Method, device and computer equipment for automatically checking operation information of equipment startup

Abstract

The present application relates to a method, device and computer equipment for automatic verification of operation information of equipment startup. The method includes: obtaining the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system; fusing the heterogeneous data in the dispatching data, operation control data and protection signal data to obtain the equipment operation status model; generating a connection relationship diagram and state change prediction information based on the real-time data and historical data in the dispatching data, operation control data and protection signal data; inputting the connection relationship diagram and state change prediction information into the equipment operation status model to obtain initial operation information verification information; generating adjustment device identification operation information based on the real-time data in the operation control data and protection signal data; determining the operation information automatic verification information based on the initial operation information verification information and the adjustment device identification operation information. The use of this method can improve the efficiency of operation information verification for equipment startup.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for automatically verifying operation information initiated by a device. Background Art

[0002] With the development of computer technology, the operation information verification technology for equipment startup has emerged. This technology refers to checking and verifying various information such as equipment status, operating parameters, safety measures and startup procedures before the equipment is started to ensure that all operations comply with regulations and record relevant information during the startup process to ensure the safety and normal operation of the equipment.

[0003] Traditionally, this verification process is typically achieved through manual inspection and mechanical instrumentation. Operators, following manuals and checklists, itemize the equipment status, operating parameters, and safety measures. They use instruments to measure key parameters such as voltage, current, and temperature to ensure compliance with startup requirements. They also record the inspection results and various startup information. However, verifying equipment startup operational information through manual inspection and mechanical instrumentation can lead to worker fatigue during the extensive verification process, further increasing the risk of accidents and resulting in inefficient verification of equipment startup operational information. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for automatically checking the operation information of device startup, which can improve the efficiency of checking the operation information of device startup, in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for automatically verifying operation information started by a device. The method comprises:

[0006] Obtain dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0007] fusing heterogeneous data among the scheduling data, the operation control data, and the protection signal data to obtain a device operation status model;

[0008] generating a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data;

[0009] Inputting the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0010] Generate adjustment device operation information according to the operation control data and the real-time data in the protection signal data;

[0011] The automatic calibration information of the operation information of the power grid system is determined according to the initial operation information calibration information and the adjustment device operation information.

[0012] In a second aspect, the present application also provides a device for automatically checking the operation information of a device startup. The device includes:

[0013] The power grid data acquisition module is used to obtain the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0014] A state model obtaining module, configured to fuse heterogeneous data in the scheduling data, the operation control data, and the protection signal data to obtain a device operation state model;

[0015] a first data processing module, configured to generate a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data;

[0016] A second data processing module is configured to input the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0017] An operation information generating module, configured to generate adjustment device identification operation information based on the operation control data and the real-time data in the protection signal data;

[0018] The verification information determination module is used to determine the automatic verification information of the operation information of the power grid system based on the initial operation information verification information and the adjustment device operation information.

[0019] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0020] Obtain dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0021] fusing heterogeneous data among the scheduling data, the operation control data, and the protection signal data to obtain a device operation status model;

[0022] generating a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data;

[0023] Inputting the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0024] Generate adjustment device operation information according to the operation control data and the real-time data in the protection signal data;

[0025] The automatic calibration information of the operation information of the power grid system is determined according to the initial operation information calibration information and the adjustment device operation information.

[0026] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0027] Obtain dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0028] fusing heterogeneous data among the scheduling data, the operation control data, and the protection signal data to obtain a device operation status model;

[0029] generating a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data;

[0030] Inputting the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0031] Generate adjustment device operation information according to the operation control data and the real-time data in the protection signal data;

[0032] The automatic calibration information of the operation information of the power grid system is determined according to the initial operation information calibration information and the adjustment device operation information.

[0033] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0034] Obtain dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0035] fusing heterogeneous data among the scheduling data, the operation control data, and the protection signal data to obtain a device operation status model;

[0036] generating a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data;

[0037] Inputting the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0038] Generate adjustment device operation information according to the operation control data and the real-time data in the protection signal data;

[0039] The automatic calibration information of the operation information of the power grid system is determined according to the initial operation information calibration information and the adjustment device operation information.

[0040] The above-mentioned method, device, computer equipment, storage medium and computer program product for automatic verification of operation information started by a device obtains the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system; integrates the heterogeneous data in the dispatching data, operation control data and protection signal data to obtain the device operation status model; generates the connection relationship diagram and state change prediction information corresponding to each device based on the real-time data and historical data in the dispatching data, operation control data and protection signal data; inputs the connection relationship diagram and state change prediction information into the device operation status model to obtain initial operation information verification information; generates adjustment device identification operation information based on the real-time data in the operation control data and protection signal data; and determines the automatic verification information of the operation information of the power grid system based on the initial operation information verification information and the adjustment device identification operation information.

