Real-time data-component collaborative scheduling system of power grid digital twin platform

Abstract

This invention discloses a real-time data-component collaborative scheduling system for a power grid digital twin platform, belonging to the field of power grid data scheduling technology. This invention achieves full coverage of multiple data sources through multi-protocol adaptation, and generates high-quality standardized datasets and fused data through multi-dimensional classification, differential verification, and hierarchical standardization processing. This supports the application of a dynamic single-map of the power grid, constructs a scenario-based scheduling strategy and component capability feature library, and achieves precise matching of data and components and intelligent collaborative scheduling across multiple scenarios. It also creates a full-link monitoring and closed-loop optimization mechanism, combined with intelligent dynamic link switching and emergency response, to ensure continuous and efficient data transmission. Furthermore, it achieves deep integration of data, components, and business scenarios, significantly improving platform scheduling efficiency, response speed, and reliability, strengthening the fusion capability of multi-source power grid data, breaking down data source barriers, and enabling the linkage analysis of multiple types of data such as metering, scheduling, environment, and users, providing a scientific basis for power grid planning and electricity consumption optimization.

Description

Technical Field

[0001] This invention relates to the field of power grid data scheduling technology, and in particular to a real-time data-component collaborative scheduling system for a power grid digital twin platform. Background Technology

[0002] Existing power grid-related digital platforms already possess basic data access, component management, and visualization capabilities. For example, some platforms can collect and display real-time data from the main grid and distribution network, such as trip alarms, power, and voltage. They provide basic tools and components for map layer control and equipment query and location, and support the construction of simple applications using low-code methods. Specifically, the data access layer can interface with multi-source data from dispatching, metering, and the Internet of Things (IoT); the component layer includes basic interactive components, chart components, and power grid-specific components; and the application layer allows for the rapid construction of visualization pages using templates.

[0003] Currently, in the construction of the power grid digital twin platform, the following problems still exist in real-time data-component collaborative scheduling:

[0004] 1. In the existing platform, data updates and component rendering are independent of each other and lack a dynamic coordination mechanism. As a result, when real-time data, such as the remote signaling status of power grid equipment and meteorological data, changes, the components cannot respond and update quickly, resulting in data display delays and affecting the timeliness of scheduling decisions.

[0005] 2. The component management lacks unified collaborative scheduling rules, and the component management and adaptation mechanism is imperfect. The existing platform's classification and management of various digital twin components such as basic components and electrical special components is relatively crude. It has not established a precise component capability characteristic system, and it is impossible to clearly define the data requirements, processing capabilities and scenario adaptation scope of the components. This results in a large degree of blindness in component invocation, low data and component adaptation accuracy, and insufficient utilization of component resources.

[0006] 3. The collaborative scheduling strategy lacks scenario-based adaptation. Existing scheduling solutions mostly adopt a single scheduling logic and do not formulate differentiated strategies for the urgency differences of different business scenarios such as routine operation monitoring, fault alarm, and meteorological disaster response. In critical scenarios, the component calling priority is unclear, which is prone to response delays. At the same time, the communication link operation quality lacks dynamic monitoring and intelligent switching mechanisms, making it difficult to ensure the continuity of high real-time data transmission. Summary of the Invention

[0007] The purpose of this invention is to provide a real-time data-component collaborative scheduling system for a power grid digital twin platform to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The real-time data-component collaborative scheduling system of the power grid digital twin platform includes:

[0010] The data access control module is used to collect power grid operation monitoring data in real time, and to perform data verification and preprocessing on the power grid operation monitoring data to generate standardized operation monitoring datasets.

[0011] The component management module is used to acquire various types of power grid digital twin components and component basic data, classify and manage the categories of power grid digital twin components based on business scenarios, build a capability characteristic database for each type of component, and determine the component capability characteristics by combining standardized operation monitoring datasets.

[0012] The collaborative scheduling module is used to pre-set multi-scenario scheduling strategies based on power grid business needs, clarify the component call priority, linkage logic, and response time limit under different scenarios, monitor the data changes in the real-time data center and the triggering conditions of business scenarios in real time, match and adapt target components based on standardized operation monitoring datasets and component capability characteristics, and perform collaborative scheduling of each component based on scheduling strategies.

[0013] The scheduling monitoring and feedback module is used to monitor the running status data of each component in real time during the scheduling process, build a visual monitoring panel, detect scheduling anomalies and trigger alarms, and at the same time, statistically analyze scheduling data, evaluate scheduling efficiency and collaborative effect, and provide corresponding optimization suggestions to each module.

[0014] Furthermore, the data access control module specifically includes:

[0015] The data classification unit is used to label the collected power grid operation monitoring data with data source tags based on the data source identifier dimension, classify the equipment types, match the target business scenario tags according to the classification results, and classify the real-time level of the power grid operation monitoring data.

[0016] The quality verification unit is used to preset differentiated verification rules based on the equipment type and real-time level of different data types, perform quality verification on the power grid operation monitoring data based on the differentiated verification rules, and classify and label the power grid operation monitoring data according to the verification results.

[0017] The data processing unit is used to classify and process power grid operation monitoring data based on the quality grading labeling results. Combining the data real-time level and business scenario labels, it performs standardization transformation on the processed power grid operation monitoring data to generate a standardized operation monitoring dataset.

[0018] Furthermore, the data access control module is also used to obtain metering data from power grid metering equipment, extract scheduling dynamic data from power grid dispatching system, obtain multi-state power grid data based on power grid twin model, and integrate metering data, scheduling dynamic data and multi-state power grid data to form fused data.

[0019] Furthermore, the specific process of the data processing unit performing standardization conversion includes:

[0020] Based on the quality grading and labeling results, power grid operation monitoring data corresponding to high-quality data labels, usable data labels, low-quality data labels, and invalid data labels are extracted respectively;

[0021] High-quality data is cleaned in a lightweight manner based on high-quality data labeling, standard format conversion is performed based on preset standard format, and transmission priority is determined according to real-time level.

[0022] Based on the available data tags, the available data is corrected for deviations and data normalization is performed. After completing the standardized field mapping, business scenario tags are obtained, and the range of applicable component types is determined based on the business scenario tags.

[0023] Based on low-quality data labeling, the low-quality data is assessed for defects, incompleteness and usability through historical correlation data. If it meets the business scenario requirements, it is standardized and transformed and marked as usable after repair. If it does not meet the requirements, it is downgraded to data to be investigated.

[0024] Based on invalid data marking, invalid data is stored in the abnormal data storage area for data isolation, core abnormal features are extracted, and abnormal data logs are generated; at the same time, the source of invalid data, collection time, and reason for verification failure are recorded.

[0025] Furthermore, the component management module includes:

[0026] The component classification unit is used to classify components by combining component functional attributes, data requirement characteristics and power grid business scenario types, and to label the scenario adaptation tags of various power grid digital twin components to build a component classification index.

[0027] The capability feature construction unit is used to obtain component classification results and basic component data, associate field information and data types of standardized operation monitoring datasets, extract component target capability parameters, i.e. core capability features, and establish capability feature description models and capability feature databases.

[0028] The adaptation mapping unit is used to obtain field information, data types, and business scenario tags of the standardized operation monitoring dataset, extract key parameters from the component capability feature library, establish adaptation mapping relationships by matching data attributes with component capability features, and generate a component-data adaptation relationship table.

