Automatic Verification Method and Device for Start-up Operation Status of Power Grid Equipment

Abstract

The application relates to a method and device for automatically checking the running state of power grid equipment startup. The method comprises the following steps: acquiring real-time running data corresponding to each power grid equipment in a power grid system; the real-time running data is continuously acquired through a fixed cycle; a real-time analysis model of the power grid equipment is self-optimized according to the real-time running data, and prediction equipment state checking information and real-time state analysis data are obtained; the real-time state analysis data is input into a real-time equipment state checking model to obtain real-time equipment state checking information; and the prediction equipment state checking information and the real-time equipment state checking information are integrally processed to obtain current running state checking information corresponding to each power grid equipment. The method can effectively improve the automatic checking efficiency of the power grid equipment startup, improve the safety and stability of the power grid operation, optimize the operation efficiency of the power grid, and ensure the continuity and reliability of the power supply.

Description

Technical Field

[0001] This 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 the operating status of power grid equipment during startup. Background Technology

[0002] With the development of power grid technology, operational status verification technology for power grid equipment startup has emerged. This technology refers to ensuring that power grid equipment (such as transformers and circuit breakers) can operate safely and reliably during startup in a power system. This process involves checking various parameters, functions, and status of the equipment to confirm that it meets the predetermined startup conditions and safety specifications, thereby avoiding system instability or safety accidents caused by equipment failure or abnormal parameters.

[0003] In traditional technologies, the operational status verification of power grid equipment startup is typically conducted through manual inspection, simulation testing, parameter verification, document review, and functional testing. Technicians manually inspect the mechanical and electrical condition of the equipment on-site, use testing equipment to simulate startup, read key parameters, review maintenance records and startup plans, and test the equipment's functions one by one. These traditional methods usually require a significant investment of manpower and time, and the verification results are influenced by the experience level of maintenance personnel, resulting in low efficiency for automated operational verification of power grid equipment startup. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automatically verifying the operating status of power grid equipment startup, which can improve the efficiency of automatic verification of the operation of power grid equipment startup.

[0005] Firstly, this application provides a method for automatically verifying the operating status of power grid equipment during startup. The method includes:

[0006] The system acquires real-time operating data for each power grid device in the power grid system; the real-time operating data is continuously acquired at fixed intervals.

[0007] The real-time analysis model of power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data.

[0008] The real-time status analysis data is input into the real-time device status verification model to obtain real-time device status verification information.

[0009] The predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information corresponding to each of the power grid devices.

[0010] Secondly, this application also provides an automatic verification device for the operating status of power grid equipment during startup. The device includes:

[0011] The operation data acquisition module is used to acquire real-time operation data corresponding to each power grid device in the power grid system; the real-time operation data is continuously acquired through fixed-period acquisition.

[0012] The first data analysis module is used for the real-time analysis model of power grid equipment to perform self-learning optimization based on the real-time operating data, and to obtain predicted equipment status verification information and real-time status analysis data.

[0013] The second data analysis module is used to input the real-time status analysis data into the real-time device status verification model to obtain real-time device status verification information.

[0014] The status information acquisition module is used to integrate the predicted device status verification information and the real-time device status verification information to obtain the current operating status verification information corresponding to each of the power grid devices.

[0015] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0016] The system acquires real-time operating data for each power grid device in the power grid system; the real-time operating data is continuously acquired at fixed intervals.

[0017] The real-time analysis model of power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data.

[0018] The real-time status analysis data is input into the real-time device status verification model to obtain real-time device status verification information.

[0019] The predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information corresponding to each of the power grid devices.

[0020] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0021] The system acquires real-time operating data for each power grid device in the power grid system; the real-time operating data is continuously acquired at fixed intervals.

[0022] The real-time analysis model of power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data.

[0023] The real-time status analysis data is input into the real-time device status verification model to obtain real-time device status verification information.

[0024] The predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information corresponding to each of the power grid devices.

[0025] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0026] The system acquires real-time operating data for each power grid device in the power grid system; the real-time operating data is continuously acquired at fixed intervals.

[0027] The real-time analysis model of power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data.

[0028] The real-time status analysis data is input into the real-time device status verification model to obtain real-time device status verification information.

[0029] The predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information corresponding to each of the power grid devices.

