Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for automatically checking the operation information of equipment start provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 obtains scheduling data, operation control data and protection signal data corresponding to each device in the power grid system from the terminal 102, fuses heterogeneous data in the scheduling data, the operation control data and the protection signal data to obtain a device running state model, generates a connection relation diagram and state change prediction information corresponding to each device according to real-time data and historical data in the scheduling data, the operation control data and the protection signal data, inputs the connection relation diagram and the state change prediction information into the device running state model to obtain initial operation information checking information, generates adjustment device operation information according to real-time data in the operation control data and the protection signal data, and determines operation information automatic checking information of the power grid system according to the initial operation information checking information and the adjustment device operation information. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, an automatic checking method for operation information of equipment start is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
Step 202, scheduling data, operation control data and protection signal data corresponding to each device in the power grid system are obtained.
The scheduling data may be data information generated and used in the operation and management process of various devices (such as a generator, a transformer, a transmission line, etc.) in the power system. These data include the operating status of the equipment, load conditions, power generation, fault records, maintenance plans, etc., which are typically monitored and recorded in real time by the power dispatching center for optimizing grid operation, ensuring reliability and stability of the power supply, and performing fault diagnosis and preventive maintenance.
The operation control data may be data information for controlling and operating various devices (e.g., generator, transformer, switch, circuit breaker, etc.) in the power system, among others. These data include switch status, control commands, setting parameters, automation control instructions, etc., through which data is passed.
The protection signal data may be data information for monitoring and protecting various devices (such as a generator, a transformer, a transmission line, etc.) in the power system. The data comprise real-time monitoring values of current, voltage, power, temperature and the like, fault detection signals, alarm information, action records of the protection device and the like. When an abnormality or fault occurs in the power equipment, the protection signal data triggers the protection device to automatically take measures, such as breaking a circuit or isolating a fault area, so as to prevent the equipment from being damaged and enlarge the fault range.
Specifically, the scheduling data, the operation control data and the protection signal data are obtained from sensors and monitoring systems of all devices in the power grid system. These data include the operating status of the equipment, the dispatch plan, the operating instructions, the status of the protection devices and alarm information. And transmitting the real-time data to a data center through a data acquisition terminal, and storing and processing by combining the historical data. In a data center, the heterogeneous data are uniformly processed and analyzed by utilizing a data fusion technology, so that the accuracy and consistency of the data are ensured.
And 204, fusing heterogeneous data in the scheduling data, the operation control data and the protection signal data to obtain a device running state model.
The device operation state model can be a model constructed by mathematical, physical and computational methods and is used for describing and simulating states and behaviors of the device under different operation conditions. The model integrates the operation parameters, historical data, environmental factors and the like of the equipment, and can predict the performance of the equipment, diagnose faults, optimize operation and maintenance strategies through simulation and analysis.
Specifically, the scheduling data, the operation control data and the protection signal data are preprocessed, including the steps of data cleaning, format conversion, time synchronization and the like, so as to ensure the consistency and the integrity of the data. And then, integrating the heterogeneous data by using a data fusion algorithm, and extracting key characteristics and parameters of equipment operation. Training and modeling the fusion data through a machine learning model or a model based on a physical mechanism to generate a device running state model reflecting the actual running state of the device, wherein the device running state model can accurately describe the performance and behavior of the device under different operating conditions.
And 206, generating a connection relation diagram and state change prediction information corresponding to each device according to the real-time data and the historical data in the scheduling data, the operation control data and the protection signal data.
The connection relation graph can be a graph graphically representing connection and relation among various elements in the system. The system has the advantages that the mutual connection and the dependency relationship among different components, nodes or devices are shown, and the structure and the layout of the system are intuitively depicted through the nodes and the connecting lines. The connection relation diagram is widely applied to the fields of network architecture, power system, database design and the like, and helps to understand the overall architecture of the system, analyze complex relations and optimize the system design.
The state change prediction information may be data information that predicts a possible state change in the future of the device, system, or process based on current state data and historical trends. By analyzing sensor data, operational records, environmental conditions, etc., potential problems, pre-estimated performance changes, scheduling maintenance, or adjusting operational strategies may be identified in advance using predictive models and algorithms.