[0029] Furthermore, the adaptation mapping unit also includes:

[0030] Based on the standardized operation monitoring dataset, extract the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data, and sort the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data to form a data feature vector;

[0031] At the same time, the upper limit of the real-time level of data that the current candidate component can receive, the expected value of the data volume level, and the upper limit of the total number of dimensions of the supported data parameter fields are extracted from the component capability feature database and used to form the component requirement vector. The data feature vector and the component requirement vector are both located in the same three-dimensional feature space.

[0032] Based on the data feature vector and component demand vector, calculate the degree of directional matching between the actual characteristics of the currently scheduled data and the expected demands of the components:

[0033] Obtain the urgency weight coefficient of the business scenario to which the data to be scheduled belongs, as well as the number of times the current candidate component has been successfully called and the total number of calls in the same business scenario;

[0034] The dynamic adaptability between the current data to be scheduled and the candidate components is calculated based on the degree of matching, the urgency weighting coefficient of the business scenario, and the number of times the current candidate component has been successfully called in the same business scenario and the total number of calls:

[0035] Write the calculated dynamic adaptability into the adaptability priority field associated with the current data to be scheduled and the current candidate component in the component-data adaptability relationship table, and determine the value of the adaptability priority field based on the value of the dynamic adaptability.

[0036] When generating the component call list, the adaptation priority field of all candidate components in the same business scenario in the component-data adaptation relationship table is read, and the call order of each candidate component is arranged in descending order of the adaptation priority field value to form the component call list.

[0037] Furthermore, the collaborative scheduling module also includes:

[0038] The strategy construction unit allows users to obtain various business requirements and scenario characteristics of the power grid, preset multi-scenario scheduling strategies, and build a scheduling strategy library.

[0039] The component matching unit is used to obtain the standardized operation monitoring dataset of the real-time data center, extract the corresponding business scenario tags and real-time level, and combine the component-data adaptation relationship table and scheduling strategy library to match the target component and generate a component call list.

[0040] The collaborative operation unit is used to establish a unified interaction protocol, send standardized operation monitoring datasets and operation instructions to target components in the component call list, and drive the power grid dynamic map associated components to operate collaboratively according to preset logic.

[0041] The abnormal switching unit is used to monitor the link operation quality between the standardized operation monitoring dataset transmission node and the target component receiving node in real time. It obtains the link operation quality based on the link operation parameters, and switches the communication link when the link operation quality is lower than the preset quality standard.

[0042] Furthermore, the abnormal switching unit performs communication link switching, specifically as follows:

[0043] Real-time monitoring of the link operation parameters between the standardized operation monitoring dataset transmission node and the target component receiving node, including transmission delay, data packet loss rate, and bandwidth stability;

[0044] Simultaneously, the real-time level and target component type of the currently transmitted standardized operation monitoring dataset are extracted, and the link operation quality score between the standardized operation monitoring dataset transmission node and the target component receiving node is obtained.

[0045] When the link operation quality score is lower than the preset quality standard, the corresponding switching trigger logic is matched based on the real-time level. The high real-time level will start the switching immediately, while the ordinary real-time level will start after multiple cycles of confirmation.

[0046] Based on historical handover success rates and the preset number of link and component type compatibility, a backup link priority pre-configuration table is generated. Based on the backup link priority pre-configuration table, backup communication links are retrieved sequentially, and the link operation parameters of each backup communication link and its compatibility with the current data and components are checked.

[0047] Based on the link operation parameters and adaptation matching degree obtained from the test, and combined with the current data real-time level weight, the overall operation quality of each backup communication link is ranked, and a comprehensive ranking result is generated.

[0048] After the switchover is completed, continuously monitor the operational quality of the new link and record the switchover effect indicators, update the backup link priority pre-configuration table, and adjust the call priority of each backup link;

[0049] If the link still fails to meet the requirements after switching, a multi-level alarm will be triggered and a dedicated emergency communication link for high real-time data will be invoked.

[0050] Furthermore, the abnormal switching unit performs communication link switching, which also includes:

[0051] Based on a preset period, the system obtains multiple consecutive historical values ​​of transmission delay, data packet loss rate, and bandwidth stability of each communication link from the link operation parameters between the transmission node and the target component receiving node in the standardized operation monitoring dataset monitored in real time.

[0052] Based on the chronological order of acquisition time, multiple consecutive historical records of each link are arranged to obtain the time series corresponding to the operating parameters of each link. Each historical record value is associated with a corresponding acquisition cycle number.

[0053] It is difficult to extract the last collected target historical record value from the time series and set the target historical record value as the baseline value;

[0054] The difference between two historical records corresponding to adjacent collection period numbers in the time series is determined, and the differences are arranged to obtain a difference sequence. The average difference in the difference sequence is then averaged to obtain the average change amplitude per unit period.

[0055] Multiply the average change amplitude per unit period by the preset prediction time advance to obtain the prediction change offset, and sum the baseline value and the prediction change offset to obtain the prediction trend value corresponding to each communication link under the prediction time advance.

[0056] At the same time, the current link operation quality score is obtained based on the link operation parameters monitored in real time.

[0057] When the predicted trend value indicates that the current communication link's operational quality will decline to below the preset quality standard within a preset time interval, and the current communication link's operational quality score is still above the preset quality standard, a preventive handover preparation process is triggered, and the current communication link is marked as a link to be switched.

[0058] Based on the triggering result, the real-time level and target component type of the standardized operation monitoring dataset currently being transmitted are extracted. Combined with the pre-configured backup link priority pre-configuration table, a pre-selected backup link with the same transmission node and receiving node as the link to be switched, and which is compatible with the current real-time level and component type is selected. Among them, there is at least one pre-selected backup link.

[0059] Send a link quality detection command to the pre-selected backup link to obtain the real-time transmission delay, data packet loss rate and bandwidth stability parameters of the pre-selected backup link, and determine the pre-switching operation quality score of each pre-selected backup link by combining the real-time level weight of the current data.

[0060] When the difference between the current link operation quality score and the pre-switch operation quality score of the link to be switched reaches the preset switching benefit threshold, and before the operation quality of the link to be switched drops to the preset quality standard, the transmission link of the standardized operation monitoring dataset is switched to the pre-selected backup link with the highest pre-switch operation quality score, and the original link to be switched is kept in hot standby status.

[0061] Furthermore, the scheduling monitoring feedback module includes:

[0062] The scheduling status monitoring unit is used to associate the standardized operation monitoring dataset, the component capability feature database and the component-data adaptation relationship table, the component call list and link operation parameters, integrate and generate key scheduling indicators, and build a multi-dimensional visual monitoring panel.

[0063] The scheduling performance analysis unit is used to perform performance analysis based on key scheduling indicators and the characteristics of power grid business scenarios. It analyzes the synergistic effect of metering data, scheduling dynamic data, and multi-state power grid data linkage in the context of multi-data fusion of a dynamic power grid map, and identifies key problems and their causes.

[0064] The optimization and adjustment unit is used to generate corresponding optimization schemes based on performance analysis results, and optimize the multi-data fusion scheduling logic and component linkage rules in combination with the power grid dynamic map scenario.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] 1. This invention achieves integrated access to all types of power grid data sources through multi-protocol adaptation. Combining multi-dimensional data classification, differentiated quality verification, and hierarchical standardization processing, it enables precise data cleaning, deviation correction, and format normalization. At the same time, it integrates metering, dispatch dynamics, and multi-mode network data to form fused data assets. This not only ensures the accuracy, integrity, and real-time hierarchical adaptation capabilities of the data, but also breaks down traditional data silos, laying a solid data foundation for precise linkage between data and components and multi-data fusion analysis. Furthermore, by pre-associating data with business scenarios, it significantly improves the efficiency and accuracy of subsequent component matching.