[0041] By acquiring and integrating dispatch data, operational control data, and protection signal data from equipment in the power grid system, establishing a model for equipment operating status, and generating equipment connection diagrams and state change prediction information, this enables accurate monitoring and dynamic prediction of equipment operating status. Verification based on real-time and historical data effectively improves the accuracy and timeliness of operational information, reducing the risk of errors and delays caused by manual verification. Ultimately, through the automatic verification of operational information, intelligent management of power grid system operations is achieved, improving the efficiency of verifying operational information during equipment startup, thereby significantly improving the safety, stability, and efficiency of power grid operations, enabling rapid response to and prevention of potential failures, and ensuring the continuity and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A diagram illustrating an application environment of a method for automatically verifying operation information initiated by a device in one embodiment;

[0043] Figure 2A schematic flow chart of a method for automatically checking operation information started by a device in one embodiment;

[0044] Figure 3 1 is a flow chart of a method for obtaining state change prediction information in one embodiment;

[0045] Figure 4 1 is a flow chart of a method for obtaining state change prediction information in another embodiment;

[0046] Figure 5 Schematic diagram of a flow chart of a method for determining an algorithm result in one embodiment;

[0047] Figure 6 1. A flow chart of a method for obtaining initial operation information verification information in one embodiment;

[0048] Figure 7 A flowchart of a method for obtaining automatic verification information of optimized operation information in one embodiment;

[0049] Figure 8 A structural block diagram of an automatic verification device for operation information started by a device in one embodiment;

[0050] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] The embodiment of the present application provides a method for automatically checking the operation information of a device startup, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system from the terminal 102; integrates the heterogeneous data in the dispatching data, operation control data and protection signal data to obtain the device operation status model; generates the connection relationship diagram and state change prediction information corresponding to each device according to the real-time data and historical data in the dispatching data, operation control data and protection signal data; inputs the connection relationship diagram and state change prediction information into the device operation status model to obtain the initial operation information verification information; generates the adjustment device identification operation information according to the real-time data in the operation control data and protection signal data; determines the automatic verification information of the operation information of the power grid system according to the initial operation information verification information and the adjustment device identification operation information. Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0053] In one embodiment, Figure 2 As shown, a method for automatically checking the operation information of a device startup is provided, and the method is applied to Figure 1 The following steps are used as an example to illustrate the server in the example:

[0054] Step 202: Obtain dispatching data, operation control data, and protection signal data corresponding to each device in the power grid system.

[0055] Dispatch data refers to the data generated and used by various devices (such as generators, transformers, and transmission lines) in the power system during operation and management. This data includes equipment operating status, load conditions, power generation, fault records, and maintenance plans. It is typically monitored and recorded in real time by a power dispatch center to optimize grid operation, ensure the reliability and stability of power supply, and conduct fault diagnosis and preventive maintenance.

[0056] Among them, operation control data can be data information used to control and operate various equipment (such as generators, transformers, switches, circuit breakers, etc.) in the power system. This data includes switch status, control commands, set parameters, automation control instructions, etc.

[0057] Protection signal data can be used to monitor and protect various equipment in power systems, such as generators, transformers, and transmission lines. This data includes real-time monitoring values ​​such as current, voltage, power, and temperature, as well as fault detection signals, alarm information, and protection device action records. When power equipment experiences an anomaly or fault, the protection signal data triggers the protection device to automatically take action, such as disconnecting the circuit or isolating the faulty area, to prevent equipment damage and further expansion of the fault.

[0058] Specifically, dispatch data, operational control data, and protection signal data are collected from sensors and monitoring systems on various devices in the power grid system. This data includes equipment operating status, dispatch plans, operational instructions, and protection device status and alarm information. Real-time data is transmitted to the data center via data acquisition terminals and stored and processed in conjunction with historical data. In the data center, data fusion technology is used to uniformly process and analyze this heterogeneous data to ensure accuracy and consistency.

[0059] Step 204 : The heterogeneous data in the scheduling data, the operation control data, and the protection signal data are integrated to obtain a device operation status model.

[0060] The equipment operating status model can be constructed using mathematical, physical, and computational methods to describe and simulate the state and behavior of the equipment under different operating conditions. This model integrates the equipment's operating parameters, historical data, and environmental factors. Through simulation and analysis, it can predict equipment performance, diagnose faults, and optimize operation and maintenance strategies.

[0061] Specifically, dispatch data, operational control data, and protection signal data are preprocessed, including data cleaning, format conversion, and time synchronization, to ensure data consistency and integrity. Next, data fusion algorithms are used to integrate this heterogeneous data and extract key characteristics and parameters of device operation. This fused data is trained and modeled using machine learning or physics-based models to generate a device operating status model that reflects the actual operating status of the device. This device operating status model accurately describes the performance and behavior of the device under different operating conditions.

[0062] Step 206: Generate a connection relationship diagram corresponding to each device and state change prediction information based on the real-time data and historical data in the scheduling data, operation control data, and protection signal data.