Specifically, real-time data and historical data are extracted from the scheduling data, the operation control data and the protection signal data, analyzed and processed, and real-time state data and historical state data corresponding to the real-time data and the historical data respectively are determined. Then, a connection relation diagram of the devices is constructed by using a graph algorithm, and the interrelationship and the influence path among the devices are clarified. Based on the method, a time sequence analysis and a machine learning algorithm are applied, the running state of the equipment is input into a model by integrating real-time state data and historical state data to model and train, and the state change of the equipment under different running conditions is predicted. And finally, generating state change prediction information of each device, and displaying future operation trend and possible faults of the device.
And step 208, inputting the connection relation diagram and the state change prediction information into the equipment running state model to obtain initial operation information checking information.
The initial operation information checking information may be initial data information for checking and verifying basic information input by the system or the device during operation. The checking process ensures that the set parameters, configuration and operation steps are accurate and meet the design specification and operation requirements.
Specifically, the generated real-time data and the historical data of the device connection relation diagram and the state change prediction information are input into a device running state model to provide a comprehensive running state background. The equipment running state model is combined with real-time data and historical data of different points in the connection relation diagram, the current state and the future state of the equipment are estimated by using the prediction information, potential abnormality and deviation can be identified by comparing the real-time data with the prediction data, and initial operation information checking information is generated. Wherein the initial operation information check information includes a plausibility check for the current operation and future operation suggestions
Step 210, generating adjustment setting operation information according to the real-time data in the operation control data and the protection signal data.
The adjustment device operation information may be data information input to the system or the device during actual operation, and is used for adjusting initial operation information checking information so as to be more suitable for the current application scenario.
Specifically, real-time data is extracted from the operation control data and the protection signal data for analysis and processing, and the current running state and the operation behavior of the equipment are identified. And generating the adjustment device operation information by utilizing a dynamic analysis method of the real-time data and combining the operation specification and the protection logic of the device in the operation control data. This adjustment device operation information includes the actual current operating state of the device, operating parameters and protection action records,
Step 212, determining the automatic check information of the operation information of the power grid system according to the initial operation information check information and the adjustment setting operation information.
The automatic operation information checking information can be checking information obtained by adjusting the initial operation information checking information by using the adjustment device operation information, and is used for adjusting each relay in the power grid system in practical application.
Specifically, the initial operation information checking information is compared with the adjustment setting operation information, and the difference and the potential operation abnormality between the initial operation information checking information and the adjustment setting operation information are identified. Then, a checking algorithm is applied to compare and analyze the difference and potential operation abnormality between the two, and the accuracy and consistency of the operation of the equipment are evaluated. By further analysis of the difference data, it is determined whether there are operational policies or parameters that need to be adjusted. And finally, generating automatic check information of the operation information of the power grid system, and providing detailed check results and suggestions.
The automatic operation information checking method for equipment starting comprises the steps of obtaining scheduling data, operation control data and protection signal data corresponding to each equipment in a power grid system, fusing heterogeneous data in the scheduling data, the operation control data and the protection signal data to obtain an equipment operation state model, generating a connection relation diagram and state change prediction information corresponding to each equipment according to real-time data and historical data in the scheduling data, the operation control data and the protection signal data, inputting the connection relation diagram and the state change prediction information into the equipment operation state model to obtain initial operation information checking information, generating adjustment setting operation information according to real-time data in the operation control data and the protection signal data, checking information according to the initial operation information and the adjustment setting operation information, and determining automatic operation information checking information of the power grid system.
By acquiring and fusing the scheduling data, the operation control data and the protection signal data of the equipment in the power grid system, an equipment operation state model is established, and an equipment connection relation diagram and state change prediction information are generated, so that accurate monitoring and dynamic prediction of the equipment operation state are realized. And the accuracy and timeliness of the operation information are effectively improved based on real-time and historical data, and the error risk and delay problems caused by manual check are reduced. Finally, intelligent management of power grid system operation is achieved through automatic checking of operation information, efficiency of operation information checking of equipment starting is improved, safety, stability and efficiency of power grid operation are further improved greatly, potential faults can be rapidly dealt with and prevented, and continuity and reliability of power supply are guaranteed.