[0067] 2. This invention establishes a precise mapping between data requirements and component capabilities through a component capability feature database and a component-data adaptation relationship table. Combined with a multi-scenario scheduling strategy, it clarifies the component call priority and linkage logic under different business scenarios, realizing rapid matching and collaborative operation of data and components in scenarios such as routine monitoring, fault alarm, and meteorological disaster response. Furthermore, by prioritizing the transmission of high real-time data and prioritizing the invocation of core components in fault alarm scenarios, coupled with dynamic link quality monitoring and intelligent switching mechanisms, it ensures the continuity of critical business data transmission and the timeliness of component response, significantly improving the response speed in emergency scenarios such as power grid fault handling and meteorological disaster early warning. At the same time, through multi-data fusion scheduling and component linkage of a dynamic power grid map, it realizes collaborative analysis and visualization of multi-dimensional data, strengthening the global perception capability of power grid operation status.

[0068] 3. This invention utilizes a scheduling monitoring and feedback module to achieve visualized monitoring and quantitative analysis of core indicators throughout the entire process of data access, component invocation, and link operation. This accurately identifies issues such as unreasonable data verification rules, component adaptation deviations, incomplete scheduling strategies, and unsmooth link switching. Based on the analysis results, targeted optimization suggestions are pushed to relevant modules, enabling dynamic updates and collaborative optimization of data verification rules, component capability characteristics, scheduling strategies, and link switching rules. This ensures the system can continuously adapt to the dynamic changes in power grid business scenarios, constantly improving data-component adaptation accuracy, scheduling efficiency, and system operational stability.

[0069] 4. This invention transforms multi-source data resources into effective capabilities to support core businesses such as power grid operation monitoring, fault alarms, weather warnings, and electricity consumption analysis, thereby improving the digitalization and intelligence level of power grid operation management. Through efficient collaboration and precise control across the entire process, it provides power grid dispatchers with comprehensive, intuitive, and real-time decision support, effectively reducing fault handling time, improving the accuracy of meteorological disaster response, and optimizing the scientific nature of electricity consumption analysis. Ultimately, it ensures the safe and stable operation of the power grid and provides core technological support for the construction and development of smart grids. Attached Figure Description

[0070] Figure 1 This is a block diagram of the real-time data-component collaborative scheduling system of the power grid digital twin platform of the present invention;

[0071] Figure 2 This is a flowchart of the real-time data-component collaborative scheduling system of the power grid digital twin platform of the present invention. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Please see Figures 1-2 The present invention provides the following technical solutions:

[0074] The real-time data-component collaborative scheduling system of the power grid digital twin platform includes:

[0075] The data access and management module is used to connect to data sources such as the main network OCS, distribution network OCS, metering system, IoT platform, and meteorological data service based on a multi-protocol adaptation mechanism. It collects power grid operation monitoring data such as power grid equipment operating parameters, alarm information, meteorological monitoring data, and user electricity consumption data in real time through streaming transmission. It also performs data verification and preprocessing on the power grid operation monitoring data to generate a standardized operation monitoring dataset.

[0076] The data access and management module is also used to integrate metering data, dispatch dynamic data, and multi-state power grid data. The metering data includes measured data such as power consumption, voltage, current, and phase angle. The dispatch dynamic data includes main grid / distribution network tripping alarms, SOE / COS data, and real-time operation data of main transformers / buses / switches. The multi-state power grid data includes power grid structure data in operation, historical, and planned states, forming integrated data to support a dynamic map of the power grid. This data is used in the following scenarios: linkage analysis of real-time operation data of the main grid / distribution network and metering data, including collaborative display of distribution transformer power / voltage / current curves and dispatch switch data; comparative display and traceability of power grid structure data in operation, historical, and planned states; and integrated early warning analysis of meteorological data such as typhoons and icing with dynamic power grid data.

[0077] The component management module is used to acquire various digital twin components of the power grid, such as basic components, electrical thematic components, power grid components, meteorological thematic components, analysis components, and visualization components, as well as basic component data. The basic component data includes component name, function description, data requirements, and adaptation scenarios. Based on each component category, it is classified and managed according to business scenarios, including power grid operation monitoring, fault alarm, weather warning, and electricity consumption analysis. Based on the basic component data and classification results, a capability characteristic database for each type of component is constructed. Combined with standardized operation monitoring datasets, the target capability parameters such as data type, data processing capability, output display format, and response latency threshold of each component are clarified, and the component capability characteristics are determined.

[0078] The collaborative scheduling module is used to pre-set multi-scenario scheduling strategies based on power grid business needs, including routine operation monitoring strategies, fault alarm response strategies, and meteorological disaster response strategies. It clarifies the component call priority, linkage logic, and response time limit under different scenarios. For example, in the fault alarm scenario, the alarm display component has a higher priority than the statistical analysis component and should be triggered first. It monitors the data changes in the real-time data center and the triggering conditions of business scenarios in real time. Based on the standardized operation monitoring dataset and component capability characteristics, it matches and adapts the target components and realizes component collaborative scheduling according to the scheduling strategy, so as to achieve accurate linkage and efficient collaboration between real-time data and components in the power grid digital twin platform.

[0079] The scheduling monitoring and feedback module is used to monitor the operational status data of each component in real time during the scheduling process, build a visual monitoring panel, and connect with the component management module to achieve full-link traceability of basic information; it sets anomaly thresholds, monitors scheduling anomalies and triggers alarms, pushes them to the collaborative scheduling module to trigger the handling process, and synchronizes abnormal data to the data access control module to assist in troubleshooting; it statistically analyzes scheduling data, evaluates scheduling efficiency and collaborative effect, and provides targeted optimization suggestions to each module.

[0080] In this embodiment, a multi-protocol adapted streaming data acquisition mechanism is used to achieve comprehensive connection with multiple data sources such as the main grid OCS, distribution grid OCS, and metering system. Combined with deep fusion technology of metering data, scheduling dynamic data, and multi-state power grid network data, high-quality fused data supporting a dynamic single map of the power grid is generated. A dynamically adapted component capability feature database is constructed to achieve accurate matching between data and components. Multi-scenario differentiated scheduling strategies are preset, and the component call priority and linkage logic are dynamically adjusted according to the urgency of business scenarios to achieve intelligent scheduling in different scenarios such as routine monitoring, fault alarm, and meteorological disaster response. Especially in emergency scenarios, priority control enables rapid response of core components, achieving seamless linkage and efficient collaboration between real-time data and various components, and significantly improving the operating efficiency and emergency response capabilities of the power grid digital twin platform.

[0081] In this embodiment, the data access control module specifically includes:

[0082] The data classification unit is used to label collected data with data source tags such as main grid OCS, distribution network OCS, metering system, IoT platform, and meteorological data service based on the data source identifier dimension. It further subdivides equipment types according to data type dimension, including operating parameters such as voltage, current, and power; alarm events such as tripping alarms and terminal alarms; environmental perception such as temperature, humidity, and typhoon path; and user behavior such as electricity consumption and electricity usage time. Each type is further subdivided into subcategories. Based on the business scenario dimension, it automatically matches target business scenario tags such as power grid operation monitoring, fault alarms, meteorological warnings, and electricity consumption analysis according to the data's intended use. Based on real-time requirements and the urgency of the business scenario, it classifies power grid operation monitoring data into millisecond, second, minute, and hourly real-time levels.

