A detection method, device and storage medium for power safety production

Abstract

The present invention discloses a detection method, device, and storage medium for power production safety. The method includes: analyzing log data for time-series operating parameter changes and normalized operating evolution to obtain operating status characteristics; performing safe production structure evolution on the operating status characteristics based on the log data to construct a power production safety knowledge graph; performing frame-by-frame dynamic optical flow tracking on video data to obtain production behavior characteristics of the dynamic production behavior of operators; performing dynamic behavior visual recognition on the production behavior characteristics and dynamic operation process characteristic analysis to generate dynamic operation process characteristic data; generating a production behavior risk quantification value for the operator's dynamic operation process characteristic data based on the power production safety knowledge graph; and making emergency response decisions based on the production behavior risk quantification value to optimize the power production safety knowledge graph. This embodiment improves the ability to prevent and respond to complex power safety incidents.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a detection method, equipment and storage medium for safe production of electric power. Background Art

[0002] With the rapid development of the power industry and the continuous application of intelligent technology, power safety production has become one of the important links to ensure the stable operation of the power grid and the safety of people's lives and property. In the process of power production, transmission, distribution and operation of related equipment, various emergencies and hidden dangers occur from time to time, bringing challenges to power safety management.

[0003] Traditional power production safety management methods mostly rely on manual inspections, on-site operations and experience accumulation, but these methods have problems such as long response time, missing information, and low risk prediction capabilities, which makes the ability to prevent and respond to complex power safety accidents relatively low. Summary of the Invention

[0004] In view of this, the present invention provides a detection method, device and storage medium for power safety production, so as to improve the ability to prevent and respond to complex power safety accidents.

[0005] A first aspect of the present invention provides a method for detecting safe production of electric power, comprising:

[0006] Obtaining log data from historical monitoring of power production behavior, analyzing the time series operating parameter changes and normalized operation evolution of the log data, and obtaining normalized operating status characteristics of the power equipment;

[0007] Performing safety production structure evolution on the operating status characteristics based on the log data to construct a power safety production knowledge graph;

[0008] Acquire real-time video data of power production behavior monitoring, perform frame-by-frame dynamic optical flow tracking on the video data, and obtain production behavior characteristics of the dynamic production behavior of the operators;

[0009] Performing dynamic behavior visual recognition on the production behavior characteristics and performing dynamic operation process characteristic analysis to generate dynamic operation process characteristic data of the operator;

[0010] Based on the power safety knowledge graph, a quantitative assessment of production safety risks is performed on the dynamic operation process characteristic data of the operator to generate a quantitative value of production behavior risk;

[0011] Emergency response decisions are made based on the quantitative value of the production behavior risk to optimize the power production safety knowledge graph.

[0012] A second aspect of the present invention provides a detection device for safe production of electric power, comprising:

[0013] An operating status feature extraction module is used to obtain log data from historical monitoring of power production behavior, analyze the time series operating parameter changes and normalized operation evolution of the log data, and obtain the normalized operating status features of the power equipment;

[0014] A knowledge graph construction module, configured to perform safety production structure evolution on the operating status characteristics based on the log data to construct a power safety production knowledge graph;

[0015] The production behavior feature extraction module is used to obtain real-time video data of power production behavior monitoring, perform frame-by-frame dynamic optical flow tracking on the video data, and obtain the production behavior features of the dynamic production behavior of the operators;

[0016] An operation process characteristic data generation module is used to perform dynamic behavior visual recognition on the production behavior characteristics and perform dynamic operation process characteristic analysis to generate dynamic operation process characteristic data of the operator;

[0017] A risk quantification value calculation module is used to perform a quantitative assessment of production safety risks on the dynamic operation process characteristic data of the operator based on the power safety knowledge graph to generate a production behavior risk quantification value;

[0018] A knowledge graph optimization module is used to make emergency response decisions based on the quantitative value of the production behavior risk to optimize the power production safety knowledge graph.

[0019] A third aspect of the present invention provides an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the detection method for safe power production as described in the first aspect above.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for detecting safe power production as described in the first aspect above.

[0024] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the detection method for safe power production as described in the first aspect above.

[0025] In this embodiment, log data from historical monitoring of power production behavior is obtained, and the log data is analyzed for time-series operating parameter changes and normalized operating evolution to obtain normalized operating state characteristics of power equipment; based on the log data, the operating state characteristics are subjected to safe production structure evolution to construct a power production safety knowledge graph; real-time video data from power production behavior monitoring is obtained, and frame-by-frame dynamic optical flow tracking is performed on the video data to obtain production behavior characteristics of the operator's dynamic production behavior; dynamic behavior visual recognition is performed on the production behavior characteristics and dynamic operation process characteristic analysis is performed to generate dynamic operation process characteristic data of the operator; based on the power safety knowledge graph, a safe production risk quantitative assessment is performed on the operator's dynamic operation process characteristic data to generate a production behavior risk quantitative value; and based on the production behavior risk quantitative value, emergency response decisions are made to optimize the power production safety knowledge graph. This embodiment continuously iteratively optimizes the power production safety knowledge graph based on real-time production business, so that the power production safety knowledge graph continuously adapts to business development, accumulates a large amount of information, accurately predicts potential production risks, and quickly provides intelligent production decisions, which helps to improve the efficiency and accuracy of accident response and enhance the ability to prevent and respond to complex power safety accidents.

[0026] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is a flow chart of a detection method for electric power production safety provided in Example 1 of the present invention.

[0029] Figure 2 This is a structural diagram of a detection device for electric power production safety provided in the second embodiment of the present invention.

[0030] Figure 3 This is a structural diagram of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Example 1

[0034] See also Figure 1 , shows a flow chart of a method for detecting safe production of electric power provided by the first embodiment of the present invention. This method can be executed by a detection device for safe production of electric power. The detection device for safe production of electric power can be implemented in the form of hardware and / or software. The detection device for safe production of electric power can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] Step 101: Obtain log data from historical monitoring of power production behavior, analyze the time series operating parameter changes and normalized operation evolution of the log data, and obtain normalized operating status characteristics of the power equipment.