In one embodiment, as shown in fig. 3, generating a connection relationship diagram and state change prediction information corresponding to each device according to real-time data and historical data in the scheduling data, the operation control data and the protection signal data includes:
step 302, generating an initial device connection relation diagram according to real-time data and historical data in the scheduling data.
Wherein the initial device connection relationship graph may be an unverified connection relationship graph.
Specifically, real-time data and historical data are extracted from the scheduling data and preprocessed to ensure data integrity and consistency. And modeling the interaction information and the dependency relationship of each device by utilizing a graph database technology, and constructing a device connection relationship graph without meaning. And (3) identifying the connection and interaction between the devices by analyzing the device communication and operation records of the real-time data and the historical data in the scheduling data, determining the attribute of the nodes and edges of the device connection relation graph with the meaning not given to the real-time data and the historical data, and generating an initial device connection relation graph with dynamic update.
And step 304, according to the real-time data in the scheduling data, updating the connection relation of the initial equipment connection relation diagram to obtain the connection relation diagram corresponding to the equipment.
Specifically, the real-time data is continuously extracted from the scheduling data to dynamically update the initial device connection relation diagram, new scheduling information is analyzed, and newly added or changed device interactions and dependency relations are identified. Through graph database technology and incremental update algorithm, the connection relation between devices is adjusted in real time, new connection is added, the attribute of the existing connection is modified or the invalid connection is deleted. Finally, generating a connection relation diagram of the devices accurately reflecting the current device states and the interrelationships
And step 306, inputting real-time data and historical data in the scheduling data, the operation control data and the protection signal data into a device state real-time prediction model of the power grid system to obtain state change prediction information.
The real-time equipment state prediction model can be a mathematical and calculation model for real-time monitoring and predicting the real-time running state of power grid equipment (such as a generator, a transformer, a power transmission line and the like). By integrating real-time sensor data, historical operating data, environmental conditions, and the like, the model is able to predict future states of the device, identify potential faults, and performance degradation.
Specifically, after real-time data and historical data are collected from scheduling data, operation control data and protection signal data, the real-time data and the historical data are input into a device state real-time prediction model of a power grid system, and the device state real-time prediction model trains an implementation operation mode and a historical operation mode of the device by utilizing a machine learning and time sequence analysis algorithm and combines the real-time data and the historical data to conduct dynamic prediction. And generating prediction information of the equipment state change through calculation of the equipment state real-time prediction model, wherein the prediction information of the equipment state change comprises possible state change trend in the future and potential fault early warning.
In the embodiment, the method remarkably improves the monitoring and management capacity of the power grid system by generating and dynamically updating the equipment connection relation diagram, inputting real-time and historical data into the equipment state real-time prediction model and acquiring state change prediction information. Specifically, the initial device connection relationship graph provides a clear global view of the interaction relationship among devices, and dynamic updating ensures that the relationship graph always reflects the latest system state. By combining with the equipment state real-time prediction model, the equipment state change can be accurately predicted, and potential risks and faults can be identified in advance. The integration method not only improves the running reliability and stability of the power grid system, but also enhances the response speed and maintenance efficiency of the power grid system, and provides powerful support for intelligent power grid management.
In one embodiment, as shown in fig. 4, the step of inputting real-time data and historical data in the scheduling data, the operation control data and the protection signal data into a device state real-time prediction model of the power grid system to obtain state change prediction information includes:
step 402, determining a real-time data processing frame of the real-time data and a history data processing frame of the history data according to the real-time data and the history data in the scheduling data, the operation control data and the protection signal data.
Wherein the real-time data processing framework may be a platform or system for processing and analyzing data generated in real-time. It is capable of fast receiving, processing and analyzing data streams for timely response and decision making.
Wherein the historical data processing framework may be a platform or system for storing, processing and analyzing historical data that has been collected. The method can carry out batch processing and complex analysis on a large amount of past data, and helps users find long-term trends, patterns and anomalies.
