Operation simulation system for power supply management based on virtual operation technology

Abstract

The invention discloses an operation simulation system for power supply management based on a virtual operation technology, and relates to the technical field of power systems. Comprising a data integration and sharing module, a real-time monitoring and analysis module, a work order circulation and intelligence module, an index root cause analysis module and a problem analysis and early warning module. Through the data integration and sharing module, data resources of different departments and systems are integrated and shared, the problem of data islands is solved, a unified data storage and management system is established, homology and consistency of data are ensured, efficient sharing and interaction of the data are achieved, and through the real-time monitoring and analysis module, real-time monitoring and analysis of the data resources are achieved. On the basis of the virtual operation technology, the operation condition of the power grid is monitored and analyzed in real time, the operation condition and the operation scene of the power grid are simulated, subtle changes are captured, the influence of a complex business process and an external environment is accurately reflected, and a deep analysis tool and algorithm are provided so as to obtain accurate and timely insight and support decision making.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to an operation simulation system for power supply management based on virtual operation technology. Background Technology

[0002] With the rapid development of information technology, all industries are accelerating their digital transformation. Against this backdrop, the power industry is also following this trend, undertaking the construction of smart grids and striving to improve the efficiency and intelligence of power supply operations through digital technology. However, this process has encountered many problems in practice. Especially in the practical application of strategic operation and control platforms, how to effectively integrate various data resources, achieve efficient work order processing, improve early warning response speed, and promote the digital transformation of county-level power bureaus have become urgent challenges to be addressed.

[0003] Although county-level power supply bureaus across the province use the county-level power supply operation command platform to view the strategic operation and control requirements of higher authorities and attempt to manage their own strategic operation and control work, existing technologies still have shortcomings in data integration, analysis, and processing. For example, data silos are common, and data sharing between different departments and systems is not smooth, resulting in information not being reflected accurately and in real time to decision-making levels at all levels; data analysis tools are crude and cannot provide sufficiently in-depth insights, affecting the accuracy and timeliness of operation monitoring indicators.

[0004] Furthermore, in terms of work order flow and work order early warning functions, the lack of highly automated and intelligent support leads to low work order processing efficiency and unsatisfactory response time. In the process of handling early warning work orders at the county level, it is also necessary to strengthen the correlation between operation and monitoring indicators and business management work orders. Therefore, a power supply management operation simulation system based on virtual operation technology is needed to address the shortcomings of existing technologies. By simulating the operation of the power grid and operational scenarios, it can provide real-time monitoring, analysis, and decision support to improve the efficiency and intelligence level of power supply operation. Summary of the Invention

[0005] The purpose of this invention is to provide an operation simulation system for power supply management based on virtual operation technology, which solves problems such as data integration and sharing, real-time monitoring and analysis, and work order processing, and brings the effects of data sharing and interaction, accurate insight into the operation of the power grid, and efficient work order processing.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] This application provides an operation simulation system for power supply management based on virtual operation technology, including:

[0008] The data integration and sharing module is responsible for integrating and sharing data resources from different departments and systems, solving the problem of data silos, and establishing a unified data storage and management system.

[0009] The real-time monitoring and analysis module, based on virtual operation technology, monitors and analyzes the operation of the power grid in real time. By simulating the operation of the power grid and operational scenarios, it captures subtle changes and accurately reflects the impact of complex business processes and the external environment. It provides in-depth analysis tools and algorithms to obtain accurate and timely insights and make decisions.

[0010] The work order circulation and intelligent module uses automated processes and intelligent algorithms to efficiently circulate work orders, improve work order processing efficiency and response time, and promote cross-departmental coordination and cooperation through the close correlation between operation monitoring indicators and business management work orders.

[0011] The indicator root cause analysis module utilizes big data analytics and machine learning techniques to perform indicator root cause analysis. By analyzing historical and real-time monitoring data, it uncovers potential problems and predicts future trends, providing the ability to discover and predict deep causal relationships.

[0012] The problem analysis and early warning module, based on big data analysis and intelligent algorithms, performs problem analysis and early warning. By establishing flexible models and parameter settings, it identifies and adapts to the ever-changing operating environment, accurately identifies and warns of emerging risks, and provides targeted solutions.

[0013] Preferably, the data integration and sharing module includes a data collection unit, a data storage unit, and a data sharing and interaction unit.

[0014] The data collection unit collects various data resources from different departments and systems, including operational data, equipment data, and personnel data, and interfaces with various data sources to obtain data in real time.