[0036] In this embodiment, log data of historical monitoring of power production behavior can be obtained from the server, and the log data can be analyzed for time series operation parameter changes and normalized operation evolution, thereby obtaining the normalized operation status characteristics of the power equipment.

[0037] Generally, log data is stored in the database or data warehouse of the power production management system, and is extracted from it using SQL (Structured Query Language) queries or data extraction tools (such as Python's Pandas library).

[0038] Log data includes information such as timestamp, device ID, operating parameters (such as voltage, power, temperature, etc.). If there is other relevant data (such as equipment maintenance records, fault records, etc.), other relevant data will be integrated with the original log data to form a comprehensive log data.

[0039] Clean the log data, remove duplicate records, handle missing values, and format the data into a unified structure (such as CSV or DataFrame format) to ensure the integrity and consistency of the log data. Use data processing tools (such as Pandas) to load the cleaned log data and set the timestamp as the index of the log data to ensure that the log data is arranged in chronological order to facilitate time series analysis.

[0040] Select appropriate time series analysis methods, such as time series decomposition, moving average, or autoregressive integrated moving average (ARIMA) model, to analyze the changing trends of operating parameters. Perform time series change analysis on each operating parameter, calculate the parameter's mean, variance, maximum, minimum, and other statistical characteristics, and identify the changing patterns and trends.

[0041] Select an appropriate normalization method, such as Z-score normalization or Min-Max scaling, to standardize the operating parameter change data, perform normalization on the operating parameter change data, ensure that data of different parameters are compared under the same dimension, extract the operating status characteristics of the power equipment from the normalized data, such as the normal operating range, operating stability, etc., and further analyze the extracted operating status characteristics to identify the normalized operating status characteristics of the power equipment, such as normal operating conditions, abnormal fluctuations, etc.

[0042] The operating status characteristics are organized into data sets and stored in databases or files to provide a basis for subsequent monitoring and management.

[0043] In one embodiment of the present invention, step 101 may include the following steps:

[0044] Step 1011: Filter abnormal outlier data points from the log data.

[0045] In actual applications, the log data of historical monitoring of power production behavior provides a lot of behavioral information, which can be used to analyze the evolution and change trends of power production business.

[0046] Using machine learning methods (such as Isolation Forest and DBSCAN) to detect abnormal outlier data points and filter out abnormal outlier data points from log data helps reduce interference and make the data more representative, thereby effectively improving the quality of log data and ensuring the accuracy and reliability of subsequent analysis.

[0047] Step 1012: If filtering is completed, the log data is segmented into time series windows to obtain multiple log segments.

[0048] Defining the size of the time series window (such as 1 hour, 1 day) and the overlap strategy (such as no overlap or partial overlap), using programming tools (such as Python's Pandas library) to split log data into time series windows, and saving the log segments within each time series window as independent data structures (such as DataFrame) helps to understand the characteristics and dynamic changes of log data in more detail, better observe the time dependency between multiple log segments, and discover periodic changes or trends.

[0049] Step 1013: Identify the business device entity from the multiple log segments.

[0050] Use regular expressions, string matching, or machine learning models (such as text classifiers) to identify the device information in each log segment, add tags to the device data in each time window segment, record the device type, ID, and related features, and thus identify business device entities in multiple log segments. Save the marked business device entities to a new data structure for subsequent analysis, which helps to associate log data with specific power production equipment and achieve more refined analysis and monitoring. Marking the business device entities of power equipment provides a basis for subsequent parameter analysis.

[0051] Step 1014: Perform time-series operation parameter change analysis on multiple business equipment entities to generate operation parameter change data.

[0052] Use time series analysis methods (such as differential method and sliding window analysis) to analyze the changes in the operating parameters of each business device entity, identify the upward, downward or fluctuating trends of the parameters, and generate operating parameter change data for each business device entity, including statistical indicators (such as mean value and standard deviation) and change trends (such as increase or decrease). Save the operating parameter change data of each device to a new file or database to form a result set for subsequent analysis.

[0053] Step 1015: Perform normalized operation evolution on the operating parameter change data to obtain normalized operating state characteristics of the power equipment.

[0054] Select an appropriate normalization method, such as Z-score standardization, Min-Max normalization, or box plot method, to normalize the operating parameter change data of each business equipment entity. By analyzing the normalized operating parameter change data and extracting the normalized operating status characteristics of the power equipment, including the normal range and abnormal threshold, the normalized operating status characteristics are saved as a report or data file to facilitate subsequent decision support and business optimization.

[0055] Step 102: Evolve the safety production structure of the operating status characteristics based on the log data to construct a power safety production knowledge graph.

[0056] In practical applications, based on log data, the operating status characteristics of power equipment can be used to evolve the safety production structure, thereby constructing a knowledge graph, which is recorded as the power safety production knowledge graph.

[0057] Among them, knowledge graph, as a way of building relationships between entities, can realize the integration and analysis of multi-dimensional information. In the field of power production safety, knowledge graph can not only systematically integrate various types of power equipment, production links, management specifications and other information, but also reveal potential safety hazards and predict failures or accidents through reasoning and association analysis, thereby providing accurate decision-making support for power production safety.

[0058] In one embodiment of the present invention, step 102 may include the following steps:

[0059] Step 1021: Perform operation record analysis on multiple log segments to extract operation record data.

[0060] In this embodiment, the job records of multiple log segments can be analyzed based on the various tags in the log segments to understand the specific work conditions and operation records in each log segment. Extracting job record data from multiple log segments helps track workflows and identify potential security risks and issues.

[0061] Ensure that multiple log segments have been cleaned and structured to include necessary fields (such as timestamp, job type, and device ID). Use a programming language (such as Python) and a data processing library (such as Pandas) to load the log segments one by one, extract data related to specific power equipment or job types in the job records, format the data, ensure that each record contains all necessary information, and save the extracted job record data to a new structured file (such as a CSV file or a database) in preparation for subsequent analysis.

