obtain a load status analysis result according to the load status.

[0101] After obtaining the load status forecast, this data is further analyzed and interpreted. This step may include data visualization, trend analysis, anomaly detection, etc. A comprehensive analysis of the load status of power equipment is obtained, providing strong support for operation and maintenance decisions.

[0102] For example, we can plot load status predictions into charts or graphs to visually visualize load trends over time. We can also set thresholds to detect abnormal load conditions (such as overload or underload), triggering timely alerts or implementing appropriate measures to prevent equipment failure or damage.

[0103] Step S304: obtaining an analysis result according to the fault diagnosis result and the load status analysis result.

[0104] The final analysis results are synthesized based on the fault diagnosis and load status analysis results. These results may include information such as the equipment's operating status, potential faults, and performance bottlenecks, providing support for subsequent monitoring and maintenance decisions. Based on these analysis results, maintenance personnel can take appropriate measures to ensure stable power system operation.

[0105] This embodiment, based on the Industrial Internet platform, uses a time series analysis strategy to perform trend analysis on the operating data and predict the equipment operating status. Using a preset fault diagnosis model, the equipment operating status is diagnosed to obtain a fault diagnosis result. A linear regression strategy is used to analyze the processed data and the load status of the power equipment to obtain a load status analysis result. An analysis result is obtained based on the fault diagnosis result and the load status analysis result. Time series analysis, by performing trend analysis on equipment operating data, can help predict the future operating status of the equipment. This predictive capability can proactively identify potential operational issues or failures in the equipment, enabling proactive monitoring and early warning of equipment status and reducing the occurrence of sudden failures. Using a preset fault diagnosis model, accurate fault diagnosis can be performed on the equipment operating status. This model, trained based on historical data, can identify abnormal patterns in equipment operation, thereby proactively detecting potential failures, reducing downtime, and improving equipment reliability. Using a linear regression strategy to analyze the processed data, the load status of the power equipment can be accurately analyzed. By analyzing load trends, a better understanding of equipment workload and operating efficiency can be achieved, helping to rationally distribute and optimize load management, thereby improving equipment utilization and reducing energy waste. Fault diagnosis results, combined with load status analysis, provide operators with comprehensive reports on equipment health. These analyses not only aid in fault diagnosis and load optimization but also provide data support for decision-makers, driving intelligent and automated equipment operations and improving decision-making efficiency and accuracy.

[0106] This application also provides a power equipment operation monitoring device, please refer to Figure 5 , the power equipment operation monitoring device includes: The acquisition module 10 is used to collect the operating data of the power equipment in real time through the perception layer equipment. The operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status.

[0107] The processing module 20 is used to perform protocol conversion, cleaning and structured processing on the operating data through the industrial Internet platform to obtain processed data.

[0108] The analysis module 30 is used to analyze and process the processed data based on the industrial Internet platform to obtain analysis results.

[0109] The monitoring module 40 is used to monitor the power equipment according to the analysis results and generate a real-time monitoring report.

[0110] The display module 50 is used to display the real-time monitoring report on the industrial Internet platform.

[0111] The electric power equipment operation monitoring device provided in this application, which utilizes the electric power equipment operation monitoring method described in the above-mentioned embodiments, can address the current technical issue of high costs associated with electric power equipment operation monitoring. Compared to the prior art, the electric power equipment operation monitoring device provided in this application has the same beneficial effects as the electric power equipment operation monitoring method described in the above-mentioned embodiments. Other technical features of the electric power equipment operation monitoring device are the same as those disclosed in the above-mentioned embodiments and are not further elaborated here.

[0112] In one embodiment, the analysis module 30 is also used to perform trend analysis on the operating data based on the industrial Internet platform using a time series analysis strategy to predict the operating status of the equipment; perform fault diagnosis on the operating status of the equipment using a preset fault diagnosis model to obtain a fault diagnosis result; use a linear regression strategy to analyze the processed data, analyze the load status of the power equipment, and obtain a load status analysis result; and obtain an analysis result based on the fault diagnosis result and the load status analysis result.

[0113] In one embodiment, the analysis module 30 is further used to perform time alignment on the operating data using a time series analysis strategy on the industrial Internet platform to construct a time series data set; perform a stationarity check on the time series data set; when the stationarity check of the time series data set fails, perform differential processing on the time series data set to obtain a processed time series data set; perform trend analysis on the processed time series data set through an autoregressive integral sliding average model to predict the operating status of the equipment.