[0030] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for automatically verifying the operating status of power grid equipment startup involves acquiring real-time operating data corresponding to each power grid device in the power grid system. This real-time operating data is continuously acquired at fixed intervals. A real-time analysis model for the power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data. The real-time status analysis data is input into the real-time equipment status verification model to obtain real-time equipment status verification information. Finally, the predicted equipment status verification information and the real-time equipment status verification information are integrated and processed to obtain the current operating status verification information corresponding to each power grid device.

[0031] By continuously acquiring real-time operational data at fixed intervals and utilizing a self-learning optimized real-time analysis model for power grid equipment, the operating status of the equipment is verified and analyzed, enabling timely detection of anomalies and potential problems in equipment operation. This method integrates predicted equipment status verification information with real-time equipment status verification information, improving not only the accuracy and reliability of power grid equipment operating status monitoring but also enabling proactive maintenance and management of equipment, reducing equipment failure rates and downtime. Ultimately, this integrated status monitoring and prediction scheme effectively improves the efficiency of automatic verification of power grid equipment startup, enhances the safety and stability of power grid operation, optimizes power grid operational efficiency, ensures the continuity and reliability of power supply, and provides strong support for the efficient management and sustainable development of smart grids. Attached Figure Description

[0032] Figure 1 This is an application environment diagram of an automatic verification method for the starting operation status of power grid equipment in one embodiment;

[0033] Figure 2 This is a flowchart illustrating an automatic verification method for the operating status of power grid equipment during startup, as shown in one embodiment.

[0034] Figure 3 This is a flowchart illustrating a method for obtaining current running status verification information in one embodiment;

[0035] Figure 4 This is a flowchart illustrating the method for obtaining current running status verification information in another embodiment;

[0036] Figure 5 This is a flowchart illustrating the method for obtaining current running status verification information in yet another embodiment;

[0037] Figure 6 This is a flowchart illustrating a method for obtaining data from device calculations in one embodiment;

[0038] Figure 7 This is a flowchart illustrating the method for obtaining data from device calculations in another embodiment;

[0039] Figure 8 This is a structural block diagram of an automatic verification device for the operating status of a power grid device in one embodiment;

[0040] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] This application provides an embodiment of an automatic verification method for the operating status of power grid equipment during startup, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 obtains real-time operating data corresponding to each power grid device in the power grid system from terminal 102; the real-time operating data is continuously obtained through fixed-period acquisition; the real-time analysis model of the power grid device performs self-learning optimization based on the real-time operating data to obtain predicted device status verification information and real-time status analysis data; the real-time status analysis data is input into the real-time device status verification model to obtain real-time device status verification information; the predicted device status verification information and the real-time device status verification information are integrated and processed to obtain the current operating status verification information corresponding to each power grid device. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0043] In one embodiment, such as Figure 2 As shown, an automatic verification method for the operating status of power grid equipment during startup is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0044] Step 202: Obtain real-time operating data for each power grid device in the power grid system.

[0045] Real-time operational data can be various instantaneous data generated by different devices in the power grid (such as transformers, transmission lines, switches, etc.) during operation. This data typically includes parameters such as voltage, current, power, frequency, and temperature, and can reflect the current operating status and performance of the equipment.

[0046] Specifically, high-precision sensors and monitoring devices are installed on each power grid device. These sensors and devices continuously collect operating parameters such as voltage, current, and temperature. The collected data is transmitted in real time to a central data processing system via wireless or wired networks. In the central data processing system, the data undergoes initial cleaning and preprocessing to obtain real-time operating data, which is then stored in a database for subsequent analysis and monitoring. Simultaneously, the system is set with a fixed data acquisition cycle, such as once per second or per minute, to ensure the continuity and consistency of real-time data.

[0047] Step 204: The real-time analysis model of the power grid equipment performs self-learning optimization based on real-time operating data to obtain predicted equipment status verification information and real-time status analysis data.

[0048] Among them, the real-time analysis model of power grid equipment can be a mathematical or computer model used to analyze and monitor the real-time operating data of various equipment in the power grid. This model can process and analyze various parameters (such as voltage, current, power, frequency, etc.) obtained from power grid equipment in real time, and through data processing, pattern recognition and prediction algorithms, assess the operating status of the equipment, provide early warning of potential faults, and provide suggestions for optimizing operation and maintenance decisions.

[0049] Among them, the predicted equipment status verification information can be obtained by predicting the future operating status of power grid equipment through analysis and modeling methods.