[0063] A connection diagram is a graphical representation of the connections and relationships between various elements in a system. It illustrates the interconnections and dependencies between different components, nodes, or devices, visually depicting the system's structure and layout through nodes and connecting lines. Connection diagrams are widely used in fields such as network architecture, power systems, and database design, helping to understand the overall system architecture, analyze complex relationships, and optimize system design.

[0064] State change prediction information can be used to predict the likely future state changes of equipment, systems, or processes based on current state data and historical trends. By analyzing sensor data, operational records, environmental conditions, and using predictive models and algorithms, potential issues can be identified in advance, performance changes can be anticipated, maintenance plans can be scheduled, and operational strategies can be adjusted.

[0065] Specifically, real-time and historical data are extracted from dispatching, operational control, and protection signal data, analyzed and processed to determine the corresponding real-time and historical status data. Next, a graph algorithm is used to construct a device connectivity diagram, clarifying the interrelationships and impact paths between devices. Based on this, time series analysis and machine learning algorithms are applied to integrate real-time and historical status data to input the device's operating status into a model for modeling and training, predicting device state changes under different operating conditions. Ultimately, a state change forecast is generated for each device, demonstrating future operational trends and potential failures.

[0066] Step 208: Input the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information.

[0067] Initial operational information verification can be used to check and verify the basic information entered during system or device operation. The verification process ensures that the set parameters, configurations, and operational procedures are accurate and meet design specifications and operational requirements.

[0068] Specifically, the generated real-time data and historical data of the device connection diagram and state change prediction information are input into the device operation status model to provide a comprehensive operation status context. The device operation status model combines the real-time data and historical data of each different point in the connection diagram, and uses the prediction information to evaluate the current and future status of the device. By comparing the real-time data and the prediction data, the device operation status model can identify potential anomalies and deviations and generate initial operation information verification information. The initial operation information verification information includes the rationality check of the current operation and the future operation suggestions.

[0069] Step 210: Generate adjustment device operation information based on the real-time data in the operation control data and the protection signal data.

[0070] Among them, the adjustment device operation information can be the data information input into the system or device during actual operation, which is used to adjust the initial operation information verification information to better adapt to the current application scenario.

[0071] Specifically, real-time data is extracted from the operation control data and protection signal data for analysis and processing to identify the current operating status and operating behavior of the equipment. The dynamic analysis method of real-time data is combined with the operation specifications and protection logic of the equipment in the operation control data to generate the adjustment equipment identification operation information. This adjustment equipment identification operation information includes the actual current operating status of the equipment, operating parameters and protection action records.

[0072] Step 212, determining automatic calibration information of the operation information of the power grid system based on the initial operation information calibration information and the adjustment device operation information.

[0073] Among them, the automatic verification information of the operation information can be the verification information obtained by adjusting the initial operation information verification information using the adjustment device operation information, and is used to adjust each relay in the power grid system in actual applications.

[0074] Specifically, the initial operational verification data is compared with the adjusted operational data to identify discrepancies and potential operational anomalies. A verification algorithm is then applied to analyze these discrepancies and potential operational anomalies, assessing the accuracy and consistency of device operation. Further analysis of the discrepancies determines whether any operational strategies or parameters require adjustment. Ultimately, an automated verification of the power grid system's operational information is generated, providing detailed verification results and recommendations.

[0075] In the above-mentioned method for automatic verification of operation information started by a device, the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system are obtained; the heterogeneous data in the dispatching data, operation control data and protection signal data are integrated to obtain the device operation status model; based on the real-time data and historical data in the dispatching data, operation control data and protection signal data, the connection relationship diagram and state change prediction information corresponding to each device are generated; the connection relationship diagram and state change prediction information are input into the device operation status model to obtain initial operation information verification information; based on the real-time data in the operation control data and protection signal data, the adjustment device identification operation information is generated; based on the initial operation information verification information and the adjustment device identification operation information, the automatic verification information of the operation information of the power grid system is determined.

[0076] By acquiring and integrating dispatch data, operational control data, and protection signal data from equipment in the power grid system, establishing a model for equipment operating status, and generating equipment connection diagrams and state change prediction information, this enables accurate monitoring and dynamic prediction of equipment operating status. Verification based on real-time and historical data effectively improves the accuracy and timeliness of operational information, reducing the risk of errors and delays caused by manual verification. Ultimately, through the automatic verification of operational information, intelligent management of power grid system operations is achieved, improving the efficiency of verifying operational information during equipment startup, thereby significantly improving the safety, stability, and efficiency of power grid operations, enabling rapid response to and prevention of potential failures, and ensuring the continuity and reliability of power supply.

[0077] In one embodiment, Figure 3 As shown, based on the real-time and historical data in the dispatch data, operation control data, and protection signal data, a connection relationship diagram corresponding to each device and state change prediction information are generated, including:

[0078] Step 302: Generate an initial device connection relationship diagram based on the real-time data and historical data in the scheduling data.