[0062] Step 1022: Perform safety production behavior analysis on the operation record data to extract safety production behavior data.

[0063] In this embodiment, key features of safe production behaviors, such as operation records, maintenance records, and exception handling records, are determined, and data analysis tools (such as Python's Scikit-learn) are used to analyze the job record data. Behaviors related to safe production are identified based on the key features, and a safe production behavior label is added to each job record to form a data set of safe production behaviors. The extracted safe production behavior data is saved in a new data structure (such as DataFrame) to characterize the patterns and characteristics of safe production behaviors, which helps to evaluate the degree of compliance with behavioral codes and safety awareness of operators.

[0064] Step 1023: extract the behavior timestamp and business processing personnel data from the production safety behavior data.

[0065] In this embodiment, behavior timestamps (job start and end timestamps) and business processing personnel data (such as operator ID, role, etc.) can be extracted from the production safety behavior data, and the timestamps and business processing personnel information can be integrated into a new data set, which helps to record the operation time and behavior information of the operators, facilitating subsequent analysis and behavior feature modeling.

[0066] Step 1024: Perform multi-period production behavior feature analysis based on the behavior timestamp and business processing personnel data to generate production behavior features for different time periods.

[0067] Ensure that each record contains behavior identification, timestamp and business processing personnel data, divide the records into multiple time periods (such as by hour, by shift) according to the timestamp, generate an independent data set for each time period, and use statistical analysis methods (such as mean, frequency distribution) to analyze the production behavior characteristics of each time period to identify behavior patterns in different time periods. This will help understand the changes and trends in production behavior patterns and identify potential safety hazards and improvement opportunities.

[0068] Step 1025: Based on the production behavior characteristics of different time periods, the operating status characteristics are subjected to safety production structure evolution to construct a knowledge graph of power safety production.

[0069] In the specific implementation, Python's Matplotlib or Seaborn is used for data visualization. According to the production behavior characteristics of different periods, the data related to safe production (such as equipment status, operation behavior, and personnel behavior) are integrated to build a knowledge graph of power safety production. The nodes (such as equipment, personnel, and behavior) and edges (such as operation relationships and influence relationships) in the knowledge graph are defined to build the structure of the knowledge graph. The integrated data is imported and generated into a knowledge graph using knowledge graph construction tools (such as Neo4j and GraphDB). This is recorded as a knowledge graph of power safety production, which helps to systematically record safety information and support risk assessment and decision-making.

[0070] In one embodiment of the present invention, step 1025 may further include the following steps:

[0071] Step 10251: Identify key behavior time nodes based on production behavior characteristics in different time periods.

[0072] In this embodiment, it is ensured that the production behavior characteristics of different time periods have been cleaned and structured, including timestamps and behavior categories, etc., and statistical analysis methods (such as cluster analysis) or machine learning algorithms (such as decision trees) are used to identify key behavior time nodes. For example, identifying key nodes by analyzing the frequency and importance of behavior helps to determine the important time points in the production process. Extracting key behavior time nodes helps focus on key events and provides a basis for subsequent analysis.

[0073] Step 10252: Set pre- and post-trigger conditions for the key behavior time nodes.

[0074] In this embodiment, pre- and post-trigger conditions are set for key behavior time nodes, and the pre- and post-trigger conditions include pre-trigger conditions (i.e., behaviors before the key behavior time nodes) and post-trigger conditions (i.e., behaviors after the key behavior time nodes). Analyzing the pre- and post-trigger conditions of key time nodes helps to understand the background and triggering conditions of key events in these key behavior time nodes, and obtaining the pre- and post-trigger conditions of key behavior time nodes helps to establish behavior triggering logic.

[0075] Step 10253: Based on the pre- and post-trigger conditions of the key behavior nodes, perform up- and down-operation behavior dependency analysis on the production behavior characteristics of different time periods to obtain production behavior time series dependency data.

[0076] Use association rule mining algorithms (such as Apriori or FP-Growth) to extract pre- and post-conditions, and implement it using Python's mlxtend library. Based on the pre- and post-conditions of key behavior time nodes, analyze the dependencies between context operations. Use graph theory methods to construct a directed graph to represent the dependencies between operations, record the dependencies, and generate production behavior temporal dependency data. Production behavior temporal dependency data is the temporal dependency of production behaviors, which can reveal the association and influence between operational behaviors and help understand the sequence and logic of operational behaviors.

[0077] Step 10254: Mining the safety production standard logic based on the production behavior time series dependency data.

[0078] Ensure that each record contains relevant key behavior time nodes and production behavior timing dependency data, use classification algorithms such as logistic regression or decision trees to analyze each record, extract logical rules that meet production safety standards, record them as production safety standard logic, and save the production safety standard logic as a rule library, which will help standardize operational behaviors, establish standard operating procedures for production safety, and improve work efficiency and safety.

[0079] Step 10255: Perform topological structure association analysis on production behavior characteristics in different time periods according to the operating status characteristics to extract topological structure data.

[0080] In this embodiment, a topological structure association analysis is performed on the production behavior characteristics of different time periods based on the operating status characteristics, thereby constructing topological structure data. The topological structure data is a normalized topological structure association between operating behavior and power equipment. Each node represents a device or behavior, and each edge represents the relationship between the two. It reveals the relationship between the operating behavior and the operating status of the power equipment, which helps to understand the impact and effect of the operating behavior on the operation of the power equipment.

[0081] Step 10256: Evolve the safety production graph structure of the topological structure data based on the safety production standard logic to construct a power safety production knowledge graph.

[0082] In this embodiment, a graph analysis tool (such as NetworkX) is used to analyze the topology data, extract the association data between behavior and equipment, combine the safety production standard logic and the behavior-equipment normalization topology data, and construct a preliminary power safety production knowledge graph. A graph database (such as Neo4j) is used to import the standard logic and topology association data to construct a comprehensive power safety production knowledge graph.

[0083] Step 103: Acquire video data for real-time monitoring of power production behavior, perform frame-by-frame dynamic optical flow tracking on the video data, and obtain production behavior characteristics of the operator's dynamic production behavior.