Specifically, an initial real-time data processing frame is designed, the frame adopts a streaming processing technology to receive and process real-time data in scheduling data, operation control data and protection signal data in real time, and the self-adaptive quick response system changes and models, so that real-time analysis and decision are carried out to obtain the real-time data processing frame. And similarly, designing an initial historical data processing frame, and periodically summarizing and storing real-time data in the scheduling data, the operation control data and the protection signal data by adopting a batch processing technology, and carrying out depth analysis and trend prediction modeling in a self-adaptive manner to obtain the historical data processing frame.
And step 404, 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 frame to obtain optimized real-time data.
Specifically, the real-time data in the scheduling data, the operation control data and the protection signal data are preprocessed by using a real-time data processing framework, including the steps of data cleaning, de-duplication, format conversion and the like, then the data are received and analyzed in real time by using a stream processing technology, and noise and redundant information are removed by using a data filtering and aggregation algorithm at the same time, so that key characteristics and useful information are extracted. And then, further optimizing the processed data by applying an optimization algorithm to generate optimized real-time data.
Step 406, optimizing the historical data in the scheduling data, the operation control data and the protection signal data according to the historical data processing frame to obtain optimized historical data.
Specifically, the historical data in the scheduling data, the operation control data and the protection signal data are preprocessed by utilizing a historical data processing frame, the steps of data cleaning, deduplication, format conversion, time alignment and the like are included, then, large-scale historical data are summarized and stored by a batch processing technology, redundant information and abnormal values are removed by adopting a data mining and analysis algorithm, and key characteristics and trend information are extracted. Then, an optimization algorithm is applied to further optimize the processed historical data, and optimized historical data are generated.
And step 408, modifying parameters of the equipment state real-time prediction model of the power grid system according to the optimization real-time data and the optimization history data, so that the equipment state real-time prediction model outputs state change prediction information.
Specifically, the optimization real-time data and the optimization history data are input into the equipment state real-time prediction model, and at least one round of adjustment of parameters of the equipment state real-time prediction model is adaptively performed through initial output data of the equipment state real-time prediction model, wherein in the process of adjusting the parameters of the model, the parameters of the model are continuously adjusted in the training process by using an optimization algorithm (such as a gradient descent method) so as to minimize prediction errors. Through multiple rounds of iteration and cross verification, optimal configuration of model parameters is ensured, and finally the updated equipment state real-time prediction model can accurately output state change prediction information.
In the embodiment, the scheduling data, the operation control data and the protection signal data are optimized by constructing the real-time data processing frame and the historical data processing frame, and the parameters of the equipment state real-time prediction model of the power grid system are adjusted accordingly. Specifically, the optimized real-time data and history data ensure high quality of data input, so that the prediction model can reflect the running state and the change trend of the equipment more accurately. By dynamically adjusting the model parameters, the adaptability and accuracy of the model are enhanced. The integrated optimization flow not only improves the operation efficiency and reliability of the power grid system, but also enhances the fault prediction and risk management capability.
In one embodiment, as shown in fig. 5, after the step of modifying parameters of the device state real-time prediction model of the power grid system according to the optimization real-time data and the optimization history data so that the device state real-time prediction model outputs state change prediction information, the method further includes:
step 502, if the state change prediction information or/and the connection relationship diagram fails to meet the preset requirement, the connection relationship diagram is adjusted according to the state change prediction information, so as to obtain an adjusted relationship diagram.
Specifically, the relation between the state change prediction information and the connection relation diagram and the preset requirement is compared, and the accuracy and the reliability of the relation between the state change prediction information and the connection relation diagram and the preset requirement are evaluated. If the preset requirement is not met, analyzing the variation prediction information or/and the abnormality in the connection relation diagram and deviation from the preset requirement, and identifying factors possibly influencing the connection relation of the equipment. And then, according to the analysis results, the structure and the attribute of the connection relation graph are adjusted, and the connection and the interaction between the devices are redefined, so that the adjusted connection relation graph is obtained.