[0083] The quality verification unit is used for different data types and real-time levels of equipment. It presets differentiated verification rules and marks the power grid operation monitoring data according to the verification results. This includes high-quality data that fully complies with the verification rules, usable data with slight deviations but not affecting use, low-quality data with some key defects, and invalid data with serious anomalies or missing core information.

[0084] The data processing unit is used to perform hierarchical processing based on the quality grading label results, and to perform standardized transformation by combining the data real-time level and business scenario labels, ultimately generating a standardized operation monitoring dataset.

[0085] In this embodiment, the specific process of the data processing unit performing standardization conversion includes:

[0086] Based on high-quality data labeling, lightweight cleaning is performed to filter redundant placeholders and duplicate data. Based on preset standard formats, field normalization mapping is completed to preserve the original data accuracy. Based on the real-time level, the transmission priority is determined.

[0087] Based on available data tags, a preset deviation correction algorithm is invoked to correct minor deviations, compensate for data drift and timestamp deviations, remove invalid fields, supplement missing non-core attribute fields, and complete standardized field mapping; business scenario tags are obtained, associated with component data requirements, and the applicable component type range is marked;

[0088] Based on low-quality data labeling, obtain historical related data from the same device and time period to identify key defects and complete the defects; for fields that cannot be completed, mark the defect type and scope of impact, conduct a usability assessment after repair, and if the business scenario requirements are met, perform standardized transformation and mark the usable attributes after repair; if not, downgrade to data to be investigated.

[0089] Based on invalid data marking, invalid data is stored in the abnormal data storage area for data isolation, core abnormal features are extracted, abnormal data logs are generated, and the logs are synchronized to the abnormal statistics unit of the data access control module and the scheduling monitoring feedback module; at the same time, the data source access status verification process is triggered to record the source of invalid data, collection time, and reason for verification failure.

[0090] In this embodiment, a multi-dimensional collaborative data classification system is constructed to achieve precise data positioning and differentiated management. A differentiated verification strategy is adopted, and targeted verification rules are preset based on data type, device type, and real-time level. At the same time, a four-level quality grading and labeling system is innovatively constructed to accurately define and classify high-quality, usable, low-quality, and invalid data, thereby achieving refined management of data quality. This ensures the purity of high-quality data while maximizing the value of usable data. Differentiated processing procedures are designed for data of different quality levels to meet the needs of different real-time requirements and different business scenarios.

[0091] In this embodiment, the component management module includes:

[0092] The component classification unit is used to acquire various power grid digital twin components and their basic data. Based on the component's functional attributes and data requirement characteristics, combined with the power grid business scenario type, the components are classified and categorized. The scenario adaptation tags of various power grid digital twin components are labeled, and a component classification index containing component category, scenario adaptation tags, and component identifier is constructed to enable rapid retrieval of components by category and scenario, providing practical support for component classification management.

[0093] The capability feature construction unit is used to obtain component classification results and basic component data, associate field information and data types of standardized operation monitoring datasets, clarify the target capability parameters such as data type adaptation range, data processing capability boundaries, output display format specifications and response latency thresholds of each component, extract the core capability features of various components, establish a capability feature description model, build a capability feature database for each type of component based on the capability feature description model, and realize the dynamic updating and maintenance of capability features.

[0094] The adaptation mapping unit is used to obtain field information, data types, and business scenario tags of the standardized operation monitoring dataset, extract parameters such as component data requirements and response latency thresholds from the component capability feature library, establish an adaptation mapping relationship between standardized operation monitoring and components by matching data attributes and component capability features, and generate a component-data adaptation relationship table containing data types, business scenarios, component identifiers, and adaptation priorities to support component matching and invocation of the collaborative scheduling module.

[0095] In this embodiment, the collaborative scheduling module further includes:

[0096] The strategy building unit allows users to obtain various business needs and scenario characteristics of the power grid, preset scheduling strategies for multiple scenarios such as routine operation monitoring, fault alarm response, and meteorological disaster response, clarify the component call priority, linkage logic and response time parameters under each scenario, and build a scheduling strategy library containing scenario identifiers, strategy rules and priority configurations, supporting dynamic updates and calls of strategies;

[0097] The component matching unit is used to obtain the standardized operation monitoring dataset of the real-time data center, extract the business scenario tags and real-time level corresponding to the data, combine the component-data adaptation relationship table and the scheduling strategy library, match the target components that meet the data requirements and business scenario requirements of the standardized operation monitoring dataset, sort the component call order according to the component call priority specified in the scheduling strategy library, and generate a component call list containing component identifier, call order and running parameters.

[0098] The collaborative operation unit is used to establish a unified interaction protocol. Through the unified interaction protocol, it sends standardized operation monitoring datasets and operation instructions to target components in the component call list, driving the power grid dynamic map associated components to operate collaboratively according to preset logic. The power grid dynamic map associated components include power grid operation monitoring components, multi-state network structure display components, and metering-dispatch data linkage analysis components, realizing real-time linkage display and analysis of metering data, dispatch dynamic data, and multi-state data in a single map, and achieving efficient communication between real-time data and components.

[0099] The abnormal switching unit is used to monitor the link operation quality between the standardized operation monitoring dataset transmission node and the target component receiving node in real time. It obtains the link operation quality by collecting link operation parameters such as link transmission delay and data packet loss rate. When the link operation quality is lower than the preset quality standard, the communication link is switched.

[0100] In this embodiment, the abnormal handover unit performs communication link switching, as follows:

[0101] Real-time monitoring of the link operation parameters between the standardized operation monitoring dataset transmission node and the target component receiving node, including transmission delay, data packet loss rate, and bandwidth stability;

[0102] Simultaneously, the real-time level of the currently transmitted standardized operation monitoring dataset is extracted. The real-time level is determined based on the data type, such as high real-time level for fault alarm data and ordinary real-time level for regular statistical data, as well as the type of the target component, such as alarm display component and metering-scheduling data linkage analysis component. The link operation quality score between the transmission node of the standardized operation monitoring dataset and the receiving node of the target component is calculated by weighting the link operation parameters and business attributes.

[0103] When the link operation quality score is lower than the preset quality standard, the real-time level of the current transmitted data is first determined. If it is a high real-time level, such as the main network trip alarm data, the switching process is started immediately. If it is a normal real-time level, the system is continuously monitored for three unit cycles to confirm that the substandard quality status is stable.

[0104] Based on historical handover success rates and preset numbers for link and component type compatibility, a backup link priority pre-configuration table is generated. Based on the backup link priority pre-configuration table, backup communication links are retrieved sequentially, and the system switches to each backup communication link one by one according to the set operating time interval. The link operating parameters of each backup communication link and its compatibility with the current data and components are checked simultaneously.

[0105] Based on the link operation parameters and adaptation matching degree obtained from the test, combined with the current data real-time level weight, each backup communication link is ranked in terms of comprehensive operation quality, and a comprehensive ranking result is generated. High real-time data is given priority weight for transmission delay, and ordinary real-time data is given priority weight for bandwidth stability. The communication link between the transmission node of the standardized operation monitoring dataset and the receiving node of the target component is switched to the backup communication link with the highest comprehensive operation quality.

[0106] After the switchover is completed, the operating quality of the new link is continuously monitored for three unit cycles. Switchover performance indicators such as switchover time, data transmission integrity after the switchover, and target component response efficiency are recorded and updated to the backup link priority pre-configuration table to dynamically adjust the calling priority of each backup link.

[0107] If the link still fails to meet the standards after the switch, multi-level alarms will be triggered and a dedicated emergency communication link for high real-time data will be invoked to ensure continuous and efficient communication between the standardized operation monitoring dataset and the target components, and to adapt to the differentiated needs of power grid services.