[0084] In practical applications, real-time video data monitoring power production behavior can be obtained from cameras deployed in power production areas. The cameras should be deployed so that they cover the power production area. Configure the camera's network connection and use protocols such as RTSP (Real-Time Streaming Protocol) to stream video data in real time. Store the video data on a local server or in cloud storage for subsequent analysis. Regularly check the quality of the video data to ensure that the image is clear and free of interference to facilitate subsequent image processing and analysis. Select an appropriate image processing library (such as OpenCV or FFmpeg) to process the video data, supporting frame-by-frame analysis and optical flow calculation.

[0085] The video data contains multiple frames of image data. Tools such as OpenCV are used to extract the video frame by frame to generate image sequences. The image data of each frame in the video data are preprocessed, including resizing, grayscale conversion, and noise removal, to improve the accuracy of subsequent optical flow calculations.

[0086] Select a suitable optical flow calculation algorithm, such as the Lucas-Kanade method or the Farneback method, to calculate the motion vector between consecutive frames. Use OpenCV's calcOpticalFlowFarneback or calcOpticalFlowPyrLK function to perform dynamic optical flow calculation and extract the motion optical flow information. Based on the calculated optical flow information, draw the motion trajectory of the operator, superimpose arrows or trajectory lines on the original image data to display the motion direction and speed, and extract the operator's dynamic behavior trajectory features, such as motion path, speed, acceleration, etc., from the optical flow data. These features are used for subsequent behavior analysis to identify the production behavior characteristics of the operator's dynamic production behavior.

[0087] In one embodiment of the present invention, step 103 may include the following steps:

[0088] Step 1031: Filter the video data.

[0089] In this embodiment, methods such as Gaussian filtering algorithm, bilateral filtering algorithm, spatial filtering algorithm, etc. can be used to filter each frame of image data in the video data to improve the clarity of the image data, thereby improving the quality of the video data.

[0090] Step 1032: If the filtering process is completed, the video data is subjected to visual recognition of the operator, and the image frame where the operator is located is segmented.

[0091] In this embodiment, target detection algorithms such as YOLO and SSD can be used to perform visual identification of workers on video data with workers as the target, thereby segmenting the image frame where the workers are located, saving each detected image frame as an independent image or area, and recording the position, timestamp and related information of each image frame to form structured data, which helps to accurately locate and track workers, and provides basic information for subsequent analysis.

[0092] Step 1033: Perform frame-by-frame dynamic optical flow tracking on the image frame to extract motion vector change data.

[0093] In this embodiment, an optical flow algorithm (such as Lucas-Kanade or Farneback) can be used to perform dynamic optical flow tracking on the image frame frame by frame, extract motion vector change data, capture the motion trajectory and changes of the operator, and help understand the dynamic behavior of the operator.

[0094] Step 1034: Perform dynamic behavior trajectory analysis on the motion vector change data to obtain production behavior characteristics of the operator's dynamic behavior.

[0095] In this embodiment, data analysis tools (such as Python's Pandas and NumPy) are used to perform dynamic behavior trajectory analysis on motion vector change data, and the dynamic behavior trajectory characteristics of the operator, including speed, acceleration, and movement direction, are extracted from the motion vector data. The operator's behavior pattern (such as walking, pausing, operating, etc.) is identified, thereby obtaining the production behavior characteristics of the operator's dynamic behavior, which helps to evaluate the standardization and efficiency of operations and discover potential safety issues.

[0096] Step 104 : Perform dynamic behavior visual recognition on the production behavior characteristics and perform dynamic operation process characteristic analysis to generate dynamic operation process characteristic data of the operator.

[0097] In practical applications, dynamic behavior visual recognition of production behavior characteristics and dynamic operation process characteristics analysis are performed to generate dynamic operation process characteristic data of operators.

[0098] Use a data processing library (such as Pandas) to load previously extracted production behavior features to ensure data availability, clean the production behavior features, remove outliers, and perform normalization to facilitate subsequent model training and analysis. Select a suitable deep learning model for dynamic behavior recognition, such as a convolutional neural network (CNN) combined with a long short-term memory network (LSTM) to process time series data, etc. Apply the model to the processing of real-time production behavior features, identify the dynamic behavior of operators, generate corresponding behavior labels, analyze the operator's operating procedures based on the identified dynamic behavior labels, and extract the corresponding dynamic operation procedure feature data, such as operation type, operation duration, and operation frequency.

[0099] Afterwards, clustering algorithms (such as K-Means or DBSCAN) are used to classify the dynamic operation process feature data, identify different process patterns and behavioral characteristics, perform statistical analysis on the frequency and duration of different operation processes, and generate relevant statistical charts for visual display and subsequent decision support.

[0100] Organize dynamic operation process characteristic data into a structured data set, including information such as operation type, duration, frequency, etc., and mark it with corresponding behavior tags. Save the organized dynamic operation process characteristic data to the database or file system for subsequent query and analysis, write analysis reports, summarize the dynamic operation process characteristic data of operators, and provide them to management for decision support.

[0101] In one embodiment of the present invention, step 104 may include the following steps:

[0102] Step 1041: Perform dynamic behavior visual recognition on the production behavior characteristics to obtain the power production behavior data of the operator.

[0103] Use deep learning models (such as LSTM, CNN-RNN or other time series models) to perform dynamic behavior visual recognition of production behavior characteristics. Input the dynamic behavior trajectory feature data of the operator into the selected deep learning model for inference, identify the specific behavior of the operator in the power production process (such as operating equipment, inspecting equipment, etc.), and store the identified power production behavior data (including behavior type, timestamp, etc.) in a structured database.

[0104] Step 1042: Calculate the dynamic behavior duration of the power production behavior data.

[0105] In this embodiment, the operator's power production behavior data is calculated through dynamic behavior duration, that is, the duration of different behaviors of the operator in the production process is quantified. The dynamic behavior duration is obtained, which helps to understand the time the operator spends on various tasks and helps to evaluate work efficiency and standard operating procedures.