[0050] Real-time status analysis data can be information obtained by monitoring and collecting various real-time data (such as voltage, current, power, temperature, etc.) of power grid equipment during operation and performing real-time analysis.

[0051] Specifically, operational data is input into the real-time analysis model of power grid equipment. Through analysis by this model, initial predicted equipment status verification information and real-time status analysis data are obtained. Based on these initial information, self-learning parameters are generated for the model. These parameters are then adjusted to obtain the adjusted real-time analysis model. Through repeated training and iteration, once the model can identify patterns and anomalies in equipment operation and meets predetermined standards, it generates predicted equipment status verification information and real-time status analysis data.

[0052] Step 206: Input the real-time status analysis data into the real-time device status verification model to obtain real-time device status verification information.

[0053] Among them, the real-time equipment status verification model can be a model used to monitor and analyze the real-time status analysis data of power grid equipment. By comparing with expected status or historical data, the current status of the equipment is verified.

[0054] Among them, the real-time equipment status verification information is the verification and evaluation results of the current operating status of power grid equipment generated by the real-time equipment status verification model. This information includes the comparison results of equipment operating parameters with expected values ​​or historical data, the identified anomalies or deviations, and the corresponding early warning prompts.

[0055] Specifically, real-time status analysis data is input into the real-time equipment status verification model. Based on preset normal operating parameters and standards of the equipment, the real-time equipment status verification model compares and analyzes the input data. Combined with the calculation and evaluation of the real-time equipment status verification model, it identifies the difference between the actual operating status of the equipment and the standard, thereby generating real-time equipment status verification information.

[0056] Step 208: The predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information of each power grid equipment.

[0057] Among them, the current operating status verification information can be the actual working status information of power grid equipment at a specific point in time in the power grid system after verification.

[0058] Specifically, both predicted and real-time equipment status verification information are simultaneously input into the integrated processing module. This module uses data fusion technology to comprehensively analyze and compare the two sets of information. By fusing the two sets of data, the system uses the real-time equipment status verification information to correct the predicted equipment status verification information, enabling a more comprehensive assessment of the equipment's operating status and identifying potential fault risks and anomalies. Finally, the integrated processing module outputs the current operating status verification information for each power grid device.

[0059] In the above-mentioned automatic verification method for the operating status of power grid equipment startup, real-time operating data corresponding to each power grid device in the power grid system is acquired. The real-time operating data is continuously acquired through fixed-period acquisition. The real-time analysis model of the power grid equipment performs self-learning optimization based on the real-time operating data to obtain predicted equipment status verification information and real-time status analysis data. The real-time status analysis data is input into the real-time equipment status verification model to obtain real-time equipment status verification information. The predicted equipment status verification information and the real-time equipment status verification information are integrated and processed to obtain the current operating status verification information corresponding to each power grid device.

[0060] By continuously acquiring real-time operational data at fixed intervals and utilizing a self-learning optimized real-time analysis model for power grid equipment, the operating status of the equipment is verified and analyzed, enabling timely detection of anomalies and potential problems in equipment operation. This method integrates predicted equipment status verification information with real-time equipment status verification information, improving not only the accuracy and reliability of power grid equipment operating status monitoring but also enabling proactive maintenance and management of equipment, reducing equipment failure rates and downtime. Ultimately, this integrated status monitoring and prediction scheme effectively improves the efficiency of automatic verification of power grid equipment startup, enhances the safety and stability of power grid operation, optimizes power grid operational efficiency, ensures the continuity and reliability of power supply, and provides strong support for the efficient management and sustainable development of smart grids.

[0061] In one embodiment, such as Figure 3 As shown, the predicted equipment status verification information and the real-time equipment status verification information are processed in an integrated manner to obtain the current operating status verification information for each power grid device, including:

[0062] Step 302: Based on the scenario perception data of the power grid system, generate the predicted equipment status verification information and the status verification logic corresponding to the real-time equipment status verification information.

[0063] Context-aware data can be environmental and equipment status-related information collected in real time through sensors, monitoring equipment, and other means, used to understand and analyze the current context.

[0064] Among them, the status verification logic can be the rules and algorithms for verifying and evaluating the current operating status of the system or equipment.