[0108] In this embodiment, a weighted score is calculated based on link operation parameters, data real-time level, and component type to accurately determine link operation quality. Differentiated switching trigger logic is designed for high real-time and ordinary real-time data to avoid blind switching and response delays. Simultaneously, by pre-configuring a backup link priority table and comprehensively evaluating backup link quality from multiple dimensions, optimal link switching is achieved. Continuous monitoring and indicator updates after switching dynamically optimize backup link priorities, ensuring continuous and efficient transmission of high real-time data. This enables efficient collaboration of real-time data and components in different business scenarios, stable and controllable link operation, and rapid and accurate anomaly handling. It significantly improves the scheduling efficiency, collaboration capabilities, and emergency support level of the power grid digital twin platform, providing full-process intelligent support for power grid operation monitoring, fault alarms, and meteorological disaster response.

[0109] In this embodiment, the scheduling monitoring feedback module includes:

[0110] The scheduling status monitoring unit is used to associate standardized operation monitoring datasets, component capability characteristic databases and component-data adaptation relationship tables, component call lists and link operation parameters, integrate and generate core scheduling indicators, build a multi-dimensional visual monitoring panel, support filtering and viewing by business scenario, data source type, real-time level, and city scope, and associate with the power grid dynamics to display the scheduling status and data linkage effect in a single map.

[0111] The core scheduling metrics include data access latency, component call success rate, link operation quality, component response efficiency, and data-component adaptation accuracy. Data access latency is further subdivided into millisecond / second / minute / hour latency data according to real-time level. Component call success rate is statistically classified by component category and business scenario. Link operation quality includes transmission latency, data packet loss rate, and bandwidth stability. Component response efficiency is the matching response latency threshold.

[0112] The scheduling performance analysis unit is used to perform performance analysis based on core scheduling indicators and the characteristics of power grid business scenarios, such as the urgency differences in scenarios like fault alarms and meteorological disaster response. This includes analyzing the access processing efficiency and component response matching degree of data at different real-time levels, determining whether data preprocessing rules are adapted to business requirements, analyzing the rationality of component call priorities and linkage logic, evaluating the timeliness of component scheduling in key scenarios such as fault alarms, analyzing the link operation quality and the adaptation relationship between data types and component types, and locating the link switching efficiency bottleneck of the abnormal switching unit. In conjunction with the multi-data fusion scenario of the power grid dynamic map, it analyzes the synergistic effect of metering-scheduling data and multi-state data linkage, and identifies core problems and causes such as insufficient data fusion and unsmooth component linkage.

[0113] The optimization and adjustment unit is used to generate differentiated optimization schemes and form closed-loop optimization based on performance analysis results. This includes feeding back data verification rules and optimization suggestions for standardized conversion algorithms to the data access control module, such as adjusting the deviation correction strategy for high real-time data and supplementing the defect completion dimensions of low-quality data; pushing component capability feature update suggestions to the component management module, such as optimizing the component data requirement adaptation range, adjusting the response latency threshold, and synchronously updating the component-data adaptation relationship table; feeding back scheduling strategy optimization parameters to the collaborative scheduling module, such as adjusting the component call priority in key scenarios and optimizing the backup link priority pre-configuration table, and link switching rules in conjunction with the abnormal switching unit, such as adjusting the switching trigger conditions for high real-time data; and optimizing the multi-data fusion scheduling logic and component linkage rules based on the power grid dynamic map scenario, ensuring that after the optimization parameters are synchronized to each related module, the scheduling efficiency, data-component adaptation accuracy, and power grid business scenario adaptability are continuously improved.

[0114] This embodiment provides a real-time data-component collaborative scheduling system for a power grid digital twin platform. The abnormal switching unit performs communication link switching and further includes:

[0115] Based on a preset period, the system obtains multiple consecutive historical values ​​of transmission delay, data packet loss rate, and bandwidth stability of each communication link from the link operation parameters between the transmission node and the target component receiving node in the standardized operation monitoring dataset monitored in real time.

[0116] Based on the chronological order of acquisition time, multiple consecutive historical records of each link are arranged to obtain the time series corresponding to the operating parameters of each link. Each historical record value is associated with a corresponding acquisition cycle number.

[0117] It is difficult to extract the last collected target historical record value from the time series and set the target historical record value as the baseline value;

[0118] The difference between two historical records corresponding to adjacent collection period numbers in the time series is determined, and the differences are arranged to obtain a difference sequence. The average difference in the difference sequence is then averaged to obtain the average change amplitude per unit period.

[0119] Multiply the average change amplitude per unit period by the preset prediction time advance to obtain the prediction change offset, and sum the baseline value and the prediction change offset to obtain the prediction trend value corresponding to each communication link under the prediction time advance.

[0120] At the same time, the current link operation quality score is obtained based on the link operation parameters monitored in real time.

[0121] When the predicted trend value indicates that the current communication link's operational quality will decline to below the preset quality standard within a preset time interval, and the current communication link's operational quality score is still above the preset quality standard, a preventive handover preparation process is triggered, and the current communication link is marked as a link to be switched.

[0122] Based on the triggering result, the real-time level and target component type of the standardized operation monitoring dataset currently being transmitted are extracted. Combined with the pre-configured backup link priority pre-configuration table, a pre-selected backup link with the same transmission node and receiving node as the link to be switched, and which is compatible with the current real-time level and component type is selected. Among them, there is at least one pre-selected backup link.

[0123] Send a link quality detection command to the pre-selected backup link to obtain the real-time transmission delay, data packet loss rate and bandwidth stability parameters of the pre-selected backup link, and determine the pre-switching operation quality score of each pre-selected backup link by combining the real-time level weight of the current data.

[0124] When the difference between the current link operation quality score and the pre-switch operation quality score of the link to be switched reaches the preset switching benefit threshold, and before the operation quality of the link to be switched drops to the preset quality standard, the transmission link of the standardized operation monitoring dataset is switched to the pre-selected backup link with the highest pre-switch operation quality score, and the original link to be switched is kept in hot standby status.

[0125] In this embodiment, the current link operation quality score refers to a value calculated by comprehensively weighting the measured values ​​of transmission delay, data packet loss rate, and bandwidth stability, combined with the real-time level weight. This value is used to quantify the overall operation quality of the communication link within the current detection period.

[0126] The current link operation quality score is obtained based on the link operation parameters monitored in real time, including:

[0127] Real-time acquisition of link operation parameters between the transmission node of the standardized operation monitoring dataset and the receiving node of the target component within the current detection period. The link operation parameters include the measured value of transmission delay, the measured value of data packet loss rate, and the measured value of bandwidth stability. The measured value of bandwidth stability is obtained by calculating the variance after collecting instantaneous bandwidth multiple times within the current detection period.

[0128] The preset reference benchmark values ​​corresponding to the measured values ​​of transmission delay, data packet loss rate, and bandwidth stability are obtained respectively. The preset reference benchmark values ​​include the upper limit threshold of transmission delay, the upper limit threshold of data packet loss rate, and the ideal variance threshold of bandwidth stability.

[0129] The transmission delay degradation coefficient is obtained by comparing the measured value of transmission delay with the upper limit threshold. The packet loss rate degradation coefficient is obtained by comparing the measured value of data packet loss rate with the upper limit threshold. The bandwidth fluctuation coefficient is obtained by comparing the measured value of bandwidth stability with the ideal variance threshold of bandwidth stability.