[0106] Specifically, the start time and end time are extracted from the operator's power production behavior data. For each behavior, its duration is calculated as the dynamic behavior duration. Dynamic behavior duration = end time - start time. The dynamic behavior duration is associated with the behavior data and saved as structured data.

[0107] Step 1043: quantify the behavior interval of the power production behavior data to obtain a production behavior interval value.

[0108] The behavior intervals of the operators' power production behavior data are quantified, the time intervals between different production behaviors are measured, and the production behavior interval values ​​are obtained. This helps to determine the transition time between operators' different tasks and provides a basis for optimizing the operation process and reducing waiting time.

[0109] The so-called behavior interval usually refers to the time difference between two consecutive behaviors. The behavior data of the operator is processed, the time difference between each two consecutive behaviors is calculated, and the production behavior interval value is obtained.

[0110] Step 1044: Calculate the sequential characteristics of the operator's behavior based on the production behavior interval value.

[0111] Selecting appropriate sequence analysis methods, such as Markov models or sequential pattern mining, and conducting sequential analysis of multiple operating behaviors based on production behavior interval values ​​and behavior types will help reveal the sequential characteristics of operator behavior and help understand the association and sequence between different operating behaviors, including the frequency and sequence relationship of behaviors, providing a reference for the formulation of operating specifications and training.

[0112] Step 1045: Use the dynamic behavior duration and behavior sequence characteristics to construct the dynamic operation process characteristic data of the operator.

[0113] In this embodiment, a suitable analysis method (such as cluster analysis or principal component analysis) is selected to perform correlation analysis on the dynamic behavior duration and behavior sequence characteristics. The dynamic behavior duration and behavior sequence characteristics are combined to generate dynamic operation process characteristic data of the operators, which helps to identify the operation patterns and behavior patterns of the operators and provide support for safe production management and risk control.

[0114] Step 105: Based on the power safety knowledge graph, a quantitative assessment of production safety risks is performed on the dynamic operation process characteristic data of the operators to generate a quantitative value of production behavior risk.

[0115] Based on the power safety knowledge graph, the dynamic operation process characteristic data of the operators are used to conduct a quantitative assessment of production safety risks, thereby generating a quantitative value of production behavior risk and intuitively displaying the risks of production behavior.

[0116] Use data processing tools (such as Pandas) to load dynamic operation process characteristic data from the database or file, such as operation type, duration, frequency, and other information, clean the dynamic operation process characteristic data, handle missing values ​​and outliers, and standardize or normalize to ensure data quality and consistency.

[0117] Based on the power safety knowledge graph, determine the risk assessment indicators related to operational behavior, such as operation complexity, equipment status, historical failure rate, etc., select appropriate risk assessment models, such as the analytic hierarchy process (AHP), fuzzy logic model, machine learning model, etc., input the dynamic operation process characteristic data into the trained risk assessment model, calculate the risk score of each operation, and for each operation behavior, combine the risk scores of multiple evaluation indicators to calculate the quantitative value of its production behavior risk.

[0118] Considering the impact of different indicators on risk, the risk quantification value is normalized so that it is within a unified range to facilitate subsequent comparison and analysis. The calculated production behavior risk quantification value is integrated with the corresponding operation characteristic data to form a complete data set. The generated risk quantification value data is saved in a database or file to ensure the convenience of subsequent query and analysis. A detailed risk assessment report is generated to explain the production behavior risk quantification value of each operation and provide corresponding risk management recommendations to support decision-making.

[0119] In one embodiment of the present invention, step 105 may include the following steps:

[0120] Step 1051: Analyze the production safety operation rules of the dynamic operation process feature data based on the power safety knowledge graph to extract irregular operation behavior data.

[0121] In this embodiment, it is ensured that the dynamic operation process characteristic data of the operators in the power safety knowledge graph have been sorted and connected with the relevant safety operation rules. The association rule mining algorithm (such as Apriori or FP-Growth) is used to analyze the production safety operation rules of the dynamic operation process characteristic data based on the power safety knowledge graph, extract irregular operation behavior data, identify operation behaviors that do not comply with safety operation procedures or potential risks, record these irregular operation behavior data, and organize the identified safety hazard data into a report containing information such as hazard type, frequency of occurrence and consequences. This helps to identify violations of operation rules and improve the ability to identify potential safety risks.

[0122] Step 1052: Analyze the operation behavior data for potential safety hazards in production, and generate operation behavior safety hazard data.

[0123] In this embodiment, a risk propagation model is constructed using graph theory or network analysis methods to describe the relationship between different hidden dangers. The irregular operation behavior data is analyzed for production safety hidden dangers, which helps to discover potential safety problems and behavioral anomalies, which are recorded as operation behavior safety hidden danger data, providing a basis for formulating improvement measures and strengthening training.

[0124] Step 1053: Mining the security risk diffusion paths of the operational behavior security hazard data to extract the risk diffusion paths.

[0125] In this embodiment, a path mining algorithm (such as the Dijkstra algorithm or the A* algorithm) is used to mine the security risk diffusible paths of the operational behavior safety hazard data, extract the risk diffusible paths, and thus identify the potential paths for the propagation of security risks, which helps to understand the propagation mechanism and key influencing factors of security risks.

[0126] Step 1054: perform risk propagation prediction on the risk diffusible path to obtain safety risk propagation prediction data.

[0127] In this embodiment, a suitable risk propagation prediction model (such as an SIR model or an Agent-Based model) is selected, and modeling is performed according to historical data and hidden danger characteristics. Based on the constructed model, risk propagation is simulated for all risk diffusible paths to obtain safety risk propagation prediction data, which helps to predict potential safety risk propagation situations, helps to identify safety issues in advance, identify potential high-risk areas and key risk factors, and take measures to reduce the possibility of risk occurrence.

[0128] Step 1055: Conduct a quantitative assessment of production safety risks on the safety risk propagation prediction data to generate a quantitative value of production behavior risk.