[0065] Specifically, the collected scenario-aware data of the power grid system (including information such as environmental conditions, load changes, and power demand) is input into the state verification logic generation model. Based on machine learning and data analysis algorithms, the model analyzes and calculates the scenario-aware data to generate predictive equipment state verification information adapted to the current scenario, as well as state verification logic corresponding to the real-time equipment state verification information. The state verification logic is used for standard operations that provide analysis and prediction of the operating status of equipment.

[0066] Step 304: Based on the status verification logic, integrate the predicted device status verification information and the real-time device status verification information to obtain the current operating status verification information.

[0067] Specifically, the state verification logic generated by the state verification model serves as the behavioral specification. Predicted equipment state verification information and real-time equipment state verification information are used as fusion inputs. Data fusion technology and the state verification logic are used to comprehensively process these two sets of information, evaluating their consistency and differences. By analyzing the correlation between predicted and real-time information, the system can identify the actual operating status and potential problems of the equipment, ultimately outputting comprehensive current operating status verification information.

[0068] In this embodiment, by generating state verification logic based on scenario-aware data of the power grid system, and integrating predictive and real-time equipment state verification information using this logic, the monitoring and prediction accuracy of power grid equipment operating status can be significantly improved. This method not only considers the current operating status of the equipment but also incorporates future predictive information, making state verification more comprehensive and reliable. By dynamically adjusting the verification logic, the system can adapt to different operating scenarios and environmental changes, promptly detecting potential faults and anomalies. This integrated approach improves the operational stability and security of the power grid system, reduces equipment failure rates and downtime, optimizes power grid maintenance and management, and ensures the continuity and reliability of power supply.

[0069] In one embodiment, such as Figure 4 As shown, based on the status verification logic, the predicted device status verification information and the real-time device status verification information are integrated to obtain the current operating status verification information, including:

[0070] Step 402: Generate a predicted state verification curve based on the state verification logic and the predicted device state verification information.

[0071] The predicted state verification curve can be a curve showing the verification results of the predicted future operating state of the equipment. This curve illustrates the trend of the predicted values ​​and is used to evaluate the accuracy and reliability of the prediction model.

[0072] Specifically, a correspondence is established between predicted equipment status verification information and status verification logic at different times. A prediction information fitting model is then used to fit this correspondence, generating a series of predicted status data at different time points, forming a predicted status verification curve. This curve illustrates the trend of equipment operating status changes over a future period.

[0073] Step 404: Generate a real-time status verification curve based on the status verification logic and real-time device status verification information.

[0074] The real-time status verification curve can be a curve representing the predicted verification result of the actual operating status of the equipment. This curve shows the trend of the actual value and is used to evaluate the accuracy and reliability of the actual model.

[0075] Specifically, a correspondence is established between real-time equipment status verification information and status verification logic at different times. A real-time information fitting model is then used to fit this correspondence, generating a series of real-time status data points to form a real-time status verification curve. This curve illustrates the trend of equipment operating status changes over a given period.

[0076] Step 406: Based on the predicted state verification curve and the real-time state verification curve, the predicted equipment state verification information and the real-time equipment state verification information are fused to obtain the current operating state verification information.

[0077] Specifically, the predicted state verification curve and the real-time state verification curve are used as constraints in the data fusion process. During fusion, the real-time state verification curve is used to correct the predicted state verification curve; that is, the constraints on the fused data change during the fusion process. Next, the predicted device state verification information and the real-time device state verification information are input into the fusion algorithm. Data fusion technology is used to comprehensively process the two sets of information, considering their correlation and differences, adjusting the weights to generate a more accurate state assessment, and finally outputting the current operating state verification information.

[0078] In this embodiment, by generating predicted and real-time state verification curves according to state verification logic, and fusing the predicted and real-time equipment state verification information, the accuracy of monitoring and predicting the operating status of power grid equipment can be significantly improved. The predicted state verification curve provides an estimate of future operating status, while the real-time state verification curve reflects the current actual operating situation. By comprehensively analyzing and fusing these two types of information, more accurate current operating status verification information can be obtained. This method not only promptly identifies potential equipment faults and anomalies but also provides maintenance personnel with a comprehensive and dynamic equipment status assessment, helping to develop more effective maintenance plans, improve the stability and reliability of the power grid system, and ensure the continuity and security of power supply.