[0079] The initial device connection relationship diagram may be an unverified connection relationship diagram.

[0080] Specifically, real-time and historical data are extracted from the scheduling data and preprocessed to ensure data integrity and consistency. Graph database technology is also used to model the interaction information and dependencies between devices, constructing a meaningless device connection graph. By analyzing device communication and operation records from the real-time and historical data in the scheduling data, connections and interactions between devices are identified. The attributes of nodes and edges in the meaningless device connection graph for both real-time and historical data are determined, generating an initial, dynamically updated device connection graph.

[0081] Step 304: Update the connection relationship of the initial device connection relationship diagram based on the real-time data in the scheduling data to obtain a connection relationship diagram corresponding to the device.

[0082] Specifically, the system continuously extracts real-time data from the scheduling data to dynamically update the initial device connection diagram, analyze new scheduling information, and identify new or changed device interactions and dependencies. Through graph database technology and incremental update algorithms, the connection relationships between devices are adjusted in real time, adding new connections, modifying the properties of existing connections, or deleting invalid connections. Ultimately, a device connection diagram that accurately reflects the current device status and relationships is generated.

[0083] Step 306 : Input the real-time data and historical data of the dispatching data, operation control data and protection signal data into the real-time prediction model of the equipment status of the power grid system to obtain status change prediction information.

[0084] A real-time equipment status prediction model is a mathematical and computational model used to monitor and predict the real-time operating status of grid equipment (such as generators, transformers, and transmission lines). By integrating real-time sensor data, historical operating data, and environmental conditions, the model can predict the future state of the equipment and identify potential failures and performance degradation.

[0085] Specifically, real-time and historical data are collected from dispatch data, operational control data, and protection signal data and fed into the real-time prediction model for equipment status in the power grid system. This model uses machine learning and time series analysis algorithms to train the equipment's current and historical operating modes and dynamically predicts them based on these data. The model generates predictions of equipment status changes, including possible future trends and potential fault warnings.

[0086] In this embodiment, by generating and dynamically updating a device connection diagram, inputting real-time and historical data into a real-time device status prediction model, and obtaining state change prediction information, this method significantly improves the monitoring and management capabilities of the power grid system. Specifically, the initial device connection diagram provides a clear global view of the interactions between devices, and dynamic updates ensure that the diagram always reflects the latest system status. Combined with the real-time device status prediction model, it can accurately predict device status changes and identify potential risks and failures in advance. This integrated approach not only improves the operational reliability and stability of the power grid system, but also enhances its response speed and maintenance efficiency, providing strong support for intelligent power grid management.

[0087] In one embodiment, Figure 4 As shown, the real-time data and historical data of the dispatching data, operation control data and protection signal data are input into the real-time prediction model of the equipment status of the power grid system to obtain the state change prediction information, including:

[0088] Step 402: Determine a real-time data processing framework for the real-time data and a historical data processing framework for the historical data based on the real-time data and the historical data in the scheduling data, the operation control data, and the protection signal data.

[0089] Among them, the real-time data processing framework can be a platform or system for processing and analyzing real-time generated data. It can quickly receive, process and analyze data streams to enable timely responses and decisions.

[0090] The historical data processing framework can be a platform or system for storing, processing, and analyzing collected historical data. It can perform batch processing and complex analysis on large amounts of past data, helping users discover long-term trends, patterns, and anomalies.

[0091] Specifically, an initial real-time data processing framework was designed. This framework uses streaming processing technology to receive and process real-time data from dispatching, operational control, and protection signaling. This framework adaptively and rapidly responds to system changes through modeling, performing real-time analysis and decision-making. Similarly, an initial historical data processing framework was designed. This framework uses batch processing technology to regularly aggregate and store real-time data from dispatching, operational control, and protection signaling. This framework then adaptively performs in-depth analysis and trend prediction modeling, resulting in a historical data processing framework.

[0092] Step 404 : Optimize the real-time data in the scheduling data, operation control data, and protection signal data according to the real-time data processing framework to obtain optimized real-time data.

[0093] Specifically, a real-time data processing framework is used to preprocess the real-time data from dispatching, operational control, and protection signaling, including data cleaning, deduplication, and format conversion. This data is then received and analyzed in real time using streaming technology. Data filtering and aggregation algorithms are used to remove noise and redundant information and extract key features and useful information. Optimization algorithms are then applied to further optimize the processed data, generating optimized real-time data.

[0094] Step 406 : Optimize the historical data in the dispatching data, operation control data, and protection signal data according to the historical data processing framework to obtain optimized historical data.