[0015] The data storage unit stores and manages the collected data by establishing a unified data storage system.

[0016] The data sharing and interaction unit shares the acquired real-time data with other modules. By providing data interfaces and services, it enables other modules to obtain the required data on demand and supports data interaction and transmission.

[0017] Preferably, the real-time monitoring and analysis module includes a simulated power grid operation unit, a real-time monitoring unit, a change capture unit, and a deep analysis unit.

[0018] The simulated power grid operation unit establishes a virtual power grid model to simulate the operation and scenarios of the real power grid, such as power load changes, equipment failures, and weather conditions, and accurately reflects the actual operating status of the power grid.

[0019] The real-time monitoring unit uses monitoring equipment to collect real-time data from various nodes and devices in the power grid. The data includes voltage, current, frequency, and load indicators. The collected data is used to update the virtual power grid model and provided for subsequent analysis.

[0020] The change capture unit captures subtle changes in the power grid, including voltage fluctuations, frequency deviations, and load anomalies, by comparing and analyzing real-time data with a virtual power grid model, and reflects the impact of complex business processes and the external environment.

[0021] The deep analysis unit uses deep analysis tools and algorithms to further analyze the captured subtle changes, identify anomalies, and provide corresponding insights and suggestions.

[0022] Preferably, the work order flow and intelligent module includes a work order creation and dispatch unit, a work order execution and coordination unit, and a work order tracking and feedback unit.

[0023] The work order creation and assignment unit is responsible for automatically creating and assigning work orders based on operation monitoring indicators and business management needs. It utilizes automated processes and intelligent algorithms to automatically generate work orders according to preset rules and conditions, and assigns the work orders to the corresponding departments or personnel.

[0024] The work order execution and coordination unit receives and processes work orders assigned by the work order creation and dispatch unit, guides the execution and coordination of work orders through intelligent algorithms and collaboration tools, and provides guidance and support for work order execution.

[0025] The work order tracking and feedback unit is responsible for tracking and providing feedback on the execution status of work orders, monitoring the execution progress and status of work orders, updating work order information in a timely manner, and receiving feedback and opinions from relevant personnel in order to adjust and optimize the work order processing flow in a timely manner.

[0026] Preferably, the root cause analysis module includes a data acquisition unit, a feature extraction and selection unit, a model training and evaluation unit, and a root cause analysis and prediction unit.

[0027] The data acquisition unit is responsible for receiving the required indicator data from historical data and real-time monitoring data from the data integration and sharing module and the real-time monitoring and analysis module, and then cleaning and preprocessing the collected data.

[0028] The feature extraction and selection unit, based on big data analysis and machine learning technology, performs feature extraction and selection on the collected indicator data, and uses appropriate feature extraction algorithms to transform the original data into more meaningful and expressive features.

[0029] The model training and evaluation unit, based on the data from the data acquisition unit and the feature extraction and selection unit, uses machine learning algorithms and model training techniques to establish an indicator root cause analysis model. Based on historical data and real-time monitoring data, the model is trained to identify the root causes of indicator changes and predict future trends. After training, the model is evaluated and validated.

[0030] The root cause analysis and prediction unit uses a trained model to perform root cause analysis and prediction on real-time monitoring data, identify the root causes of potential problems leading to changes in indicators, and provide in-depth causal relationship analysis. Through the predictive capabilities of the model, it predicts future trends and potential problems.

[0031] Preferably, the problem analysis and early warning module includes a problem identification and classification unit, an early warning generation and management unit, and a recommendation and optimization unit.

[0032] The problem identification and classification unit, based on big data analysis and intelligent algorithms, identifies and classifies problems in the data of the indicator root cause analysis model. Using appropriate algorithms and models, it analyzes and mines the data, identifies potential problems and anomalies, and classifies and marks them for subsequent early warning and solution provision.

[0033] The early warning generation and management unit generates corresponding early warning information based on the results of the problem identification and classification unit. By establishing flexible models and parameter settings, it can identify and adapt to the constantly changing operating environment, and accurately identify and warn of emerging risks.

[0034] The recommendation and optimization unit, based on the results of the problem identification and classification unit, recommends applicable solutions according to different problem types and scenarios, and optimizes the effectiveness and application of the solutions based on feedback and real-time data updates.

[0035] Preferably, the virtual power grid model is established by collecting real power system data, including generator capacity and output, load demand, transmission line parameters, and transformer turns ratio information, and using the power system data as the input and constraint conditions of the model.