And step 504, adjusting the real-time data processing frame and the historical data processing frame according to the adjustment relation diagram, and returning to execute 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 frame to obtain optimized real-time data until the state change prediction information and the connection relation diagram can meet the preset requirement.
Specifically, according to the adjustment connection relation diagram, the parameters and the processing flows of the real-time data processing frame and the historical data processing frame are re-evaluated and adjusted, and the two adjusted data processing frames are applied to optimize the real-time data and the historical data in the scheduling data, the operation control data and the protection signal data, so that new optimized real-time data and new optimized historical data are generated. And inputting the optimized data into the equipment state real-time prediction model again, and iteratively adjusting model parameters to generate updated state change prediction information. And repeating the process, and continuously adjusting and optimizing until the state change prediction information and the connection relation diagram meet the preset requirement.
In the embodiment, the connection relation diagram and the data processing frame are adjusted in an iterative mode, so that the state change prediction information and the connection relation diagram meet preset requirements, and the adaptability and the accuracy of a power grid system are improved obviously. Specifically, when the state change prediction information or the connection relation diagram fails to meet the preset requirement, the connection relation diagram is timely adjusted according to the prediction information, and the real-time and historical data processing frame is optimized accordingly, so that the data processing is more accurate and efficient. This process is repeated until both the predicted information and the connection graph reach the desired criteria. The method not only improves the dynamic adjustment capability and fault prediction accuracy of the system, but also optimizes the data processing flow and model parameters, and enhances the overall operation efficiency and safety of the power grid system.
In one embodiment, as shown in fig. 6, inputting the connection relation diagram and the state change prediction information into the equipment running state model to obtain initial operation information checking information, including:
step 602, determining a device operation curve according to the connection relation diagram and the state change prediction information.
Wherein the device operating curve may be a graphical representation reflecting the performance and behavior of the device under different operating conditions. It generally shows the trend of the operating parameters of a plant (such as pressure, temperature, power, etc.) over time or operating variables (such as load, speed, etc.) in the form of a curve. By analyzing the plant operating curves, it is possible to evaluate the performance of the plant, identify optimal operating conditions, diagnose potential problems, and optimize operating strategies.
Specifically, the connection relation diagram and the state change prediction information are comprehensively analyzed, and the interdependence relation and the state change trend between the devices are identified. And then, extracting the operation parameters and state data of the key equipment on the basis of the mutual dependency relationship and the state change trend, and drawing the state change curves of the equipment under different operation conditions by using a time sequence analysis and regression analysis method. And generating an operation curve reflecting the operation rule and the performance characteristic of the equipment as an operation curve of the equipment through data fitting and model optimization.
Step 604, determining a theoretical operation curve according to real-time data and historical data in the scheduling data, the operation control data and the protection signal data.
The theoretical operating curve may be an ideal operating curve calculated according to design parameters and theory of the device, and is used for representing the performance and behavior of the device under different operating conditions. It shows the variation trend of each operation parameter (such as pressure, temperature, power, etc.) of the equipment under the optimal working condition. The theoretical operating curve is used to compare the actual operating curve to evaluate the operating efficiency of the device, diagnose deviations, and optimize operation.
Specifically, real-time data and historical data are extracted from scheduling data, operation control data and protection signal data to perform data analysis and modeling technology, and initial theoretical operation curves of all equipment under different working conditions are determined. And analyzing historical operation data of the equipment by using time sequence analysis, regression analysis and a machine learning algorithm, extracting key parameters and characteristics, correcting an initial theoretical operation curve, and generating the theoretical operation curve reflecting the ideal operation state of the equipment.
And step 606, comparing the equipment operation curve with the theoretical operation curve to obtain initial operation information checking information.
Specifically, the device operating curve is data aligned and normalized with the theoretical operating curve to ensure that the two are comparable in the same time and parameter dimensions. Then, the two curves are compared by using an error analysis and deviation detection method, and the difference between the actual operation and the theoretical operation is identified. By calculating the deviation value, the error range and the trend analysis, the accuracy and consistency of the operation of the equipment are evaluated. Finally, initial operation information checking information is generated.