[0095] Specifically, a historical data processing framework is used to preprocess historical data from dispatching, operational control, and protection signaling. This includes steps such as data cleaning, deduplication, format conversion, and time alignment. Batch processing techniques are then used to aggregate and store large amounts of historical data. Data mining and analysis algorithms are employed to remove redundant information and outliers, extracting key features and trend information. Optimization algorithms are then applied to further optimize the processed historical data to generate optimized historical data.

[0096] Step 408 : modifying the parameters of the real-time prediction model for the device status of the power grid system according to the optimized real-time data and the optimized historical data, so that the real-time prediction model for the device status outputs status change prediction information.

[0097] Specifically, the real-time device status prediction model is fed with optimized real-time data and historical data. Based on the model's initial output data, the model's parameters are adaptively adjusted for at least one round. During this process, an optimization algorithm (such as gradient descent) is employed to continuously adjust the model parameters during training to minimize prediction error. Through multiple rounds of iteration and cross-validation, the optimal configuration of the model parameters is ensured, ultimately enabling the updated model to accurately output predictions of state changes.

[0098] In this embodiment, by constructing a real-time data processing framework and a historical data processing framework, dispatch data, operational control data, and protection signal data are optimized and processed, and the parameters of the real-time prediction model for the power grid system's equipment status are adjusted accordingly. This method significantly improves the data processing and prediction capabilities of the power grid system. Specifically, the optimized real-time and historical data ensure high-quality data input, enabling the prediction model to more accurately reflect the operating status and changing trends of the equipment. By dynamically adjusting the model parameters, the adaptability and accuracy of the model are enhanced. This integrated optimization process not only improves the operating efficiency and reliability of the power grid system, but also enhances fault prediction and risk management capabilities.

[0099] In one embodiment, Figure 5 As shown, after the step of modifying the parameters of the real-time prediction model of the device status of the power grid system according to the optimized real-time data and the optimized historical data so that the real-time prediction model of the device status outputs the state change prediction information, the method further includes:

[0100] Step 502: If the state change prediction information and / or the connection relationship diagram fails to meet the preset requirements, the connection relationship diagram is adjusted according to the state change prediction information to obtain an adjusted relationship diagram.

[0101] Specifically, the system compares the state change prediction information and the connection diagram with the pre-set requirements to assess their accuracy and reliability. If the pre-set requirements are not met, the system analyzes anomalies and deviations from the pre-set requirements in the change prediction information and / or the connection diagram to identify factors that may affect device connectivity. Based on these analysis results, the structure and properties of the connection diagram are adjusted, redefining the connections and interactions between devices to produce a revised connection diagram.

[0102] Step 504, adjust the real-time data processing framework and the historical data processing framework according to the adjustment relationship diagram, return to execute the step of optimizing the real-time data in the scheduling data, operation control data and protection signal data according to the real-time data processing framework, and obtain the step of optimizing the real-time data until the state change prediction information and the connection relationship diagram can meet the preset requirements.

[0103] Specifically, based on the adjusted connection diagram, the parameters and processing flows of the real-time data processing framework and the historical data processing framework are reassessed and adjusted. The adjusted two data processing frameworks are then applied to optimize the real-time and historical data for dispatching, operational control, and protection signaling data, generating new optimized real-time and historical data. This optimized data is then fed back into the real-time device status prediction model, and the model parameters are iteratively adjusted to generate updated state change prediction information. This process is repeated, with continuous adjustment and optimization, until both the state change prediction information and the connection diagram meet the preset requirements.

[0104] In this embodiment, by iteratively adjusting the connection diagram and data processing framework to ensure that the state change prediction information and the connection diagram meet preset requirements, this method significantly improves the adaptability and accuracy of the power grid system. Specifically, when the state change prediction information or the connection diagram fails to meet the preset requirements, the connection diagram is promptly adjusted based on the prediction information, and the real-time and historical data processing frameworks are optimized accordingly, making data processing more accurate and efficient. This process is repeated until both the prediction information and the connection diagram meet the expected standards. This method not only improves the system's dynamic adjustment capabilities and fault prediction accuracy, but also optimizes the data processing process and model parameters, enhancing the overall operational efficiency and security of the power grid system.

[0105] In one embodiment, Figure 6 As shown, the connection relationship diagram and state change prediction information are input into the equipment operation state model to obtain initial operation information verification information, including:

[0106] Step 602: Determine the device operation curve based on the connection relationship diagram and the state change prediction information.

[0107] An equipment operating curve is a graphical representation of equipment performance and behavior under different operating conditions. It typically displays, as a curve, the changing trends of equipment operating parameters (such as pressure, temperature, and power) over time or operating variables (such as load and speed). By analyzing equipment operating curves, equipment performance can be evaluated, optimal operating conditions identified, potential problems diagnosed, and operational strategies optimized.

[0108] Specifically, the connection diagram and state change prediction information are comprehensively analyzed to identify interdependencies between devices and state change trends. Next, operating parameters and status data of key devices are extracted based on these interdependencies and state change trends. Using time series analysis and regression analysis, state change curves for each device under different operating conditions are plotted. Through data fitting and model optimization, operating curves reflecting the operating patterns and performance characteristics of the devices are generated as device operation curves.