[0036] Based on the characteristics and operating principles of the power system, a mathematical model is established to describe the operation of the power system. Methods such as power flow calculation and optimization algorithms are used to simulate the operating state of the power system. The characteristics of various devices are considered in the model, such as the response speed of generators, the resistance loss of transmission lines, and the efficiency of transformers.

[0037] By utilizing existing optimization algorithms and techniques, the model can be designed and optimized. Historical data or actual operating data can be used to verify the model. By adjusting the model parameters and algorithms, the operating conditions of the power system can be accurately predicted. The established virtual power grid model can be applied to the actual operation of the power system to monitor and predict the operating status of the power system and provide corresponding decision support.

[0038] Preferably, the work order creation and dispatch unit utilizes automated processes and intelligent algorithms to automatically generate work orders based on preset rules and conditions. The work order creation and dispatch process includes...

[0039] Monitoring indicator collection: Real-time collection of operational monitoring indicators, such as equipment operating status, temperature, voltage, and frequency data, through monitoring equipment;

[0040] The collected monitoring data is processed and analyzed to identify anomalies or potential problems and determine whether a work order needs to be generated.

[0041] Rules and conditions are defined based on business management needs and operation monitoring indicators. For example, if the equipment temperature exceeds the threshold or the voltage fluctuation exceeds the limit, these rules and conditions will serve as the basis for triggering the generation of work orders.

[0042] Work order generation is automatically triggered when certain conditions are met, based on preset rules and conditions. Work orders include work order type, description, priority information, and related equipment and personnel information.

[0043] Work order assignment: Based on the priority of the work order, intelligent algorithms are used to automatically assign the work order to the appropriate maintenance personnel or team.

[0044] Work order execution and feedback: Maintenance personnel receive work orders and perform corresponding maintenance tasks. During the execution of work orders, updates and records are made in real time through mobile devices or systems.

[0045] Work order closure and evaluation: Once the maintenance task is completed, the work order is closed, and the maintenance quality is evaluated. The evaluation results are fed back into the system as a basis for improving work order generation and assignment.

[0046] The beneficial effects of this invention are as follows:

[0047] (1) Through the data integration and sharing module, data resources from different departments and systems are integrated and shared, solving the problem of data silos, establishing a unified data storage and management system, ensuring the homogeneity and consistency of data, and realizing efficient data sharing and interaction;

[0048] (2) Through the real-time monitoring and analysis module, the operation of the power grid is monitored and analyzed in real time based on virtual operation technology. The operation of the power grid and the operation scenario are simulated. Subtle changes are captured and the impact of complex business processes and external environment are accurately reflected. In-depth analysis tools and algorithms are provided to obtain accurate and timely insights and support decision-making.

[0049] (3) Through the work order circulation and intelligent module, the efficient circulation function of work orders is realized. By using automated processes and intelligent algorithms, work orders are automatically generated according to preset rules and conditions, and automatically assigned to suitable maintenance personnel or teams, which improves the efficiency of work order processing and response time, realizes the close connection between operation and monitoring indicators and business management work orders, and promotes cross-departmental coordination and cooperation. Attached Figure Description

[0050] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0051] Figure 1 This application provides a schematic diagram of the data integration and sharing module structure of an operation simulation system for power supply management based on virtual operation technology, as shown in Embodiment 1 of this application.

[0052] Figure 2 This is a schematic diagram of the real-time monitoring and analysis module structure of an operation simulation system for power supply management based on virtual operation technology, provided in Embodiment 1 of this application.

[0053] Figure 3 This is a schematic diagram of the work order flow and intelligent module structure of a power supply management operation simulation system based on virtual operation technology, provided in Embodiment 1 of this application.

[0054] Figure 4 This is a schematic diagram of the root cause analysis module structure of an operation simulation system for power supply management based on virtual operation technology, provided in Embodiment 1 of this application.

[0055] Figure 5 This is a schematic diagram of the problem analysis and early warning module structure of an operation simulation system for power supply management based on virtual operation technology, provided in Embodiment 1 of this application. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0057] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0058] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0059] Example 1

[0060] Please see Figures 1-5 This embodiment provides an operation simulation system for power supply management based on virtual operation technology, which solves problems such as data integration and sharing, real-time monitoring and analysis, and work order processing, and brings the effects of data sharing and interaction, accurate insight into the power grid operation, and efficient work order processing.

[0061] This invention provides an operation simulation system for power supply management based on virtual operation technology, including...