In the embodiment, the equipment operation curve is determined according to the connection relation diagram and the state change prediction information, and is compared with the theoretical operation curve determined according to the real-time data and the historical data in the scheduling data, the operation control data and the protection signal data to generate the initial operation information check information. Specifically, the device operation curve reflects the actual operation condition, while the theoretical operation curve represents the ideal operation condition, and the operation deviation and the potential problem can be accurately identified through the comparison of the device operation curve and the theoretical operation curve. The generated initial operation information checking information provides scientific basis for adjusting and optimizing the operation strategy, reduces the failure occurrence rate and improves the stability and safety of the system.
In one embodiment, as shown in fig. 7, the method further comprises:
step 702, obtaining comprehensive topological structure, equipment state data and environment data corresponding to each equipment in a power grid system.
The integrated topology structure can be an overall layout and configuration diagram formed by comprehensively considering each node and connection relation in a system or a network. It depicts interconnections and relationships between various components, devices or nodes in a system, including physical and logical level architectures.
The equipment state data can be various data information reflecting the current operation condition of the equipment, including but not limited to parameters such as temperature, pressure, current, voltage, rotating speed, load, vibration and the like. The data are collected in real time through the sensor and the monitoring system, and key information such as the running performance, the health condition and the operating state of the equipment is provided for monitoring, diagnosing, maintaining and optimizing the running of the equipment, so that the equipment can work safely, reliably and efficiently.
The environmental data can be various data information describing and reflecting the specific environmental conditions and states of the power grid system, including temperature, humidity, air pressure, wind speed, rainfall, air quality, noise level and the like. These data are collected in real-time by sensors and monitoring systems, providing detailed recordings and analysis of environmental changes and conditions.
Specifically, comprehensive topological structure information of each device in the power grid system is collected, wherein the comprehensive topological structure information comprises physical positions, connection relations and network configuration of the devices. Next, device status data, such as operating parameters, fault records, and performance indicators, are obtained using the sensors and monitoring system. At the same time, relevant environmental data is extracted from the environmental monitoring system, including temperature, humidity, air pressure, and other environmental factors. And integrating the data into a data center through the data acquisition terminal and network transmission, and carrying out unified storage and processing. A comprehensive database is ultimately formed containing device topology, state data, and environmental data.
And step 704, inputting the comprehensive topological structure, the equipment state data and the environment data into a potential risk prediction model to obtain potential risk prediction data.
The potential risk prediction data may be data information for predicting risks and problems that may occur to the power grid system by analyzing current data and historical trends. The method comprises the step of predicting potential risks such as equipment faults and natural disasters. These predictive data are generated by models and algorithms that help decision makers identify and address potential risks in advance.
Specifically, the comprehensive topological structure, the equipment state data and the environment data are input into a potential risk prediction model, the potential risk prediction model utilizes machine learning and big data analysis technology to comprehensively evaluate and analyze the running state and the environment condition of equipment on the basis of the comprehensive topological structure, identify key factors and risk modes possibly causing equipment faults or abnormality, and generate the potential risk prediction data, wherein the potential risk prediction data comprises the risk level, the fault probability and early warning information of the equipment.
And step 706, optimizing the automatic operation information checking information by utilizing the potential risk prediction data to obtain the automatic operation information checking information.
The automatic check information of the optimized operation information can be the automatic check information of the optimized operation information, and the efficiency and the accuracy are higher.
Specifically, the potential risk prediction data and the initial operation information check information are compared and analyzed, and potential risks and operation deviations are identified. Based on the potential risks and the operation deviation, a checking algorithm is adjusted and optimized according to the potential risk prediction data, high-risk equipment and areas are focused, and the checking strength of key operation parameters is enhanced. And (3) obtaining automatic check information of the optimized operation information by iterative optimization rechecking of the operation information, wherein the automatic check information of the optimized operation information comprises detailed operation adjustment suggestions and risk early warning.