[0079] In one embodiment, such as Figure 5 As shown, based on the predicted state verification curve and the real-time state verification curve, the predicted equipment state verification information and the real-time equipment state verification information are fused to obtain the current operating state verification information, including:

[0080] Step 502: Determine the optimal point for equipment status verification based on the predicted status verification curve and the real-time status verification curve.

[0081] Among them, the optimal point of equipment condition verification is determined by analyzing and comparing real-time data with expected values ​​during the equipment condition verification process. The optimal point of equipment condition verification represents the best performance state of the equipment under specific conditions, which helps to optimize equipment operation, improve efficiency, reduce failures, and extend equipment life.

[0082] Specifically, the predicted state verification curve and the real-time state verification curve are compared and analyzed to find the intersection point or near point. The intersection point or near point usually represents a high degree of consistency between the predicted state and the real-time state. The stability and reliability of the intersection point or near point are evaluated by optimization algorithms, and the intersection point or near point with the most stable equipment operating state and the lowest risk is further screened as the optimal point for equipment state verification.

[0083] Step 504: Based on the optimal point for equipment status verification, integrate the predicted equipment status verification information and the real-time equipment status verification information to obtain the current operating status verification information.

[0084] Specifically, using the optimal point for equipment status verification as a benchmark, the predicted equipment status verification information and the real-time equipment status verification information are fused. Through a data fusion algorithm, the predicted equipment status verification information and the real-time equipment status verification information are comprehensively processed, taking into account their correlation and differences, and assigning appropriate weights to generate a unified set of verification information as the current operating status verification information.

[0085] In this embodiment, by determining the optimal point for equipment status verification based on the predicted and real-time status verification curves, and then fusing the predicted and real-time equipment status verification information, the accuracy of power grid equipment operating status assessment can be significantly improved. Determining the optimal verification point makes the fusion process more targeted, finding the best balance between predicted and actual data, thereby generating more reliable current operating status verification information. This method not only improves the accuracy of anomaly detection and fault prediction but also optimizes equipment maintenance strategies, reduces equipment failure rates and downtime, and enhances the stability and reliability of the power grid system.

[0086] In one embodiment, such as Figure 6 As shown, the real-time analysis model for power grid equipment performs self-learning optimization based on real-time operating data to obtain predicted equipment status verification information and real-time status analysis data, including:

[0087] Step 602: Input the real-time operating data into the real-time analysis model of the power grid equipment to obtain the first analysis data.

[0088] The first analysis data can be the initial analysis data from the real-time analysis model of the power grid equipment.

[0089] Specifically, real-time operational data is input into a real-time analysis model for power grid equipment. This model, based on machine learning algorithms, analyzes the input real-time operational data to identify operational patterns and anomalies. By calculating and processing these operational patterns and anomalies, initial analytical data is generated. This data includes an assessment of the equipment's current operational status and a preliminary identification of potential problems.

[0090] Step 604: Obtain the next operating data according to a fixed cycle, and input the next operating data into the real-time analysis model of the power grid equipment to obtain the second analysis data.

[0091] The second analysis data can be the analysis data from the second analysis of the real-time analysis model of the power grid equipment.

[0092] Specifically, based on the fixed period corresponding to the acquisition of real-time operating data, the next operating data is acquired and input into the real-time analysis model of the power grid equipment. The real-time analysis model of the power grid equipment analyzes the input next operating data based on machine learning algorithms, updates the equipment's operating status assessment and detects potential anomalies. By calculating and processing the updated operating status assessment and detected potential anomalies, second analysis data is generated, which provides the latest equipment operating status information.

[0093] Step 606: Based on the differences between the second analysis data and the first analysis data, adjust the model parameters of the real-time analysis model of the power grid equipment to obtain a new real-time analysis model of the power grid equipment.

[0094] Specifically, the second set of analytical data is compared with the first set of analytical data, and the differences between them are calculated to identify the errors and biases in the model's prediction of equipment operating status. Then, using these errors and biases, the model parameters are adjusted through backpropagation or other optimization algorithms to reduce errors and improve the model's prediction accuracy. Next, the model is retrained to more accurately reflect the actual operating status of the power grid equipment, resulting in a new real-time analysis model for the power grid equipment.

[0095] Step 608: Input the real-time operating data into the new real-time analysis model of the power grid equipment to obtain the predicted equipment status verification information and real-time status analysis data.