[0129] In practical applications, it is necessary to determine the indicators used for quantitative assessment of safety risks, such as the probability of risk occurrence, the degree of potential consequences, controllability, etc., conduct quantitative assessment of production safety risks on the safety risk propagation prediction data, and calculate the quantitative value of production behavior risk for each risk path, which will help to quantitatively assess the severity of potential risks and provide a basis for formulating risk management strategies.

[0130] Step 106: Make emergency response decisions based on the quantitative value of production behavior risk to optimize the power production safety knowledge graph.

[0131] In practical applications, decisions can be made in the dimension of emergency response based on the quantitative value of production behavior risks, thereby optimizing the knowledge graph of power safety production.

[0132] Use data processing tools (such as Pandas) to load production behavior risk quantification value data from the database or file, clean the production behavior risk quantification value data, process missing values ​​and outliers, and ensure the accuracy and consistency of the data for subsequent analysis.

[0133] Set risk thresholds based on industry standards and historical data to distinguish different levels of risk (such as low, medium, and high risks), and formulate corresponding emergency response strategies for different risk levels. For example, low risk: routine monitoring and vigilance; medium risk: strengthen monitoring,

[0134] Develop temporary operating procedures; High risk: Immediately activate the emergency plan and mobilize relevant resources. After the emergency response is implemented, record feedback data, including response effectiveness, execution efficiency, and processing results, to evaluate the effectiveness of the emergency response strategy. Analyze the feedback data to identify deficiencies in the emergency response strategy. Optimize the emergency response strategy using statistical analysis or machine learning methods (such as regression analysis). Based on the feedback analysis results, adjust and optimize the emergency response strategy to improve the timeliness and effectiveness of future responses.

[0135] Based on the optimized emergency response strategy, a real-time monitoring system is built. Data stream processing technology (such as Apache Kafka) is combined with intelligent decision-making models (such as reinforcement learning) to achieve real-time emergency response decisions. Based on the optimized emergency response strategy and decision results, the power safety production knowledge graph is updated, new nodes and edges are added to reflect the latest knowledge and experience, and the emergency response results and optimized strategy data are mapped to the power safety production knowledge graph to form a connection between emergency response and safety production. The integrity and accuracy of the power safety production knowledge graph are regularly verified to ensure that it reflects the latest safety production knowledge, and updated and optimized when necessary.

[0136] In one embodiment of the present invention, step 106 may include the following steps:

[0137] Step 1061: Make an emergency response decision based on the production behavior risk quantification value to obtain a risk behavior emergency response strategy.

[0138] In this embodiment, the quantitative values ​​of production behavior risks are collected and organized to ensure that the data is complete and accurate. Methods such as decision trees, fuzzy logic, or expert systems are used to determine emergency response strategies based on the quantitative values ​​of production behavior risks. Corresponding response measures are set for different quantitative values ​​of production behavior risks. For example, high risk: immediate shutdown and inspection; medium risk: increased monitoring frequency; low risk: regular inspection; and emergency response strategies are recorded as documents or database entries.

[0139] By presetting decision rules for application responses and making emergency response decisions based on the quantitative value of production behavior risks, it is helpful to formulate targeted emergency response strategies and obtain risk behavior emergency response strategies to improve the efficiency and accuracy of responding to emergencies and risk situations.

[0140] Step 1062: Perform iterative learning of strategy execution on the risk behavior emergency response strategy to obtain an iterative learning representation of the response strategy.

[0141] In this embodiment, corresponding operations are implemented according to the formulated emergency response strategy, the execution results and actual effects are recorded, and data related to the strategy execution are collected, including response time, effect evaluation, subsequent risk occurrence, etc., and machine learning algorithms (such as Q-learning or reinforcement learning) are used to analyze the execution results, adjust and optimize the response strategy, and generate an iterative learning representation of the response strategy, which helps to continuously optimize the response strategy. The generation of the iterative learning representation of the response strategy learns from experience and improves the intelligence level of the emergency response.

[0142] Step 1063: Perform multi-scenario migration optimization on the iterative learning representation of the response strategy to obtain a migration-optimized iterative learning representation.

[0143] In this embodiment, the effects and improvement suggestions of the emergency response strategy are recorded, different application scenarios are identified, the similarities and differences between these scenarios and existing strategies are analyzed, and transfer learning algorithms (such as domain adaptation and model fine-tuning) are applied to migrate the response strategy to the new scenario. The iterative learning representation of the response strategy is optimized for multi-scenario migration to obtain a migration-optimized iterative learning representation, which makes the emergency response more adaptable, helps to apply experience to different situations, and improves the quality and flexibility of decision-making.

[0144] Step 1064: Use transfer optimization iterative learning representation to make intelligent response decisions on the power safety production knowledge graph to optimize the power safety production knowledge graph.

[0145] In this embodiment, the transfer optimization iterative learning representation is used to make intelligent response decisions on the power safety production knowledge graph, thereby optimizing the power safety production knowledge graph and improving the intelligence level of decision-making. Constructing the power safety production knowledge graph helps to integrate and apply various optimization measures and improve the efficiency and level of safety production management.

[0146] This process mainly involves the reconstruction of the power safety production knowledge graph, integrating the optimized emergency response strategies and learning results into the power safety production knowledge graph to form a more accurate decision support system. It combines graph analysis and machine learning technology to optimize the intelligent response decision-making capabilities of the power safety production knowledge graph to ensure that emergency response strategies can be effectively implemented in new scenarios.

[0147] Set up a monitoring system to collect safety data (such as equipment status, operation records, etc.) in the power production process in real time, use the optimized safety production knowledge graph to analyze the real-time data, identify potential safety hazards and risks, and quickly execute emergency response decisions based on real-time analysis results to ensure the safety of the power production process. Collect the results of real-time analysis and emergency response, and continuously update and optimize the power production safety knowledge graph and emergency response strategy.