In the embodiment, the comprehensive topological structure, the equipment state data and the environment data of each equipment in the power grid system are obtained, the comprehensive topological structure, the equipment state data and the environment data are input into the potential risk prediction model to generate the potential risk prediction data, and then the operation information automatic check information is optimized by utilizing the prediction data to obtain the optimized operation information automatic check information. Specifically, the comprehensive topological structure enables the overall layout and connection relation of the power grid to be more definite, and the equipment state data and the environment data provide real-time operation and environment information. Through the potential risk prediction model, risks can be recognized and evaluated in advance, the information can be automatically checked by optimizing operation information, and the check result is ensured to be more accurate and timely. Therefore, the probability of faults is reduced, the operation strategy is optimized, the overall operation efficiency and reliability of the power grid system are improved, and the stability and the duration of power supply are ensured.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an equipment-started operation information automatic checking device for realizing the above-mentioned equipment-started operation information automatic checking method. The implementation scheme of the device for solving the problem is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the device for automatically checking the operation information of one or more equipment starts provided below can be referred to the limitation of the method for automatically checking the operation information of one equipment starts in the above description, and the description is omitted here.
In one embodiment, as shown in fig. 8, there is provided an apparatus for automatically checking operation information for device start, which includes a power grid data acquisition module 802, a state model obtaining module 804, a first data processing module 806, a second data processing module 808, an operation information generating module 810, and a check information determining module 812, wherein:
The power grid data acquisition module 802 is configured to acquire scheduling data, operation control data and protection signal data corresponding to each device in the power grid system;
a state model obtaining module 804, configured to fuse heterogeneous data in the scheduling data, the operation control data, and the protection signal data to obtain an equipment operation state model;
A first data processing module 806, configured to generate a connection relationship diagram and state change prediction information corresponding to each device according to the real-time data and the historical data in the scheduling data, the operation control data, and the protection signal data;
the second data processing module 808 is configured to input the connection relationship diagram and the state change prediction information into the device running state model, so as to obtain initial operation information check information;
An operation information generating module 810 for generating adjustment setting operation information according to real-time data in the operation control data and the protection signal data;
and the checking information determining module 812 is configured to determine automatic checking information of the operation information of the power grid system according to the initial operation information checking information and the adjustment setting operation information.
In one embodiment, the first data processing module 806 is further configured to generate an initial device connection relationship graph according to real-time data and historical data in the scheduling data, update a connection relationship of the initial device connection relationship graph according to the real-time data in the scheduling data to obtain a connection relationship graph corresponding to the device, and input the real-time data and the historical data in the scheduling data, the operation control data and the protection signal data to a device state real-time prediction model of the power grid system to obtain state change prediction information.
In one embodiment, the first data processing module 806 is further configured to determine a real-time data processing frame of the real-time data and a historical data processing frame of the historical data according to the real-time data and the historical data in the scheduling data, the operation control data and the protection signal data, optimize the real-time data in the scheduling data, the operation control data and the protection signal data according to the real-time data processing frame to obtain optimized real-time data, optimize the historical data in the scheduling data, the operation control data and the protection signal data according to the historical data processing frame to obtain optimized historical data, and modify parameters of a real-time prediction model of a device state 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 state outputs state change prediction information.
In one embodiment, the first data processing module 806 is further configured 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 or/and the connection relationship diagram fails to meet the preset requirement, adjust the real-time data processing frame and the historical data processing frame according to the adjusted relationship diagram, and return to execute 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 frame to obtain optimized real-time data until the state change prediction information and the connection relationship diagram can meet the preset requirement.
In one embodiment, the second data processing module 808 is further configured to determine an equipment operation curve according to the connection relationship diagram and the state change prediction information, determine a theoretical operation curve according to real-time data and historical data in the scheduling data, the operation control data and the protection signal data, and compare the equipment operation curve with the theoretical operation curve to obtain initial operation information checking information.
In one embodiment, the check information determining module 812 is further configured to obtain a comprehensive topology structure, device state data, and environment data corresponding to each device in the power grid system, input the comprehensive topology structure, the device state data, and the environment data to the risk potential prediction model to obtain risk potential prediction data, and optimize the automatic check information of the operation information by using the risk potential prediction data to obtain the automatic check information of the optimized operation information.
All or part of each module in the automatic operation information checking device started by the equipment can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a device-initiated method of automatically checking operational information.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-described method embodiments.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.