[0062] The data integration and sharing module is responsible for integrating and sharing data resources from different departments and systems, solving the problem of data silos, and ensuring data homogeneity and consistency by establishing a unified data storage and management system.

[0063] The specific data resources include: business data, which is business-related data generated by various departments and systems, such as sales data, customer data, and supply chain data; operational data, which involves operational activities and processes, such as production data, equipment data, and logistics data; financial data, which includes financial statements, accounting records, and cost data generated by the finance department; human resources data, which includes employee information, salary data, and performance data managed by the human resources department; customer data, which includes customer information, interaction records, and market research data from the customer relationship management (CRM) system; and log data, which includes log records generated by the system and applications, including error logs, access logs, and operation logs.

[0064] The real-time monitoring and analysis module, based on virtual operation technology, monitors and analyzes the operation of the power grid in real time. By simulating the operation of the power grid and operational scenarios, it captures subtle changes and accurately reflects the impact of complex business processes and the external environment. It provides in-depth analysis tools and algorithms to obtain accurate and timely insights and support decision-making.

[0065] The work order circulation and intelligent module, through automated processes and intelligent algorithms, enables efficient work order circulation, improves work order processing efficiency and response time, achieves close correlation between operation monitoring indicators and business management work orders, and promotes cross-departmental coordination and cooperation.

[0066] The root cause analysis module utilizes big data analytics and machine learning techniques to perform root cause analysis of indicators. By analyzing historical and real-time monitoring data, it uncovers the causes of potential problems and predicts future trends, providing the ability to uncover and predict deep causal relationships, thereby enabling proactive control and refined management.

[0067] The problem analysis and early warning module, based on big data analysis and intelligent algorithms, performs problem analysis and early warning. By establishing flexible models and parameter settings, it identifies and adapts to the ever-changing operating environment, accurately identifies and warns of emerging risks, avoids false alarms, and provides targeted solutions.

[0068] The data integration and sharing module integrates data from various departments and systems and makes it available to other modules. The real-time monitoring and analysis module receives real-time monitoring data from the data integration and sharing module for analysis and insight. The work order flow and early warning module receives early warning information from the real-time monitoring and analysis module for work order processing and cross-departmental coordination. The indicator root cause analysis module receives data from the real-time monitoring and analysis module for root cause analysis and prediction. The problem analysis and early warning module receives analysis results and early warning information from the indicator root cause analysis module and provides corresponding solutions.

[0069] An operation simulation system for power supply management based on virtual operation technology can be built to overcome the shortcomings of existing technologies, improve the efficiency and intelligence level of power supply operation, and provide important support for the digital transformation of the power industry and the construction of smart grids.

[0070] In this embodiment, the operation and usage scenarios of the power grid are simulated, specifically including:

[0071] Topology modeling involves establishing a topology model of the power grid based on its actual layout and connections. This includes various nodes such as power plants, substations, transmission lines, and distribution networks, as well as their connections.

[0072] Equipment characteristic simulation involves modeling and simulating the characteristics of various devices in the power grid (such as generators, transformers, and switchgear). It considers characteristics such as rated capacity, efficiency curves, and response speed to accurately reflect the behavior of the equipment during operation.

[0073] Load simulation models the load demand of the power grid, including short-term load fluctuations and seasonal load changes. Based on historical data and predictive models, it generates load curves that approximate actual conditions.

[0074] External environment simulation considers the impact of external environmental factors on power grid operation, such as weather conditions (temperature, humidity, wind speed, etc.) and energy market prices. Meteorological data and market data are used to simulate these external environmental parameters.

[0075] Operation strategy simulation: Based on operation and maintenance strategies and operating procedures, simulate the operation strategies and scenarios of the power grid, including equipment switching operations, disaster recovery measures, scheduling strategies, etc., to reflect various situations and decisions in actual operation;

[0076] Real-time data injection involves injecting actual monitored real-time data into the simulation environment and fusing it with the simulation data. This allows for a more accurate simulation of the actual operation of the power grid and enables status updates and adjustments based on real-time data.

[0077] By simulating the operation and scenarios of the power grid, the power grid can be simulated and drilled in a virtual environment, capturing the impact of subtle changes and complex business processes, providing accurate and comprehensive operational analysis, and supporting decision-making and optimization.

[0078] In this embodiment, the data integration and sharing module includes a data collection unit, a data storage unit, and a data sharing and interaction unit.

[0079] The data collection unit collects various data resources from different departments and systems, including operational data, equipment data, and personnel data, and obtains data in real time by connecting with various data sources through interfaces.