[0148] In this embodiment, log data from historical monitoring of power production behavior is obtained, and the log data is analyzed for time-series operating parameter changes and normalized operating evolution to obtain normalized operating state characteristics of power equipment; based on the log data, the operating state characteristics are subjected to safe production structure evolution to construct a power production safety knowledge graph; real-time video data from power production behavior monitoring is obtained, and frame-by-frame dynamic optical flow tracking is performed on the video data to obtain production behavior characteristics of the operator's dynamic production behavior; dynamic behavior visual recognition is performed on the production behavior characteristics and dynamic operation process characteristic analysis is performed to generate dynamic operation process characteristic data of the operator; based on the power safety knowledge graph, a safe production risk quantitative assessment is performed on the operator's dynamic operation process characteristic data to generate a production behavior risk quantitative value; and based on the production behavior risk quantitative value, emergency response decisions are made to optimize the power production safety knowledge graph. This embodiment continuously iteratively optimizes the power production safety knowledge graph based on real-time production business, so that the power production safety knowledge graph continuously adapts to business development, accumulates a large amount of information, accurately predicts potential production risks, and quickly provides intelligent production decisions, which helps to improve the efficiency and accuracy of accident response and enhance the ability to prevent and respond to complex power safety accidents.

[0149] Example 2

[0150] See also Figure 2 , shows a schematic diagram of the structure of a detection device for power production safety provided by the second embodiment of the present invention. Figure 2 As shown, the device includes:

[0151] The operating status feature extraction module 201 is used to obtain log data from historical monitoring of power production behavior, analyze the time series operating parameter changes and normalized operation evolution of the log data, and obtain the normalized operating status features of the power equipment;

[0152] A knowledge graph construction module 202 is configured to perform a safety production structure evolution on the operating status characteristics based on the log data to construct a power safety production knowledge graph;

[0153] The production behavior feature extraction module 203 is used to obtain video data of real-time monitoring of power production behavior, perform frame-by-frame dynamic optical flow tracking on the video data, and obtain production behavior features of the dynamic production behavior of the operator;

[0154] An operation process characteristic data generation module 204 is used to perform dynamic behavior visual recognition on the production behavior characteristics and perform dynamic operation process characteristic analysis to generate dynamic operation process characteristic data of the operator;

[0155] A risk quantification value calculation module 205 is configured to perform a quantitative assessment of production safety risks on the dynamic operation process characteristic data of the operator based on the power safety knowledge graph to generate a production behavior risk quantification value;

[0156] The knowledge graph optimization module 206 is used to make emergency response decisions based on the quantitative value of the production behavior risk to optimize the power production safety knowledge graph.

[0157] In one embodiment of the present invention, the operating status feature extraction module 201 is further configured to:

[0158] filtering abnormal outlier data points from the log data;

[0159] If filtering is completed, the log data is segmented into time series windows to obtain multiple log segments;

[0160] identifying a service device entity from a plurality of the log segments;

[0161] Performing time-series operation parameter change analysis on the plurality of service device entities to generate operation parameter change data;

[0162] The operating parameter change data is subjected to normalized operation evolution to obtain normalized operating state characteristics of the power equipment.

[0163] In one embodiment of the present invention, the knowledge graph construction module 202 is further configured to:

[0164] performing operation record analysis on the plurality of log segments to extract operation record data;

[0165] Performing a production safety behavior analysis on the operation record data to extract production safety behavior data;

[0166] Extracting behavior timestamps and business processing personnel data from the production safety behavior data;

[0167] Perform multi-period production behavior feature analysis based on the behavior timestamp and the business processing personnel data to generate production behavior features for different time periods;

[0168] The safety production structure evolution of the operating status characteristics is performed based on the production behavior characteristics in different time periods to construct a knowledge graph of power safety production.

[0169] In one embodiment of the present invention, the knowledge graph construction module 202 is further configured to:

[0170] Identifying key behavior time nodes for the production behavior characteristics in different time periods;

[0171] Setting pre- and post-trigger conditions for the key behavior time nodes;

[0172] Performing up-down operation behavior dependency analysis on the production behavior characteristics in different time periods based on the pre- and post-trigger conditions of the key behavior nodes to obtain production behavior time series dependency data;

[0173] The production behavior time sequence relies on data mining safety production standard logic;

[0174] Performing a topological structure association analysis on the production behavior characteristics in different time periods according to the operating state characteristics to extract topological structure data;

[0175] Based on the safety production standard logic, the topological structure data is evolved into a safety production graph structure to construct a power safety production knowledge graph.

[0176] In one embodiment of the present invention, the production behavior feature extraction module 203 is further configured to:

[0177] performing filtering processing on the video data;

[0178] If the filtering process is completed, the video data is subjected to visual recognition of the operator, and the image frame where the operator is located is segmented;

[0179] Performing frame-by-frame dynamic optical flow tracking on the image frame to extract motion vector change data;

[0180] Dynamic behavior trajectory analysis is performed on the motion vector change data to obtain production behavior characteristics of the operator's dynamic behavior.

[0181] In one embodiment of the present invention, the operation process characteristic data generating module 204 is further configured to:

[0182] Performing dynamic behavior visual recognition on the production behavior characteristics to obtain power production behavior data of the operator;

[0183] Calculating dynamic behavior duration for the power production behavior data;

[0184] quantifying the behavior interval of the power production behavior data to obtain a production behavior interval value;

[0185] Calculating sequential characteristics of operator behavior based on the production behavior interval value;

[0186] The dynamic behavior duration and the behavior sequence characteristics are used to construct dynamic operation process characteristic data of the operator.

[0187] In one embodiment of the present invention, the risk quantification value calculation module 205 is further configured to:

[0188] Performing safety production operation rule analysis on the dynamic operation process feature data based on the power safety knowledge graph to extract irregular operation behavior data;

[0189] Performing a production safety hazard analysis on the operation behavior data to generate operation behavior safety hazard data;

[0190] Mining the diffusible paths of security risks on the data of potential safety hazards of the operation behaviors to extract the diffusible paths of risks;

[0191] Perform risk propagation prediction on the risk diffusible path to obtain safety risk propagation prediction data;

[0192] Conduct a quantitative assessment of production safety risks on the safety risk propagation prediction data to generate a quantitative value of production behavior risk.