[0114] In one embodiment, the analysis module 30 is also used to analyze the processed data using a linear regression strategy to obtain current data and voltage data of the power equipment; obtain the linear relationship between the load and the voltage and current; construct a target loss function based on the linear relationship; solve the target loss function based on the current data and the voltage data to obtain the load state of the power equipment; and obtain a load state analysis result based on the load state.

[0115] In one embodiment, the processing module 20 is also used to perform protocol conversion on the operating data through the industrial Internet platform, convert data of different formats in the operating data into the same format to obtain converted operating data; perform data cleaning on the converted operating data, fill in missing values ​​in the converted operating data, and eliminate abnormal values ​​in the converted operating data to obtain cleaned operating data; and perform structured processing on the cleaned operating data to obtain processed data.

[0116] In one embodiment, the processing module 20 is further used to structure the cleaning operation data to obtain time series data; create metadata tags for each data in the time series data; and classify the time series data according to the metadata tags to obtain processed data.

[0117] In one embodiment, the monitoring module 40 is further used to obtain fault diagnosis data and load status data of the power equipment based on the analysis results; determine the target power equipment through the fault diagnosis data and the load status data; monitor the operating data of the target power equipment and generate a real-time monitoring report.

[0118] In one embodiment, the monitoring module 40 is further configured to generate an abnormal monitoring report and trigger an alarm signal when any one of the operating temperature, operating voltage, and load status in the operating data of the target power equipment exceeds a set threshold.

[0119] In one embodiment, the monitoring module 40 is also used to determine the target viewing content according to the query instruction when receiving the query instruction sent by the user; determine the display page and display content on the industrial Internet platform according to the target viewing content; and display the operating data of the power equipment on the industrial Internet platform according to the display page and the display content.

[0120] The present application provides an electric power equipment operation monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the electric power equipment operation monitoring method in the above-mentioned embodiment one.

[0121] Reference below Figure 6, which shows a schematic diagram of the structure of an electric power equipment operation monitoring device suitable for implementing an embodiment of the present application. The electric power equipment operation monitoring device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The power equipment operation monitoring device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0122] like Figure 6 As shown, the power equipment operation monitoring device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the power equipment operation monitoring device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input device 1007, such as a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008, such as an LCD (Liquid Crystal Display), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication device 1009. The communication device 1009 can allow the power equipment operation monitoring device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a power equipment operation monitoring device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or provided instead.

[0123] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0124] The electric power equipment operation monitoring device provided in this application, which utilizes the electric power equipment operation monitoring method described in the above-mentioned embodiments, can address the current technical issue of high costs associated with electric power equipment operation monitoring. Compared to the prior art, the beneficial effects of the electric power equipment operation monitoring device provided in this application are the same as those of the electric power equipment operation monitoring method described in the above-mentioned embodiments. Other technical features of the electric power equipment operation monitoring device are the same as those disclosed in the above-mentioned embodiments and are not further elaborated here.

[0125] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0126] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0127] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the power equipment operation monitoring method in the above-mentioned embodiment.

[0128] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash memory), optical fiber, CD-ROM (CD-Read Only Memory), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0129] The computer-readable storage medium may be included in the power equipment operation monitoring device; or may exist independently without being assembled into the power equipment operation monitoring device.

[0130] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the power equipment operation monitoring device, the power equipment operation monitoring device: collects the operation data of the power equipment in real time through the perception layer device, and the operation data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; converts, cleans and structures the operation data through the industrial Internet platform to obtain processed data; analyzes and processes the processed data based on the industrial Internet platform to obtain analysis results; monitors the power equipment according to the analysis results and generates a real-time monitoring report; and displays the real-time monitoring report on the industrial Internet platform.

[0131] The computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0133] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0134] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for monitoring the operation of electric power equipment. This computer-readable storage medium can address the current technical issue of high costs associated with monitoring the operation of electric power equipment. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for monitoring the operation of electric power equipment provided in the aforementioned embodiments, and are not further elaborated here.

[0135] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned method for monitoring the operation of electric power equipment when executed by a processor.

[0136] The computer program product provided in this application can solve the current technical problem of high cost of monitoring the operation of power equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the power equipment operation monitoring method provided in the above embodiment, and will not be repeated here.