[0080] The data storage unit stores and manages the collected data. By establishing a unified data storage system, including technologies such as data lakes and data warehouses, it ensures the source and consistency of the data.

[0081] The data sharing and interaction unit shares the acquired real-time data with other modules. By providing data interfaces and services, it enables other modules to obtain the required data on demand and supports data interaction and transmission.

[0082] Each unit of the data integration and sharing module undertakes different functions and tasks. Different divisions can make the data integration and sharing module more clearly realize its functions and ensure the flow and utilization of data in the system.

[0083] In this embodiment, the real-time monitoring and analysis module includes a simulated power grid operation unit, a real-time monitoring unit, a change capture unit, and a deep analysis unit.

[0084] The simulated power grid operation unit simulates the operation and scenarios of the real power grid by establishing a virtual power grid model. The model takes into account various factors, such as power load changes, equipment failures, and weather conditions, in order to more accurately reflect the actual operation of the power grid.

[0085] The real-time monitoring unit uses monitoring equipment to collect real-time data from various nodes and devices in the power grid. The data includes indicators such as voltage, current, frequency, and load. The collected data can be used to update the virtual power grid model and provide data for subsequent analysis.

[0086] The change capture unit can capture subtle changes in the power grid, including voltage fluctuations, frequency deviations, and load anomalies, by comparing and analyzing real-time data with a virtual power grid model, and accurately reflect the impact of complex business processes and the external environment.

[0087] The deep analysis unit uses deep analysis tools and algorithms to further analyze subtle changes captured, identify anomalies, and provide corresponding insights and suggestions. It can accurately reflect the impact of complex business processes and the external environment, help users better understand the power grid's operating status, and support decision-making.

[0088] Real-time monitoring and analysis based on virtual operation technology can more accurately capture subtle changes in the power grid, accurately reflect the impact of complex business processes and the external environment, provide users with accurate and timely insights, and support decision-making.

[0089] In this embodiment, the work order workflow and intelligent module includes a work order creation and dispatch unit, a work order execution and coordination unit, and a work order tracking and feedback unit.

[0090] The work order creation and assignment unit is responsible for automatically creating and assigning work orders based on operation monitoring indicators and business management needs. Utilizing automated processes and intelligent algorithms, it automatically generates work orders according to preset rules and conditions and assigns them to the corresponding departments or personnel, which can improve work order processing efficiency and response time.

[0091] The work order execution and coordination unit receives and processes work orders assigned by the work order creation and dispatch unit. Through intelligent algorithms and collaboration tools, it guides the execution and coordination of work orders, provides guidance and support for work order execution, coordinates cooperation between different departments, and ensures the timely resolution and completion of work orders.

[0092] The work order tracking and feedback unit is responsible for tracking and providing feedback on the execution of work orders, monitoring the progress and status of work order execution, and updating work order information in a timely manner. At the same time, it can also receive feedback and opinions from relevant personnel so as to adjust and optimize the work order processing flow in a timely manner.

[0093] The work order flow and intelligent module enables efficient work order flow, improving work order processing efficiency and response time. The work order creation and assignment unit automatically generates work orders based on operation and monitoring indicators and business management needs, and assigns them to the corresponding departments or personnel. The work order execution and coordination unit is responsible for guiding the execution of work orders and coordinating cooperation between different departments, while the work order tracking and feedback unit monitors the execution progress and status of work orders, updates information in a timely manner, and receives feedback, which can promote cross-departmental coordination and cooperation in power supply management, and achieve efficient processing and completion of work orders.

[0094] In this embodiment, the root cause analysis module includes a data acquisition unit, a feature extraction and selection unit, a model training and evaluation unit, and a root cause analysis and prediction unit.

[0095] The data acquisition unit is responsible for receiving the required indicator data from historical data and real-time monitoring data from the data integration and sharing module and the real-time monitoring and analysis module, and then cleaning and preprocessing the collected data, including data denoising, missing value filling, and abnormal data removal, to ensure the accuracy and reliability of the data.

[0096] The feature extraction and selection unit, based on big data analysis and machine learning technology, performs feature extraction and selection on the collected indicator data. It uses appropriate feature extraction algorithms to transform the raw data into more meaningful and expressive features, and at the same time uses feature selection algorithms to screen out the most relevant and important features to improve the effectiveness of subsequent analysis.

[0097] The model training and evaluation unit, based on the data from the data acquisition unit and the feature extraction and selection unit, uses machine learning algorithms and model training techniques to establish an indicator root cause analysis model. Based on historical data and real-time monitoring data, the model is trained to identify the root causes of indicator changes and predict future trends. After training, the model is evaluated and validated to ensure its accuracy and stability.