[0130] The transmission delay quality component, packet loss rate quality component, and bandwidth stability quality component are obtained by taking the reciprocals of the transmission delay degradation coefficient, packet loss rate degradation coefficient, and bandwidth fluctuation coefficient, respectively.

[0131] Based on the real-time level of the current standardized operation monitoring dataset, the preset weight allocation table is retrieved, and the transmission delay weight coefficient, packet loss rate weight coefficient, and bandwidth stability weight coefficient corresponding to the real-time level are matched from the weight allocation table. Among them, the transmission delay weight coefficient corresponding to the high real-time level is higher than the packet loss rate weight coefficient and the bandwidth stability weight coefficient, and the bandwidth stability weight coefficient corresponding to the ordinary real-time level is not lower than the transmission delay weight coefficient.

[0132] The weighted delay quality is obtained by multiplying the transmission delay quality component by the transmission delay weighting coefficient, the weighted packet loss quality is obtained by multiplying the packet loss rate quality component by the packet loss rate weighting coefficient, and the weighted bandwidth quality is obtained by multiplying the bandwidth stability quality component by the bandwidth stability weighting coefficient.

[0133] The weighted latency quality, weighted packet loss quality, and weighted bandwidth quality are added together to obtain the current link operation quality score within the current detection period.

[0134] In this embodiment, the predicted trend value refers to the feature value generated by multiplying the average change amplitude per unit period obtained by time series analysis of multiple consecutive historical values ​​of each link's operating parameters with the predicted time lead and then superimposing the baseline value. This feature value is used to characterize the expected value of the link's operating parameters at a specific future time.

[0135] In this embodiment, the baseline value refers to the last collected target historical record value extracted from the time series corresponding to the operating parameters of each link, which serves as the starting reference point for prediction calculation.

[0136] In this embodiment, the difference sequence refers to the data set formed by subtracting the two historical records corresponding to adjacent acquisition period numbers in the time series in turn, and arranging each difference in the acquisition order. Each difference represents the change range of the link's operating parameters within an adjacent period.

[0137] In this embodiment, the average change amplitude per unit period refers to the value obtained by averaging all the differences in the difference sequence, which is used to characterize the average change of the link operating parameters within a single acquisition period.

[0138] In this embodiment, the predicted change offset refers to the value obtained by multiplying the average change amplitude per unit period by a preset time advance, which is used to quantify the total expected change in the link operating parameters from the reference time to the future predicted time.

[0139] In this embodiment, the prediction time lead refers to the number of acquisition cycles that are predicted from the reference time backward, which is used to determine the length of the time interval corresponding to the predicted trend value.

[0140] In this embodiment, the preventive handover preparation process refers to a series of operational steps, such as screening backup links, quality detection, and pre-handover scoring, when it is determined from the predicted trend value that the link will deteriorate in the future and the current quality is still up to standard. The aim is to proactively advance the handover timing.

[0141] In this embodiment, the link to be switched refers to the target communication link whose operational quality is determined to decline to below the preset quality standard within a preset time interval based on the predicted trend value, and whose current link operational quality score is still higher than the preset quality standard, and is therefore marked as requiring the initiation of a preventive switching preparation process.

[0142] In this embodiment, the pre-switching operation quality score refers to the value calculated by sending a link quality detection command to the pre-selected backup link, based on the obtained real-time transmission delay, data packet loss rate, and bandwidth stability parameters, and according to the same scoring method as the current link operation quality score and the current data real-time level weight, to quantify the overall operation quality of the pre-selected backup link at the detection time.

[0143] In this embodiment, the handover benefit threshold refers to a pre-set difference threshold. When the difference between the current link operation quality score of the link to be switched and the pre-switching operation quality score of a certain pre-selected backup link reaches or exceeds the threshold, it is determined that performing the handover can obtain sufficient performance improvement, thereby triggering the actual link migration.

[0144] In this embodiment, hot standby status refers to a standby operation mode in which the original link to be switched still maintains physical connection, continuously monitors link parameters, and has the ability to resume transmission at any time after the original link to be switched has completed the transmission switchover, so that it can quickly switch back if the new link is abnormal after the switchover.

[0145] The working principle and beneficial effects of the above technical solution are as follows: By continuously monitoring the historical change trend of link operating parameters, the link quality trend in the future period is predicted. Preventive handover preparation is initiated before the link actually deteriorates, realizing the transformation from passive response to active avoidance. Based on the dual judgment of predicted trend value and current quality score, invalid handover caused by short-term fluctuations is avoided. At the same time, by comparing the difference in operating quality between the link to be switched and the pre-selected backup link, the handover benefits are quantified, ensuring that migration is only performed when the handover can bring significant gains. The quality detection and sorting of backup links are completed before the handover, and the original link can be kept in hot standby. This not only ensures the continuity of transmission of high real-time data, but also reduces the risk of handover failure, significantly improving the overall reliability and scheduling efficiency of communication links.

[0146] This embodiment provides a real-time data-component collaborative scheduling system for a power grid digital twin platform. The adaptation mapping unit further includes:

[0147] Based on the standardized operation monitoring dataset, extract the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data, and sort the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data to form a data feature vector;

[0148] At the same time, the upper limit of the real-time level of data that the current candidate component can receive, the expected value of the data volume level, and the upper limit of the total number of dimensions of the supported data parameter fields are extracted from the component capability feature database and used to form the component requirement vector. The data feature vector and the component requirement vector are both located in the same three-dimensional feature space.

[0149] Based on the data feature vector and component demand vector, calculate the degree of directional matching between the actual characteristics of the currently scheduled data and the expected demands of the components:

[0150] ;

[0151] in, This indicates the degree of directional matching between the actual characteristics of the current scheduling data and the expected requirements of the components; This represents the data feature vector corresponding to the currently scheduled data; This represents the component requirement vector corresponding to the current candidate component; This represents the dot product of the data feature vector and the component requirement vector; Represents the magnitude of the data feature vector; Represents the magnitude of the component demand vector;

[0152] Obtain the urgency weight coefficient of the business scenario to which the data to be scheduled belongs, as well as the number of times the current candidate component has been successfully called and the total number of calls in the same business scenario;

[0153] The dynamic adaptability between the current data to be scheduled and the candidate components is calculated based on the degree of matching, the urgency weighting coefficient of the business scenario, and the number of times the current candidate component has been successfully called in the same business scenario and the total number of calls:

[0154] ;

[0155] in, This indicates the dynamic fit between the currently scheduled data and the candidate components; This represents the urgency weighting coefficient of the current business scenario, and its value ranges from (0, 1). This indicates the number of times the current candidate component has been successfully invoked in the current business scenario; This indicates the total number of times the current candidate component is invoked in the current business scenario; This represents the historical call success rate of the current candidate component in the current business scenario, and its value range is (0, 1).

[0156] Write the calculated dynamic adaptability into the adaptability priority field associated with the current data to be scheduled and the current candidate component in the component-data adaptability relationship table, and determine the value of the adaptability priority field based on the value of the dynamic adaptability.

[0157] When generating the component call list, the adaptation priority field of all candidate components in the same business scenario in the component-data adaptation relationship table is read, and the call order of each candidate component is arranged in descending order of the adaptation priority field value to form the component call list.

[0158] In this embodiment, the data feature vector refers to an ordered array composed of the values ​​of the current scheduling data in three dimensions: real-time level quantization value, volume level quantization value, and total number of parameter field dimensions. It is used to characterize the data's demand characteristics for component capabilities.