[0096] Specifically, real-time operating data is input into a new real-time analysis model for power grid equipment. Based on the latest adjusted parameters and training results, the new real-time analysis model analyzes and processes the input real-time operating data to generate two types of information: predicted equipment status verification information, which reflects the predicted operating status of the equipment at future times; and real-time status analysis data, which provides an assessment of the operating status of the equipment at the current time.

[0097] In this embodiment, first analysis data is obtained by inputting real-time operating data into the real-time analysis model of the power grid equipment. Second analysis data is obtained by acquiring the next set of operating data at fixed intervals and inputting it into the model. The model parameters are adjusted based on the differences between the two sets of analysis data to obtain a new real-time analysis model for the power grid equipment. This significantly improves the accuracy of equipment condition monitoring and prediction. This dynamic adjustment and optimization process allows the real-time analysis model of the power grid equipment to continuously learn and adapt to new operating data, generating more accurate predicted equipment condition verification information and real-time condition analysis data. This method improves the accuracy of equipment operating condition assessment, enhances the ability to detect anomalies and predict faults, and facilitates preventative maintenance and timely fault handling.

[0098] In one embodiment, such as Figure 7 As shown, real-time operating data is input into a new real-time analysis model for power grid equipment to obtain predicted equipment status verification information and real-time status analysis data, including:

[0099] Step 702: Input the real-time operating data into the new real-time analysis model of the power grid equipment to obtain the initial state verification information and the initial state analysis data.

[0100] The initial state verification information can be the verification information output for the first time by the new real-time analysis model of the power grid equipment.

[0101] The initial state analysis data can be the analysis data output for the first time by the new real-time analysis model of the power grid equipment.

[0102] Specifically, real-time operational data is input into a new real-time analysis model for power grid equipment. Based on the latest adjusted parameters and training results, the new model analyzes and processes the input data to generate initial state verification information. This information is used to assess the current health and operational status of the equipment. Simultaneously, the new model also generates initial state analysis data, providing detailed operating parameters and analysis results for the equipment at the current moment.

[0103] Step 704: If the difference between the initial state verification information and / or the initial state analysis data and the preset standard is greater than a threshold, the next running data is used as the real-time running data, and the new real-time analysis model of the power grid equipment is used as the real-time analysis model of the power grid equipment. The process returns to the step of inputting the real-time running data into the real-time analysis model of the power grid equipment to obtain the first analysis data, until the difference between the initial state verification information and / or the initial state analysis data and the preset standard is less than a threshold, and a qualified real-time analysis model of the power grid equipment is obtained.

[0104] Specifically, the initial state verification information and / or initial state analysis data are compared with preset standards. If the comparison results show that the difference between one or both is greater than a set threshold, the operating data of the next cycle is collected, preprocessed, and used as new real-time operating data. A new real-time analysis model for the power grid equipment is then used as the new real-time analysis model for the power grid equipment. The process then returns to the steps of inputting real-time operating data into the real-time analysis model for the power grid equipment to obtain the first analysis data, and so on. The model parameters are adjusted, and this process is repeated iteratively—that is, data acquisition, model input, analysis, and verification are performed until the difference between the initial state verification information and the initial state analysis data and the preset standards is less than a threshold, resulting in a real-time analysis model for the power grid equipment that meets the preset standards.

[0105] Step 706: Input the real-time operating data and the next operating data into the appropriate real-time analysis model of the power grid equipment to obtain the predicted equipment status verification information and real-time status analysis data.

[0106] Specifically, real-time operating data and the next operating data of the continuously collected power grid equipment are preprocessed and input into the optimized and compatible real-time analysis model of the power grid equipment. The compatible real-time analysis model of the power grid equipment uses the latest parameters and training results to analyze and process the input data, generating two types of information: predicted equipment status verification information, which reflects the prediction of the operating status of the equipment at future time; and real-time status analysis data, which provides an assessment of the operating status of the equipment at the current time.

[0107] In this embodiment, initial state verification information and initial state analysis data are obtained by inputting real-time operating data into a new real-time analysis model of power grid equipment. If the difference between these data and a preset standard exceeds a threshold, the model is repeatedly updated using the next operating data until the difference falls below the threshold. This ensures the accuracy and reliability of the real-time analysis model of power grid equipment. This iterative optimization process allows the model to continuously self-adjust and improve, adapting to the constantly changing operating environment. Finally, the standard-compliant real-time analysis model is applied to the real-time operating data and the next operating data to generate accurate predicted equipment state verification information and real-time state analysis data. This method improves the accuracy of equipment state monitoring and prediction, helps to promptly detect anomalies and potential faults, and optimizes maintenance strategies.