[0109] Step 604: Determine a theoretical operation curve based on the real-time data and historical data in the dispatching data, the operation control data, and the protection signal data.

[0110] The theoretical operating curve is an ideal operating curve derived from the device's design parameters and theoretical calculations. It represents the performance and behavior that the device should achieve under different operating conditions. It illustrates the changing trends of various operating parameters (such as pressure, temperature, and power) under optimal operating conditions. The theoretical operating curve is used to compare with the actual operating curve to assess device efficiency, diagnose deviations, and optimize operations.

[0111] Specifically, real-time and historical data extracted from dispatching, operational control, and protection signal data are analyzed and modeled using data analysis and modeling techniques to determine the initial theoretical operating curves for each device under different operating conditions. Using time series analysis, regression analysis, and machine learning algorithms, the device's historical operating data is analyzed, key parameters and features are extracted, and the initial theoretical operating curves are modified to generate theoretical operating curves that reflect the device's ideal operating state.

[0112] Step 606 : Compare the equipment operation curve with the theoretical operation curve to obtain initial operation information verification information.

[0113] Specifically, the device operating curve is aligned and normalized with the theoretical operating curve to ensure comparability across the same time and parameter dimensions. Next, error analysis and deviation detection methods are used to compare the two curves, identifying discrepancies between actual and theoretical operation. The accuracy and consistency of device operation are assessed through calculation of deviation values, error ranges, and trend analysis. Finally, initial operational verification information is generated.

[0114] In this embodiment, initial operation information verification information is generated by determining the device operation curve based on the connection relationship diagram and state change prediction information, and comparing it with the theoretical operation curve determined based on real-time and historical data from the dispatch data, operation control data, and protection signal data. This method significantly improves the operational accuracy and reliability of the power grid system. Specifically, the device operation curve reflects the actual operating conditions, while the theoretical operation curve represents the ideal operating state. By comparing the two, operational deviations and potential problems can be accurately identified. The generated initial operation information verification information provides a scientific basis for adjusting and optimizing operational strategies, reducing the occurrence of failures and improving the stability and safety of the system.

[0115] In one embodiment, Figure 7 As shown, the method further includes:

[0116] Step 702: Obtain the comprehensive topology structure, device status data, and environmental data corresponding to each device in the power grid system.

[0117] A comprehensive topology is a diagram of the overall layout and configuration of a system or network, taking into account the various nodes and connections. It depicts the interconnections and relationships between components, devices, or nodes in the system, including both physical and logical architecture.

[0118] Equipment status data can include various data and information reflecting the current operating status of the equipment, including but not limited to parameters such as temperature, pressure, current, voltage, speed, load, and vibration. This data is collected in real time through sensors and monitoring systems, providing key information such as the equipment's operating performance, health status, and operational status. This data is used to monitor, diagnose, maintain, and optimize equipment operation, ensuring its safe, reliable, and efficient operation.

[0119] Environmental data can be any type of information describing and reflecting the specific environmental conditions and status of the power grid system, including temperature, humidity, air pressure, wind speed, rainfall, air quality, noise levels, etc. This data is collected in real time through sensors and monitoring systems, providing detailed records and analysis of environmental changes and conditions.

[0120] Specifically, comprehensive topological information is collected for each device in the power grid system, including its physical location, connectivity, and network configuration. Next, sensors and monitoring systems are used to acquire device status data, such as operating parameters, fault records, and performance indicators. Simultaneously, relevant environmental data, including temperature, humidity, air pressure, and other environmental factors, is extracted from the environmental monitoring system. This data is integrated into a data center via data acquisition terminals and network transmission for unified storage and processing. Ultimately, a comprehensive database encompassing device topology, status, and environmental data is formed.

[0121] Step 704 : Input the comprehensive topology structure, device status data, and environmental data into the potential risk prediction model to obtain potential risk prediction data.

[0122] Potential risk prediction data can be information generated by analyzing current data and historical trends to predict potential risks and issues in the power grid system. This includes estimates of potential risks such as equipment failures and natural disasters. This prediction data is generated through models and algorithms to help decision makers identify and address potential risks in advance.

[0123] Specifically, the comprehensive topology structure, equipment status data and environmental data are input into the potential risk prediction model. The potential risk prediction model uses machine learning and big data analysis technology to comprehensively evaluate and analyze the operating status and environmental conditions of the equipment based on the comprehensive topology structure, identify key factors and risk patterns that may cause equipment failure or abnormality, and generate potential risk prediction data, where the potential risk prediction data includes the equipment's risk level, failure probability and early warning information.

[0124] Step 706: Optimize the automatic verification information of the operation information using the potential risk prediction data to obtain optimized automatic verification information of the operation information.