[0193] In one embodiment of the present invention, the knowledge graph optimization module 206 is further configured to:

[0194] Make an emergency response decision based on the production behavior risk quantification value to obtain a risk behavior emergency response strategy;

[0195] Performing iterative learning on the risk behavior emergency response strategy to obtain an iterative learning representation of the response strategy;

[0196] Performing multi-scenario migration optimization on the iterative learning representation of the response strategy to obtain a migration-optimized iterative learning representation;

[0197] The migration optimization iterative learning representation is used to make intelligent response decisions on the electric power production safety knowledge graph to optimize the electric power production safety knowledge graph.

[0198] The detection device for electric power safety production provided by the embodiment of the present invention can execute the detection method for electric power safety production provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the detection method for electric power safety production.

[0199] Example 3

[0200] See also Figure 3, shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0201] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0202] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0203] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the detection method for safe power production.

[0204] In some embodiments, the detection method for safe production of electric power may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the detection method for safe production of electric power described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the detection method for safe production of electric power in any other appropriate manner (e.g., by means of firmware).

[0205] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0206] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0207] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0208] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0209] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0210] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0211] Example 4

[0212] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the detection method for safe power production provided by any embodiment of the present invention.

[0213] The computer program product may be implemented by writing computer program code for performing the operations of the present invention 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 cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0214] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0215] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A detection method for electric power production safety, characterized in that: include: Obtaining log data from historical monitoring of power production behavior, analyzing the time series operating parameter changes and normalized operation evolution of the log data, and obtaining normalized operating status characteristics of the power equipment; performing operation record analysis on the plurality of log segments to extract operation record data; Performing a production safety behavior analysis on the operation record data to extract production safety behavior data; Extracting behavior timestamps and business processing personnel data from the production safety behavior data; Perform multi-period production behavior feature analysis based on the behavior timestamp and the business processing personnel data to generate production behavior features for different time periods; Identifying key behavior time nodes for the production behavior characteristics in different time periods; Setting pre- and post-trigger conditions for the key behavior time nodes; Performing up-down operation behavior dependency analysis on the production behavior characteristics in different time periods based on the pre- and post-trigger conditions of the key behavior nodes to obtain production behavior time series dependency data; The production behavior time sequence relies on data mining safety production standard logic; Performing a topological structure association analysis on the production behavior characteristics in different time periods according to the operating state characteristics to extract topological structure data; Based on the safety production standard logic, the topology data is subjected to safety production graph structure evolution to construct a power safety production knowledge graph; Acquire real-time video data of power production behavior monitoring, perform frame-by-frame dynamic optical flow tracking on the video data, and obtain production behavior characteristics of the dynamic production behavior of the operators; Performing dynamic behavior visual recognition on the production behavior characteristics to obtain power production behavior data of the operator; Calculating dynamic behavior duration for the power production behavior data; quantifying the behavior interval of the power production behavior data to obtain a production behavior interval value; Calculating sequential characteristics of operator behavior based on the production behavior interval value; Using the dynamic behavior duration and the behavior sequence characteristics to construct dynamic operation process characteristic data of the operator; Based on the power production safety knowledge graph, a quantitative assessment of production safety risks is performed on the dynamic operation process characteristic data of the operator to generate a quantitative value of production behavior risk; Emergency response decisions are made based on the quantitative value of the production behavior risk to optimize the power production safety knowledge graph.

2. The method according to claim 1, characterized in that The analysis of the time series operation parameter changes and normalized operation evolution of the log data to obtain the normalized operation status characteristics of the power equipment includes: filtering abnormal outlier data points from the log data; If filtering is completed, the log data is segmented into time series windows to obtain multiple log segments; identifying a service device entity from a plurality of the log segments; Performing time-series operation parameter change analysis on the plurality of service device entities to generate operation parameter change data; The operating parameter change data is subjected to normalized operation evolution to obtain normalized operating state characteristics of the power equipment.

3. The method according to any one of claims 1 to 2, characterized in that The step of performing frame-by-frame dynamic optical flow tracking on the video data to obtain production behavior characteristics of the operator's dynamic production behavior includes: performing filtering processing on the video data; If the filtering process is completed, the video data is subjected to visual recognition of the operator, and the image frame where the operator is located is segmented; Performing frame-by-frame dynamic optical flow tracking on the image frame to extract motion vector change data; Dynamic behavior trajectory analysis is performed on the motion vector change data to obtain production behavior characteristics of the operator's dynamic behavior.

4. The method according to any one of claims 1 to 2, characterized in that The performing of a quantitative production safety risk assessment on the dynamic operation process characteristic data of the operator based on the power safety knowledge graph to generate a quantitative production behavior risk value includes: Performing safety production operation rule analysis on the dynamic operation process feature data based on the power safety knowledge graph to extract irregular operation behavior data; Performing a production safety hazard analysis on the operation behavior data to generate operation behavior safety hazard data; Mining the diffusible paths of security risks on the data of potential safety hazards of the operation behaviors to extract the diffusible paths of risks; Perform risk propagation prediction on the risk diffusible path to obtain safety risk propagation prediction data; Conduct a quantitative assessment of production safety risks on the safety risk propagation prediction data to generate a quantitative value of production behavior risk.

5. The method according to any one of claims 1 to 2, characterized in that The emergency response decision-making based on the production behavior risk quantification value to optimize the power production safety knowledge graph includes: Make an emergency response decision based on the production behavior risk quantification value to obtain a risk behavior emergency response strategy; Performing iterative learning on the risk behavior emergency response strategy to obtain an iterative learning representation of the response strategy; Performing multi-scenario migration optimization on the iterative learning representation of the response strategy to obtain a migration-optimized iterative learning representation; The migration optimization iterative learning representation is used to make intelligent response decisions on the electric power production safety knowledge graph to optimize the electric power production safety knowledge graph.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the detection method for safe power production according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting safe power production according to any one of claims 1 to 5 is implemented.

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