[0108] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0109] Based on the same inventive concept, this application also provides an automatic verification device for the operating status of power grid equipment startup, used to implement the above-mentioned automatic verification method for the operating status of power grid equipment startup. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the automatic verification device for the operating status of power grid equipment startup provided below can be found in the limitations of the automatic verification method for the operating status of power grid equipment startup described above, and will not be repeated here.

[0110] In one embodiment, such as Figure 8 As shown, an automatic verification device for the operating status of power grid equipment startup is provided, comprising: an operating data acquisition module 802, a first data analysis module 804, a second data analysis module 806, and a status information acquisition module 808, wherein:

[0111] The operation data acquisition module 802 is used to acquire real-time operation data corresponding to each power grid device in the power grid system; the real-time operation data is continuously acquired through fixed-period acquisition.

[0112] The first data analysis module 804 is used for the real-time analysis model of power grid equipment to perform self-learning optimization based on real-time operating data, and to obtain predicted equipment status verification information and real-time status analysis data.

[0113] The second data analysis module 806 is used to input real-time status analysis data into the real-time equipment status verification model to obtain real-time equipment status verification information.

[0114] The status information acquisition module 808 is used to integrate the predicted equipment status verification information and the real-time equipment status verification information to obtain the current operating status verification information of each power grid device.

[0115] In one embodiment, the status information obtaining module 808 is further configured to generate state verification logic corresponding to predicted equipment status verification information and real-time equipment status verification information based on the scenario perception data of the power grid system; and to integrate the predicted equipment status verification information and real-time equipment status verification information based on the state verification logic to obtain the current operating status verification information.

[0116] In one embodiment, the status information obtaining module 808 is further configured to generate a predicted status verification curve based on the status verification logic and the predicted device status verification information; generate a real-time status verification curve based on the status verification logic and the real-time device status verification information; and obtain the current operating status verification information by fusing the predicted device status verification information and the real-time device status verification information based on the predicted status verification curve and the real-time status verification curve.

[0117] In one embodiment, the status information obtaining module 808 is further configured to determine the optimal point for device status verification based on the predicted status verification curve and the real-time status verification curve; and to obtain the current operating status verification information by fusing the predicted device status verification information and the real-time device status verification information based on the optimal point for device status verification.

[0118] In one embodiment, the first data analysis module 804 is further configured to: input real-time operating data into a real-time analysis model of power grid equipment to obtain first analysis data; acquire the next operating data according to a fixed period and input the next operating data into the real-time analysis model of power grid equipment to obtain second analysis data; adjust the model parameters of the real-time analysis model of power grid equipment according to the difference between the second analysis data and the first analysis data to obtain a new real-time analysis model of power grid equipment; and input real-time operating data into the new real-time analysis model of power grid equipment to obtain predicted equipment status verification information and real-time status analysis data.

[0119] In one embodiment, the first data analysis module 804 is further configured to input real-time operating data into a new real-time analysis model of the power grid equipment to obtain initial state verification information and initial state analysis data; if the difference between the initial state verification information and / or the initial state analysis data and the preset standard is greater than a threshold, the next operating data is used as real-time operating data, and the new real-time analysis model of the power grid equipment is used as the real-time analysis model of the power grid equipment, and the step of inputting real-time operating data into the real-time analysis model of the power grid equipment to obtain the first analysis data is returned, until the difference between the initial state verification information and / or the initial state analysis data and the preset standard is less than a threshold, and a compliant real-time analysis model of the power grid equipment is obtained; the real-time operating data and the next operating data are input into the compliant real-time analysis model of the power grid equipment to obtain predicted equipment state verification information and real-time state analysis data.

[0120] The modules in the aforementioned automatic verification device for the starting operation status of power grid equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0121] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores server data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements an automatic verification method for the operating status of power grid equipment during startup.

[0122] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0124] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0125] 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 in the above method embodiments.