[0125] The optimized operation information automatic verification information may be optimized operation information automatic verification information, which has higher efficiency and accuracy.

[0126] Specifically, potential risk prediction data is compared and analyzed with initial operational information verification data to identify potential risks and operational deviations. Based on these potential risks and operational deviations, the verification algorithm is adjusted and optimized based on the potential risk prediction data, focusing on high-risk equipment and areas and strengthening the verification of key operating parameters. Through iterative optimization and recalibration of operational information, optimized operational information automatic verification information is generated, which includes detailed operational adjustment suggestions and risk warnings.

[0127] In this embodiment, by obtaining the comprehensive topological structure, device status data and environmental data of each device in the power grid system and inputting them into the potential risk prediction model to generate potential risk prediction data, and then using these prediction data to optimize the automatic verification information of the operation information, the optimized automatic verification information of the operation information is obtained. This method significantly improves the prediction and prevention capabilities of the power grid system. Specifically, the comprehensive topological structure makes the overall layout and connection relationship of the power grid clearer, and the device status data and environmental data provide real-time operation and environmental information. Through the potential risk prediction model, risks can be identified and assessed in advance, and the automatic verification information of the operation information can be optimized to ensure that the verification results are more accurate and timely. This not only reduces the probability of failure, but also optimizes the operation strategy, improves the overall operational efficiency and reliability of the power grid system, and ensures the stability and continuity of the power supply.

[0128] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0129] Based on the same inventive concept, the present application also provides an apparatus for automatically verifying device-initiated operation information, for implementing the aforementioned method for automatically verifying device-initiated operation information. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the apparatus for automatically verifying device-initiated operation information provided below can be found in the aforementioned definition of the method for automatically verifying device-initiated operation information, and will not be further elaborated here.

[0130] In one embodiment, Figure 8 As shown, a device for automatically verifying operation information of equipment startup is provided, comprising: a power grid data acquisition module 802, a state model acquisition module 804, a first data processing module 806, a second data processing module 808, an operation information generation module 810 and a verification information determination module 812, wherein:

[0131] The power grid data acquisition module 802 is used to obtain the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system;

[0132] The state model obtaining module 804 is used to integrate heterogeneous data in the scheduling data, operation control data and protection signal data to obtain the equipment operation state model;

[0133] The first data processing module 806 is used to generate a connection relationship diagram and state change prediction information corresponding to each device based on the real-time data and historical data in the scheduling data, operation control data and protection signal data;

[0134] The second data processing module 808 is used to input the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information;

[0135] The operation information generating module 810 is used to generate the adjustment device identification operation information according to the real-time data in the operation control data and the protection signal data;

[0136] The calibration information determination module 812 is used to determine the automatic calibration information of the operation information of the power grid system based on the calibration information of the initial operation information and the adjustment device operation information.

[0137] In one embodiment, the first data processing module 806 is also used to generate an initial device connection relationship diagram based on the real-time data and historical data in the scheduling data; update the connection relationship of the initial device connection relationship diagram based on the real-time data in the scheduling data to obtain a connection relationship diagram corresponding to the device; input the real-time data and historical data in the scheduling data, operation control data and protection signal data into the real-time prediction model of the device status of the power grid system to obtain state change prediction information.

[0138] In one embodiment, the first data processing module 806 is also used to determine a real-time data processing framework for real-time data and a historical data processing framework for historical data based on the real-time data and historical data in the dispatching data, operation control data and protection signal data; optimize the real-time data in the dispatching data, operation control data and protection signal data according to the real-time data processing framework to obtain optimized real-time data; optimize the historical data in the dispatching data, operation control data and protection signal data according to the historical data processing framework to obtain optimized historical data; modify the parameters of the real-time prediction model of the equipment status of the power grid system according to the optimized real-time data and the optimized historical data, so that the real-time prediction model of the equipment status outputs state change prediction information.

[0139] In one embodiment, the first data processing module 806 is also used to adjust the connection relationship diagram according to the state change prediction information to obtain an adjusted relationship diagram if the state change prediction information and / or the connection relationship diagram fail to meet the preset requirements; adjust the real-time data processing framework and the historical data processing framework according to the adjusted relationship diagram, and return to execute the step of optimizing the real-time data in the scheduling data, operation control data and protection signal data according to the real-time data processing framework to obtain optimized real-time data, until the state change prediction information and the connection relationship diagram can meet the preset requirements.

[0140] In one embodiment, the second data processing module 808 is also used to determine the equipment operation curve based on the connection relationship diagram and state change prediction information; determine the theoretical operation curve based on the real-time data and historical data in the scheduling data, operation control data and protection signal data; and compare the equipment operation curve with the theoretical operation curve to obtain initial operation information verification information.