[0098] The root cause analysis and prediction unit uses a trained model to perform root cause analysis and prediction on real-time monitoring data. It can identify the root causes of potential problems leading to changes in indicators and provide in-depth causal relationship analysis. At the same time, through the predictive capabilities of the model, it can predict future trends and potential problems, providing support for pre-emptive control and refined management.

[0099] The root cause analysis module can uncover the causes of potential problems and predict future trends. The data acquisition and cleaning unit is responsible for acquiring and preprocessing data. The feature extraction and selection unit transforms data into meaningful features. The model training and evaluation unit builds the root cause analysis model. The root cause analysis and prediction unit uses the trained model to perform root cause analysis and prediction. These units work together to provide power supply management with in-depth causal relationship analysis and prediction capabilities, helping decision-makers to better understand the power grid operation and make corresponding decisions.

[0100] In this embodiment, the problem analysis and early warning module includes a problem identification and classification unit, an early warning generation and management unit, and a recommendation and optimization unit.

[0101] The problem identification and classification unit, based on big data analysis and intelligent algorithms, identifies and classifies problems in the data of the root cause analysis model. Using appropriate algorithms and models, it analyzes and mines the data, identifies potential problems and anomalies, and classifies and marks them for subsequent early warning and solution provision.

[0102] The early warning generation and management unit generates corresponding early warning information based on the results of the problem identification and classification unit. By establishing flexible models and parameter settings to identify and adapt to the constantly changing operating environment, it can accurately identify and warn of emerging risks, avoid false alarms, and provide targeted solutions. Simultaneously, it is also responsible for the management of early warning information, including the sending, recording, and tracking of early warning notifications.

[0103] The recommendation and optimization unit, based on the results of the problem identification and classification unit, recommends applicable solutions according to different problem types and scenarios, and optimizes the effectiveness and application of the solutions based on feedback and real-time data updates.

[0104] The problem analysis and early warning module can perform problem analysis and early warning, accurately identify and warn of emerging risks, avoid false alarms, and provide targeted solutions, providing targeted guidance and support for power supply management.

[0105] In this embodiment, the virtual power grid model is established by deeply studying the basic principles and components of the power system, including power generation, transmission, and distribution, understanding the working mode and characteristics of each device, and collecting real power system data, including generator capacity and output, load demand, transmission line parameters, transformer turns ratio, and other information. The power system data is used as the input and constraint conditions of the model.

[0106] Based on the characteristics and operating principles of the power system, a mathematical model is established to describe the operation of the power system. Methods such as power flow calculation and optimization algorithms are used to simulate the operating state of the power system. The characteristics of various devices are considered in the model, such as the response speed of generators, the resistance loss of transmission lines, and the efficiency of transformers. These characteristics affect the stability and efficiency of the power system.

[0107] Then, existing optimization algorithms and techniques are used to design and optimize the model to improve its accuracy and computational efficiency. The model is validated using historical data or actual operating data, and the model parameters and algorithms are adjusted to ensure that the model can accurately predict the operation of the power system. The established virtual power grid model is then applied to the actual operation of the power system to monitor and predict the operating status of the power system and provide corresponding decision support.

[0108] Establishing virtual power grid models and applying them to actual power system operations can improve the efficiency, reliability, and flexibility of power systems, provide decision-makers with accurate and comprehensive information and insights, help optimize operations and management, and enhance the overall performance of power systems.

[0109] In this embodiment, the work order creation and dispatch unit utilizes automated processes and intelligent algorithms to automatically generate work orders based on preset rules and conditions. The work order creation and dispatch process includes...

[0110] Monitoring indicator collection: Real-time collection of operational monitoring indicators, such as equipment operating status, temperature, voltage, frequency, and other data, through monitoring equipment;

[0111] The collected monitoring data is processed and analyzed, and technologies such as data mining and machine learning are used to identify anomalies or potential problems and determine whether a work order needs to be generated.

[0112] Rules and conditions are defined based on business management needs and operation monitoring indicators. For example, if the equipment temperature exceeds the threshold or the voltage fluctuation exceeds the limit, these rules and conditions will serve as the basis for triggering the generation of work orders.

[0113] Work order generation is automatically triggered when certain conditions are met, based on preset rules and conditions. Work orders can include information such as work order type, description, priority, and related equipment and personnel information.