[0126] 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, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0127] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, 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 many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0129] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for automatically checking the operating state of power grid equipment start-up, characterized in that, The method comprises: obtaining real-time operation data corresponding to each power grid device in a power grid system; the real-time operation data is obtained through continuous acquisition with a fixed period; a power grid device real-time analysis model is self-optimized according to the real-time operation data to obtain predicted device state checking information and real-time state analysis data; the real-time state analysis data is input into a real-time device state checking model to obtain real-time device state checking information; the predicted device state checking information and the real-time device state checking information are integrated to obtain current operation state checking information corresponding to each power grid device; including: generating a predicted state checking curve according to the state checking logic corresponding to the predicted device state checking information and the real-time device state checking information; generating a real-time state checking curve according to the state checking logic and the real-time device state checking information; determining a device state checking optimal point according to the predicted state checking curve and the real-time state checking curve, and fusing the predicted device state checking information and the real-time device state checking information to obtain the current operation state checking information according to the device state checking optimal point.

2. The method of claim 1, wherein, The integration of the predicted device state checking information and the real-time device state checking information to obtain the current operation state checking information corresponding to each power grid device comprises: generating the state checking logic corresponding to the predicted device state checking information and the real-time device state checking information according to the scenario perception data of the power grid system; integrating the predicted device state checking information and the real-time device state checking information according to the state checking logic to obtain the current operation state checking information.

3. The method of claim 1, wherein, The power grid device real-time analysis model is self-optimized according to the real-time operation data to obtain predicted device state checking information and real-time state analysis data, comprising: inputting the real-time operation data into the power grid device real-time analysis model to obtain first analysis data; obtaining next operation data according to the fixed period, and inputting the next operation data into the power grid device real-time analysis model to obtain second analysis data; adjusting model parameters of the power grid device real-time analysis model according to the difference between the second analysis data and the first analysis data to obtain a new power grid device real-time analysis model; inputting the real-time operation data into the new power grid device real-time analysis model to obtain the predicted device state checking information and the real-time state analysis data.

4. The method of claim 3, wherein, The inputting of the real-time operation data into the new power grid device real-time analysis model to obtain the predicted device state checking information and the real-time state analysis data comprises: inputting the real-time operation data into the new power grid device real-time analysis model to obtain initial state checking information and initial state analysis data; In a case where a difference between the initial state checking information and / or the initial state analysis data and a preset standard is greater than a threshold value, the next operation data is taken as the real-time operation data, and the new power grid equipment real-time analysis model is taken as the power grid equipment real-time analysis model, and the step of inputting the real-time operation data into the power grid equipment real-time analysis model to obtain first analysis data is returned to be executed until the difference between the initial state checking information and / or the initial state analysis data and the preset standard is less than the threshold value, and a consistent power grid equipment real-time analysis model is obtained; The real-time operation data and the next operation data are input into the consistent power grid equipment real-time analysis model to obtain the predicted equipment state checking information and the real-time state analysis data.

5. An apparatus for automatically checking the operating state of power grid equipment start-up, characterized by The device comprises: An operation data acquisition module is configured to acquire real-time operation data corresponding to each power grid equipment in a power grid system; the real-time operation data is obtained through continuous acquisition at a fixed period; A first data analysis module is configured to enable a power grid equipment real-time analysis model to perform self-learning optimization according to the real-time operation data to obtain predicted equipment state checking information and real-time state analysis data; A second data analysis module is configured to input the real-time state analysis data into a real-time equipment state checking model to obtain real-time equipment state checking information; A state information obtaining module is configured to integrally process the predicted equipment state checking information and the real-time equipment state checking information to obtain current operation state checking information corresponding to each power grid equipment; and is further configured to generate a predicted state checking curve according to state checking logic corresponding to the predicted equipment state checking information and the real-time equipment state checking information; generate a real-time state checking curve according to the state checking logic and the real-time equipment state checking information; determine an equipment state checking optimal point according to the predicted state checking curve and the real-time state checking curve; and fuse the predicted equipment state checking information and the real-time equipment state checking information according to the equipment state checking optimal point to obtain the current operation state checking information.

6. The apparatus of claim 5, wherein, The state information obtaining module is further configured to generate state checking logic corresponding to the predicted equipment state checking information and the real-time equipment state checking information according to scenario perception data of the power grid system; and integrate the predicted equipment state checking information and the real-time equipment state checking information according to the state checking logic to obtain the current operation state checking information. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor, when executing the computer program, implements the steps of the method in any one of claims 1 to 4.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 4.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 4.

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