[0141] In one embodiment, the verification information determination module 812 is also used to obtain the comprehensive topology structure, device status data and environmental data corresponding to each device in the power grid system; input the comprehensive topology structure, device status data and environmental data into the potential risk prediction model to obtain potential risk prediction data; use the potential risk prediction data to optimize the automatic verification information of the operation information to obtain the optimized automatic verification information of the operation information.

[0142] Each module in the aforementioned device-activated automatic verification device for operation information may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0143] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for automatically verifying the operation information of the device startup.

[0144] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0146] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0147] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of each of the above-described method embodiments.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0149] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0150] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for automatically checking operation information of equipment startup, characterized in that: The method comprises: Obtain dispatching data, operation control data and protection signal data corresponding to each device in the power grid system; fusing heterogeneous data among the scheduling data, the operation control data, and the protection signal data to obtain a device operation status model; generating a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data; Inputting the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information, including: determining an equipment operation curve based on the connection relationship diagram and the state change prediction information; determining a theoretical operation curve based on real-time data and historical data in the scheduling data, the operation control data, and the protection signal data; and comparing the equipment operation curve with the theoretical operation curve to obtain the initial operation information verification information; generating adjustment device operation information according to the operation control data and the real-time data in the protection signal data; Automatic operation information verification information of the power grid system is determined according to the initial operation information verification information and the adjustment device operation information.

2. The method according to claim 1, characterized in that Generating a connection relationship diagram and state change prediction information corresponding to each of the devices according to the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data includes: Generate an initial device connection relationship diagram based on the real-time data and historical data in the scheduling data; updating the connection relationship of the initial device connection relationship diagram according to the real-time data in the scheduling data to obtain a connection relationship diagram corresponding to the device; The real-time data and historical data among the dispatching data, the operation control data and the protection signal data are input into the real-time prediction model of the equipment status of the power grid system to obtain the status change prediction information.

3. The method according to claim 2, characterized in that The step of inputting the real-time data and historical data among the dispatching data, the operation control data, and the protection signal data into the real-time prediction model of the equipment status of the power grid system to obtain the status change prediction information includes: Determining a real-time data processing framework for the real-time data and a historical data processing framework for the historical data based on the real-time data and the historical data in the scheduling data, the operation control data, and the protection signal data; According to the real-time data processing framework, optimizing the real-time data in the scheduling data, the operation control data and the protection signal data to obtain optimized real-time data; Optimizing the historical data in the dispatching data, the operation control data, and the protection signal data according to the historical data processing framework to obtain optimized historical data; According to the optimized real-time data and the optimized historical data, the parameters of the real-time prediction model of the equipment status of the power grid system are modified so that the real-time prediction model of the equipment status outputs the state change prediction information.

4. The method according to claim 3, characterized in that After the step of modifying the parameters of the real-time prediction model for the device state of the power grid system based on the optimized real-time data and the optimized historical data so that the real-time prediction model for the device state outputs the state change prediction information, the method further includes: If the state change prediction information and / or the connection relationship diagram fails to meet the preset requirements, adjusting the connection relationship diagram according to the state change prediction information to obtain an adjusted relationship diagram; According to the adjustment relationship diagram, the real-time data processing framework and the historical data processing framework are adjusted, and the step of optimizing the real-time data in the scheduling data, the operation control data and the protection signal data according to the real-time data processing framework is returned to be executed, so as to obtain the step of optimizing the real-time data until the state change prediction information and the connection relationship diagram can meet the preset requirements.

5. The method according to claim 1, wherein The method further comprises: Obtaining the comprehensive topology structure, device status data and environmental data corresponding to each of the devices in the power grid system; Inputting the comprehensive topology structure, the device status data, and the environmental data into a potential risk prediction model to obtain potential risk prediction data; The automatic verification information of the operation information is optimized using the potential risk prediction data to obtain optimized automatic verification information of the operation information.

6. An automatic verification device for operation information of equipment startup, characterized in that: The device comprises: The power grid data acquisition module is used to obtain the dispatching data, operation control data and protection signal data corresponding to each device in the power grid system; A state model obtaining module, configured to fuse heterogeneous data in the scheduling data, the operation control data, and the protection signal data to obtain a device operation state model; a first data processing module, configured to generate a connection relationship diagram and state change prediction information corresponding to each of the devices based on the real-time data and historical data in the scheduling data, the operation control data, and the protection signal data; A second data processing module is configured to input the connection relationship diagram and the state change prediction information into the equipment operation state model to obtain initial operation information verification information, including: determining an equipment operation curve based on the connection relationship diagram and the state change prediction information; determining a theoretical operation curve based on real-time data and historical data in the scheduling data, the operation control data, and the protection signal data; and comparing the equipment operation curve with the theoretical operation curve to obtain the initial operation information verification information; an operation information generating module, configured to generate adjustment device operation information according to the operation control data and the real-time data in the protection signal data; The verification information determination module is used to determine the automatic verification information of the operation information of the power grid system according to the initial operation information verification information and the adjustment device operation information.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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