[0159] In this embodiment, the component requirement vector refers to an ordered array extracted from the component capability feature database, consisting of three dimensions: the upper limit of the real-time level of the data that the component can receive, the expected value of the data volume level, and the upper limit of the total number of supported data parameter field dimensions. It is used to characterize the expected capability boundary of the component for input data.

[0160] In this embodiment, the degree of matching in direction refers to the value obtained by calculating the cosine similarity between the data feature vector and the component demand vector. This value reflects the directional consistency between the actual characteristics of the current scheduling data and the expected demand of the component in the three-dimensional feature space. The more consistent the directions, the closer the value is to 1.

[0161] In this embodiment, dynamic adaptability refers to the quantitative index obtained by multiplying the matching degree in the overall direction, the urgency weight coefficient of the current business scenario, and the historical call success rate of the candidate component in the same business scenario. It is used to dynamically evaluate the adaptability between the current scheduling data and the candidate component. The larger the value, the higher the adaptability.

[0162] In this embodiment, the adaptation priority field refers to a dynamically updatable numerical field stored in the component-data adaptation relationship table that is associated with the currently scheduled data and the current candidate component. The value of this field is directly determined by the dynamic adaptation degree and is used to compare the order of calling different candidate components in the same business scenario.

[0163] In this embodiment, the component call list refers to an ordered sequence of components generated by sorting all candidate components from high to low according to the value of the adaptation priority field for the same business scenario. The collaborative scheduling module initiates component calls in sequence according to the list.

[0164] In this embodiment, the real-time level quantization value is mapped to a preset value according to the millisecond, second, minute or hour level of the data. The higher the real-time level, the larger the quantization value. The volume level quantization value is mapped to the corresponding level value according to the preset range to which the data belongs in bytes.

[0165] In this embodiment, the upper limit of the real-time level represents the quantized value corresponding to the highest real-time level that the component can process, and the expected value of the volume level represents the quantized value of the data volume level that the component expects to receive under optimal working conditions.

[0166] In this embodiment, the urgency weight coefficient is directly mapped to the business scenario type.

[0167] In this embodiment, the degree of matching between the actual characteristics of the current scheduling data and the expected requirements of the components in terms of direction is essentially represented by calculating the cosine value of the angle between the data feature vector and the component requirement vector.

[0168] In this embodiment, when W=0, it indicates that the current candidate component has never been called in the business scenario. In this case, the call success rate cannot be obtained through historical statistics. To avoid division by zero errors in the formula and to ensure system availability during initial component deployment or the introduction of new business scenarios, a default historical call success rate of 1 (i.e., PW=1) is set. This default value means that no negative assumptions are made about the component's historical performance in the initial state. The success rate is dynamically updated based on the actual call results after the component generates its first call record in the scenario. Simultaneously, the system can continuously record the component's call status through the scheduling monitoring feedback module, automatically switching to the actually calculated P / W value after W≥1, thereby achieving a progressive and accurate evaluation of the component's adaptability.

[0169] The working principle and beneficial effects of the above technical solution are as follows: By constructing the real-time level, volume level, and field dimensions of scheduling data with the upper limit of the receiving capacity, volume expectation, and dimension support capability of the component into a feature vector and demand vector in the same space, the consistency between data and components in terms of capability structure is quantified by the degree of directional matching. Dynamic adaptation is calculated by combining the urgency of the business scenario and the historical success rate of component calls. This enables accurate and adaptive matching of data and components. The adaptation priority is determined based on the dynamic adaptation degree, and an ordered component call list is generated, avoiding blind calls and significantly improving the utilization efficiency of component resources and scheduling response speed. Especially in the high real-time and high urgency power grid scenario, high success rate components are prioritized for matching, ensuring the timely processing and display of critical business data and enhancing the overall collaborative scheduling capability and operational reliability of the system.

[0170] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A real-time data-component collaborative scheduling system for a power grid digital twin platform, characterized in that, include: The data access control module is used to collect power grid operation monitoring data in real time, and to perform data verification and preprocessing on the power grid operation monitoring data to generate standardized operation monitoring datasets. The component management module is used to acquire various types of power grid digital twin components and component basic data, classify and manage the categories of power grid digital twin components based on business scenarios, build a capability characteristic database for each type of component, and determine the component capability characteristics by combining standardized operation monitoring datasets. The collaborative scheduling module is used to pre-set multi-scenario scheduling strategies based on power grid business needs, clarify the component call priority, linkage logic, and response time limit under different scenarios, monitor the data changes in the real-time data center and the triggering conditions of business scenarios in real time, match and adapt target components based on standardized operation monitoring datasets and component capability characteristics, and perform collaborative scheduling of each component based on scheduling strategies. The scheduling monitoring and feedback module is used to monitor the running status data of each component in real time during the scheduling process, build a visual monitoring panel, detect scheduling anomalies and trigger alarms, and at the same time, statistically analyze scheduling data, evaluate scheduling efficiency and collaborative effect, and provide corresponding optimization suggestions to each module.

2. The real-time data-component co-scheduling system of power grid digital twin platform of claim 1, wherein, The data access control module specifically includes: The data classification unit is used to label the collected power grid operation monitoring data with data source tags based on the data source identifier dimension, classify the equipment types, match the target business scenario tags according to the classification results, and classify the real-time level of the power grid operation monitoring data. The quality verification unit is used to preset differentiated verification rules based on the equipment type and real-time level of different data types, perform quality verification on the power grid operation monitoring data based on the differentiated verification rules, and classify and label the power grid operation monitoring data according to the verification results. The data processing unit is used to classify and process power grid operation monitoring data based on the quality grading labeling results. Combining the data real-time level and business scenario labels, it performs standardization transformation on the processed power grid operation monitoring data to generate a standardized operation monitoring dataset.

3. The real-time data-component co-scheduling system of power grid digital twin platform of claim 2, wherein, The data access control module is also used to obtain metering data from power grid metering equipment, extract scheduling dynamic data from power grid dispatching system, obtain multi-state power grid data based on power grid twin model, and integrate metering data, scheduling dynamic data and multi-state power grid data to form fused data.

4. The real-time data-component co-scheduling system of power grid digital twin platform of claim 3, wherein, The specific process of standardization conversion performed by the data processing unit includes: Based on the quality grading and labeling results, power grid operation monitoring data corresponding to high-quality data labels, usable data labels, low-quality data labels, and invalid data labels are extracted respectively; High-quality data is cleaned in a lightweight manner based on high-quality data labeling, standard format conversion is performed based on preset standard format, and transmission priority is determined according to real-time level. Based on the available data tags, the available data is corrected for deviations and data normalization is performed. After completing the standardized field mapping, business scenario tags are obtained, and the range of applicable component types is determined based on the business scenario tags. Based on low-quality data labeling, the low-quality data is assessed for defects, incompleteness and usability through historical correlation data. If it meets the business scenario requirements, it is standardized and transformed and marked as usable after repair. If it does not meet the requirements, it is downgraded to data to be investigated. Based on invalid data marking, invalid data is stored in the abnormal data storage area for data isolation, core abnormal features are extracted, and abnormal data logs are generated; at the same time, the source of invalid data, collection time, and reason for verification failure are recorded.