[0114] Work order assignment: Based on factors such as work order priority, required skills, and equipment availability, intelligent algorithms automatically assign work orders to suitable maintenance personnel or teams.

[0115] Work order execution and feedback: Maintenance personnel receive work orders and perform corresponding maintenance tasks. During the execution of the work order, updates and records can be made in real time through mobile devices or systems, including information such as work order status, progress, and required materials.

[0116] Work order closure and evaluation: Once a maintenance task is completed, the work order can be closed, and the maintenance quality can be evaluated. The evaluation results can be fed back into the system as a basis for improving work order generation and assignment.

[0117] Automated processes and intelligent algorithms for work order creation and assignment can improve the efficiency, accuracy, and response speed of work order processing, better manage and optimize operation and maintenance processes, improve equipment availability and production efficiency, and provide decision support and improvement basis.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An operation simulation system for power supply management based on virtual operation technology, characterized in that: It includes a data integration and sharing module, which is responsible for integrating and sharing data resources from different departments and systems, solving the problem of data silos, and establishing a unified data storage and management system; The real-time monitoring and analysis module, based on virtual operation technology, monitors and analyzes the operation of the power grid in real time. By simulating the operation of the power grid and operational scenarios, it captures subtle changes and accurately reflects the impact of complex business processes and the external environment. It provides in-depth analysis tools and algorithms to obtain accurate and timely insights and make decisions. The work order circulation and intelligent module uses automated processes and intelligent algorithms to efficiently circulate work orders, improve work order processing efficiency and response time, and promote cross-departmental coordination and cooperation through the close correlation between operation monitoring indicators and business management work orders. The indicator root cause analysis module utilizes big data analytics and machine learning techniques to perform indicator root cause analysis. By analyzing historical and real-time monitoring data, it uncovers potential problems and predicts future trends, providing the ability to discover and predict deep causal relationships. The problem analysis and early warning module, based on big data analysis and intelligent algorithms, performs problem analysis and early warning. By establishing flexible models and parameter settings, it identifies and adapts to the ever-changing operating environment, accurately identifies and warns of emerging risks, and provides targeted solutions.

2. The power supply management operation simulation system based on virtual operation technology according to claim 1, characterized in that: The data integration and sharing module includes a data collection unit, a data storage unit, and a data sharing and interaction unit. The data collection unit collects various data resources from different departments and systems, including operational data, equipment data, and personnel data, and interfaces with various data sources to obtain data in real time. The data storage unit stores and manages the collected data by establishing a unified data storage system. The data sharing and interaction unit shares the acquired real-time data with other modules. By providing data interfaces and services, it enables other modules to obtain the required data on demand and supports data interaction and transmission.

3. The power supply management operation simulation system based on virtual operation technology according to claim 1, characterized in that: The real-time monitoring and analysis module includes a simulated power grid operation unit, a real-time monitoring unit, a change capture unit, and a deep analysis unit. The simulated power grid operation unit establishes a virtual power grid model to simulate the operation and scenarios of the real power grid, such as power load changes, equipment failures, and weather conditions, and accurately reflects the actual operating status of the power grid. The real-time monitoring unit uses monitoring equipment to collect real-time data from various nodes and devices in the power grid. The data includes voltage, current, frequency, and load indicators. The collected data is used to update the virtual power grid model and provided for subsequent analysis. The change capture unit captures subtle changes in the power grid, including voltage fluctuations, frequency deviations, and load anomalies, by comparing and analyzing real-time data with a virtual power grid model, and reflects the impact of complex business processes and the external environment. The deep analysis unit uses deep analysis tools and algorithms to further analyze the captured subtle changes, identify anomalies, and provide corresponding insights and suggestions.

4. The power supply management operation simulation system based on virtual operation technology according to claim 1, characterized in that: The work order flow and intelligent module includes a work order creation and assignment unit, a work order execution and coordination unit, and a work order tracking and feedback unit. The work order creation and assignment unit is responsible for automatically creating and assigning work orders based on operation monitoring indicators and business management needs. It utilizes automated processes and intelligent algorithms to automatically generate work orders according to preset rules and conditions, and assigns the work orders to the corresponding departments or personnel. The work order execution and coordination unit receives and processes work orders assigned by the work order creation and dispatch unit, guides the execution and coordination of work orders through intelligent algorithms and collaboration tools, and provides guidance and support for work order execution. The work order tracking and feedback unit is responsible for tracking and providing feedback on the execution status of work orders, monitoring the execution progress and status of work orders, updating work order information in a timely manner, and receiving feedback and opinions from relevant personnel in order to adjust and optimize the work order processing flow in a timely manner.