[0137] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring the operation of electric power equipment, characterized in that: The power equipment operation monitoring method comprises: The sensing layer collects the operating data of the power equipment in real time, including environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; The operating data is subjected to protocol conversion, cleaning and structuring through the industrial Internet platform to obtain processed data; Analyze and process the processed data based on the industrial Internet platform to obtain analysis results; Monitor the power equipment according to the analysis results and generate a real-time monitoring report; The real-time monitoring report is displayed on the industrial Internet platform.

2. The method according to claim 1, wherein The step of analyzing and processing the processed data based on the preset module in the industrial Internet platform to obtain the analysis results includes: Based on the industrial Internet platform, a time series analysis strategy is used to perform trend analysis on the operating data to predict the equipment operating status; Performing fault diagnosis on the equipment operating state by using a preset fault diagnosis model to obtain a fault diagnosis result; Analyzing the processed data using a linear regression strategy to analyze the load state of the power equipment and obtain a load state analysis result; An analysis result is obtained according to the fault diagnosis result and the load status analysis result.

3. The method according to claim 2, wherein The step of performing trend analysis on the operating data using a time series analysis strategy based on the industrial Internet platform to predict the operating status of the equipment includes: Time-align the operation data using a time series analysis strategy on the industrial Internet platform to construct a time series data set; Performing a stationarity check on the time series data set; When the stationarity check of the time series data set fails, performing differential processing on the time series data set to obtain a processed time series data set; The processed time series data set is subjected to trend analysis through the autoregressive integrated moving average model to predict the equipment operation status.

4. The method according to claim 2, wherein The step of analyzing the processed data using a linear regression strategy to analyze the load state of the power equipment and obtain a load state analysis result includes: The processed data is analyzed using a linear regression strategy to obtain the current and voltage data of the power equipment; Obtain the linear relationship between load, voltage and current; Constructing a target loss function based on the linear relationship; Solving the target loss function based on the current data and the voltage data to obtain a load state of the power equipment; A load state analysis result is obtained according to the load state.

5. The method according to claim 1, wherein The step of performing protocol conversion, cleaning, and structuring of the operating data through the industrial Internet platform to obtain processed data includes: Performing protocol conversion on the operating data through the industrial Internet platform, converting data in different formats in the operating data into the same format to obtain converted operating data; performing data cleaning on the conversion operation data, filling missing values ​​in the conversion operation data, and removing abnormal values ​​in the conversion operation data to obtain cleaned operation data; The cleaning operation data is structured to obtain processed data.

6. The method according to claim 5, wherein The step of structurally processing the cleaning operation data to obtain processed data includes: Structuring the cleaning operation data to obtain time series data; Creating metadata tags for each data in the time series data; The time series data is classified according to the metadata tags to obtain processed data.

7. The method according to claim 1, wherein The step of monitoring the power equipment according to the analysis result and generating a real-time monitoring report comprises: Obtaining fault diagnosis data and load status data of the power equipment according to the analysis results; determining a target electric device by using the fault diagnosis data and the load status data; The operating data of the target power equipment is monitored and a real-time monitoring report is generated.

8. The method according to claim 7, wherein The step of monitoring the operating data of the target power equipment and generating a real-time monitoring report includes: When any one of the operating temperature, operating voltage and load status in the operating data of the target power equipment exceeds a set threshold, an abnormal monitoring report is generated and an alarm signal is triggered.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Upon receiving a query instruction sent by a user, determining target viewing content according to the query instruction; Determining a display page and display content on the industrial Internet platform according to the target viewing content; The operating data of the power equipment is displayed on the industrial Internet platform according to the display page and the display content.

10. An electric power equipment operation monitoring device, characterized in that: The power equipment operation monitoring device includes: The acquisition module is used to collect the operating data of the power equipment in real time through the perception layer equipment. The operating data includes environmental parameters, high-voltage cabinet status, transformer status, low-voltage cabinet status, cable tray status, and cable trench status; A processing module, configured to perform protocol conversion, cleaning, and structure processing on the operating data through the industrial Internet platform to obtain processed data; An analysis module is used to analyze and process the processed data based on the industrial Internet platform to obtain analysis results; A monitoring module, configured to monitor the power equipment according to the analysis results and generate a real-time monitoring report; A display module is used to display the real-time monitoring report on the industrial Internet platform.

Images