5. The real-time data-component co-scheduling system of power grid digital twin platform of claim 1, wherein, The component management module includes: The component classification unit is used to classify components by combining component functional attributes, data requirement characteristics and power grid business scenario types, and to label the scenario adaptation tags of various power grid digital twin components to build a component classification index. The capability feature construction unit is used to obtain component classification results and basic component data, associate field information and data types of standardized operation monitoring datasets, extract component target capability parameters, i.e. core capability features, and establish capability feature description models and capability feature databases. The adaptation mapping unit is used to obtain field information, data types, and business scenario tags of the standardized operation monitoring dataset, extract key parameters from the component capability feature library, establish adaptation mapping relationships by matching data attributes with component capability features, and generate a component-data adaptation relationship table.

6. The real-time data-component co-scheduling system of power grid digital twin platform of claim 5, wherein, The adaptation mapping unit further includes: Based on the standardized operation monitoring dataset, extract the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data, and sort the real-time level quantization value, volume level quantization value, and total number of parameter field dimensions corresponding to the current scheduling data to form a data feature vector; At the same time, the upper limit of the real-time level of data that the current candidate component can receive, the expected value of the data volume level, and the upper limit of the total number of dimensions of the supported data parameter fields are extracted from the component capability feature database and used to form the component requirement vector. The data feature vector and the component requirement vector are both located in the same three-dimensional feature space. Based on the data feature vector and component demand vector, calculate the degree of directional matching between the actual characteristics of the currently scheduled data and the expected demands of the components: Obtain the urgency weight coefficient of the business scenario to which the data to be scheduled belongs, as well as the number of times the current candidate component has been successfully called and the total number of calls in the same business scenario; The dynamic adaptability between the current data to be scheduled and the candidate components is calculated based on the degree of matching, the urgency weighting coefficient of the business scenario, and the number of times the current candidate component has been successfully called in the same business scenario and the total number of calls: Write the calculated dynamic adaptability into the adaptability priority field associated with the current data to be scheduled and the current candidate component in the component-data adaptability relationship table, and determine the value of the adaptability priority field based on the value of the dynamic adaptability. When generating the component call list, the adaptation priority field of all candidate components in the same business scenario in the component-data adaptation relationship table is read, and the call order of each candidate component is arranged in descending order of the adaptation priority field value to form the component call list.

7. The real-time data-component co-scheduling system of power grid digital twin platform of claim 1, wherein, The collaborative scheduling module also includes: The strategy construction unit allows users to obtain various business requirements and scenario characteristics of the power grid, preset multi-scenario scheduling strategies, and build a scheduling strategy library. The component matching unit is used to obtain the standardized operation monitoring dataset of the real-time data center, extract the corresponding business scenario tags and real-time level, and combine the component-data adaptation relationship table and scheduling strategy library to match the target component and generate a component call list. The collaborative operation unit is used to establish a unified interaction protocol, send standardized operation monitoring datasets and operation instructions to target components in the component call list, and drive the power grid dynamic map associated components to operate collaboratively according to preset logic. The abnormal switching unit is used to monitor the link operation quality between the standardized operation monitoring dataset transmission node and the target component receiving node in real time. It obtains the link operation quality based on the link operation parameters, and switches the communication link when the link operation quality is lower than the preset quality standard.

8. The real-time data-component collaborative scheduling system of the power grid digital twin platform as described in claim 7, characterized in that, The abnormal switching unit performs communication link switching as follows: Real-time monitoring of the link operation parameters between the standardized operation monitoring dataset transmission node and the target component receiving node, including transmission delay, data packet loss rate, and bandwidth stability; Simultaneously, the real-time level and target component type of the currently transmitted standardized operation monitoring dataset are extracted, and the link operation quality score between the standardized operation monitoring dataset transmission node and the target component receiving node is obtained. When the link operation quality score is lower than the preset quality standard, the corresponding switching trigger logic is matched based on the real-time level. The high real-time level will start the switching immediately, while the ordinary real-time level will start after multiple cycles of confirmation. Based on historical handover success rates and the preset number of link and component type compatibility, a backup link priority pre-configuration table is generated. Based on the backup link priority pre-configuration table, backup communication links are retrieved sequentially, and the link operation parameters of each backup communication link and its compatibility with the current data and components are checked. Based on the link operation parameters and adaptation matching degree obtained from the test, and combined with the current data real-time level weight, the overall operation quality of each backup communication link is ranked, and a comprehensive ranking result is generated. After the switchover is completed, continuously monitor the operational quality of the new link and record the switchover effect indicators, update the backup link priority pre-configuration table, and adjust the call priority of each backup link; If the link still fails to meet the requirements after switching, a multi-level alarm will be triggered and a dedicated emergency communication link for high real-time data will be invoked.

9. The real-time data-component co-scheduling system of power grid digital twin platform of claim 8, wherein, The abnormal switching unit performs communication link switching, and also includes: Based on a preset period, the transmission delay, data packet loss rate, and bandwidth stability of each communication link are obtained from the link operation parameters between the transmission node and the target component receiving node in the standardized operation monitoring dataset monitored in real time. Based on the chronological order of acquisition time, multiple consecutive historical records of each link are arranged to obtain the time series corresponding to the operating parameters of each link. Each historical record value is associated with a corresponding acquisition cycle number. It is difficult to extract the last collected target historical record value from the time series and set the target historical record value as the baseline value; The difference between two historical records corresponding to adjacent collection period numbers in the time series is determined, and the differences are arranged to obtain a difference sequence. The average difference in the difference sequence is then averaged to obtain the average change amplitude per unit period. Multiply the average change amplitude per unit period by the preset prediction time advance to obtain the prediction change offset, and sum the baseline value and the prediction change offset to obtain the prediction trend value corresponding to each communication link under the prediction time advance. At the same time, the current link operation quality score is obtained based on the link operation parameters monitored in real time. When the predicted trend value indicates that the current communication link's operational quality will decline to below the preset quality standard within a preset time interval, and the current communication link's operational quality score is still above the preset quality standard, a preventive handover preparation process is triggered, and the current communication link is marked as a link to be switched. Based on the triggering result, the real-time level and target component type of the standardized operation monitoring dataset currently being transmitted are extracted. Combined with the pre-configured backup link priority pre-configuration table, a pre-selected backup link with the same transmission node and receiving node as the link to be switched, and which is compatible with the current real-time level and component type is selected. Among them, there is at least one pre-selected backup link. Send a link quality detection command to the pre-selected backup link to obtain the real-time transmission delay, data packet loss rate and bandwidth stability parameters of the pre-selected backup link, and determine the pre-switching operation quality score of each pre-selected backup link by combining the real-time level weight of the current data. When the difference between the current link operation quality score and the pre-switch operation quality score of the link to be switched reaches the preset switching benefit threshold, and before the operation quality of the link to be switched drops to the preset quality standard, the transmission link of the standardized operation monitoring dataset is switched to the pre-selected backup link with the highest pre-switch operation quality score, and the original link to be switched is kept in hot standby status.

10. The real-time data-component co-scheduling system of power grid digital twin platform of claim 1, wherein, The scheduling monitoring feedback module includes: The scheduling status monitoring unit is used to associate the standardized operation monitoring dataset, the component capability feature database and the component-data adaptation relationship table, the component call list and link operation parameters, integrate and generate key scheduling indicators, and build a multi-dimensional visual monitoring panel. The scheduling performance analysis unit is used to perform performance analysis based on key scheduling indicators and the characteristics of power grid business scenarios. It analyzes the synergistic effect of metering data, scheduling dynamic data, and multi-state power grid data linkage in the context of multi-data fusion of a dynamic power grid map, and identifies key problems and their causes. The optimization and adjustment unit is used to generate corresponding optimization schemes based on performance analysis results, and optimize the multi-data fusion scheduling logic and component linkage rules in combination with the power grid dynamic map scenario.

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