5. The power supply management operation simulation system based on virtual operation technology according to claim 1, characterized in that: The root cause analysis module includes a data acquisition unit, a feature extraction and selection unit, a model training and evaluation unit, and a root cause analysis and prediction unit. The data acquisition unit is responsible for receiving the required indicator data from historical data and real-time monitoring data from the data integration and sharing module and the real-time monitoring and analysis module, and then cleaning and preprocessing the collected data. The feature extraction and selection unit, based on big data analysis and machine learning technology, performs feature extraction and selection on the collected indicator data, and uses appropriate feature extraction algorithms to transform the original data into more meaningful and expressive features. The model training and evaluation unit, based on the data from the data acquisition unit and the feature extraction and selection unit, uses machine learning algorithms and model training techniques to establish an indicator root cause analysis model. Based on historical data and real-time monitoring data, the model is trained to identify the root causes of indicator changes and predict future trends. After training, the model is evaluated and validated. The root cause analysis and prediction unit uses a trained model to perform root cause analysis and prediction on real-time monitoring data, identify the root causes of potential problems leading to changes in indicators, and provide in-depth causal relationship analysis. Through the predictive capabilities of the model, it predicts future trends and potential problems.

6. The power supply management operation simulation system based on virtual operation technology according to claim 1, characterized in that: The problem analysis and early warning module includes a problem identification and classification unit, an early warning generation and management unit, and a recommendation and optimization unit. The problem identification and classification unit, based on big data analysis and intelligent algorithms, identifies and classifies problems in the data of the indicator root cause analysis model. Using appropriate algorithms and models, it analyzes and mines the data, identifies potential problems and anomalies, and classifies and marks them for subsequent early warning and solution provision. The early warning generation and management unit generates corresponding early warning information based on the results of the problem identification and classification unit. By establishing flexible models and parameter settings, it can identify and adapt to the constantly changing operating environment, and accurately identify and warn of emerging risks. The recommendation and optimization unit, based on the results of the problem identification and classification unit, recommends applicable solutions according to different problem types and scenarios, and optimizes the effectiveness and application of the solutions based on feedback and real-time data updates.

7. The power supply management operation simulation system based on virtual operation technology according to claim 3, characterized in that: The process involves establishing a virtual power grid model by collecting real power system data, including generator capacity and output, load demand, transmission line parameters, and transformer turns ratio information, using the power system data as input and constraints for the model. Based on the characteristics and operating principles of the power system, a mathematical model is established to describe the operation of the power system. Methods such as power flow calculation and optimization algorithms are used to simulate the operating state of the power system. The characteristics of various devices are considered in the model, such as the response speed of generators, the resistance loss of transmission lines, and the efficiency of transformers. By utilizing existing optimization algorithms and techniques, the model can be designed and optimized. Historical data or actual operating data can be used to verify the model. By adjusting the model parameters and algorithms, the operating conditions of the power system can be accurately predicted. The established virtual power grid model can be applied to the actual operation of the power system to monitor and predict the operating status of the power system and provide corresponding decision support.

8. The power supply management operation simulation system based on virtual operation technology according to claim 4, characterized in that: The work order creation and dispatch unit utilizes automated processes and intelligent algorithms to automatically generate work orders based on preset rules and conditions. The work order creation and dispatch process includes: Monitoring indicator collection: Real-time collection of operational monitoring indicators, such as equipment operating status, temperature, voltage, and frequency data, through monitoring equipment; The collected monitoring data is processed and analyzed to identify anomalies or potential problems and determine whether a work order needs to be generated. Rules and conditions are defined based on business management needs and operation monitoring indicators. For example, if the equipment temperature exceeds the threshold or the voltage fluctuation exceeds the limit, these rules and conditions will serve as the basis for triggering the generation of work orders. Work order generation is automatically triggered when certain conditions are met, based on preset rules and conditions. Work orders include work order type, description, priority information, and related equipment and personnel information. Work order assignment: Based on the priority of the work order, intelligent algorithms are used to automatically assign the work order to the appropriate maintenance personnel or team. Work order execution and feedback: Maintenance personnel receive work orders and perform corresponding maintenance tasks. During the execution of work orders, updates and records are made in real time through mobile devices or systems. Work order closure and evaluation: Once the maintenance task is completed, the work order is closed, and the maintenance quality is evaluated. The evaluation results are fed back into the system as a basis for improving work order generation and assignment.

Images