Intelligent water and electricity integrated management system and method based on digital twinning

Abstract

The application relates to the field of water and electricity management, and discloses a smart water and electricity integrated management system and method based on digital twinning, wherein the smart water and electricity integrated management method based on digital twinning comprises the following steps: uniformly identifying and recombining operation objects distributed in water supply well groups, pump houses, water pools, pipe network nodes, and power stations at all levels, transmission and distribution lines and key load equipment; according to the operation characteristic differences of the water and electricity objects in the multi-layer entity mapping set, continuously running data are split into state sequences that can reflect the changes of emission guarantee loads; potential instability signs of key equipment under current task conditions are dynamically evaluated; when the digital twinning model identifies that there is an abnormal change trend between the water and electricity systems, related water supply units and power supply units are divided into linkage analysis objects according to spatial correlation and scheduling relationship; and the monitoring strategy and the control strategy of the water and electricity system are dynamically adjusted. The application has the advantage of improving the water and electricity guarantee safety during the emission task.

Description

Technical Field

[0001] This invention relates to the field of hydropower management, specifically to a smart integrated hydropower management system and method based on digital twins. Background Technology

[0002] During launch preparation and mission support at a space launch site, the water and power supply systems must operate collaboratively under conditions of high reliability and continuity to ensure the stable operation of testing, refueling, telemetry, tracking, and control equipment. However, under current technological conditions, the water and power systems within a space launch site were constructed at different times and by different manufacturers. The deep wells, pump houses, water tanks, and pipe networks in the water supply system, and the substations, distribution lines, and critical load equipment in the power supply system, all employ independent monitoring and data acquisition methods. The data interfaces, sampling periods, and data models of each site are inconsistent, resulting in fragmented distribution of water and power operation status data.

[0003] In the specific scenario of a launch mission window, when local pump station load fluctuates or a power supply line exhibits an abnormal trend, existing systems can typically only issue threshold alarms based on single sites and single devices. This makes it difficult to identify the coupling changes in water and electricity loads and their impact on the overall operational status at the system level. For example, the impact of power supply voltage fluctuations on the start-up and shutdown status of pump stations, or the impact of water supply load changes on the operational margin of the local power grid, cannot be uniformly modeled and correlated using existing distributed monitoring systems. This can easily lead to hidden risks propagating between multiple systems without being detected in a timely manner.

[0004] Therefore, it is essential to design a smart integrated hydropower management system and method based on digital twins to improve the safety of hydropower supply during launch missions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a smart hydropower integrated management system and method based on digital twins, which has the advantage of improving the safety of hydropower supply during launch missions and solves the problems mentioned in the background technology.

[0006] To achieve the aforementioned goal of improving the safety of hydropower supply during launch missions, this invention provides the following technical solution: a smart hydropower integrated management method based on digital twins, comprising the following steps: The operating objects distributed in water supply well groups, pump houses, water tanks, pipeline nodes, power stations at all levels, transmission and distribution lines and key load equipment are uniformly identified and reorganized. Based on the equipment structural attributes, operating functions and scheduling affiliations, a multi-layer entity mapping set of the hydropower system is constructed. To address the differences in operational characteristics among various hydropower objects in the multi-layer entity mapping set, time axis alignment and state fragmentation processing are performed on the real-time collected monitoring information. By introducing a task stage identifier factor, continuous operational data is split into state sequences that can reflect changes in launch support load, thereby forming a hydropower operational state sample set with stage semantics. Based on the state sample set, a digital twin model is constructed for each hydropower entity. By continuously updating the model parameters and utilizing the state calculation mechanism inside the model, the potential instability signs of key equipment under the current task conditions are dynamically assessed. When the digital twin model identifies abnormal trends in the hydropower system, the relevant water supply units and power supply units are identified as linkage analysis objects according to their spatial association and scheduling relationship. The operating status of the linkage analysis objects is then cross-validated by combining video monitoring and remote measurement and control data. Based on the assessment results of the scope of impact and risk level of the anomaly, the monitoring and control strategies of the hydropower system are dynamically adjusted, and hydropower dispatch instructions that match the current launch mission phase are generated.

[0007] Preferably, the process of constructing a multi-layer entity mapping set for a hydropower system is as follows: Collect equipment numbers, installation locations, structural components, and system information for all types of water and electricity equipment, and establish basic equipment lists separately for water supply systems and power supply systems. Based on the functional roles of the equipment in the operating system, the equipment in the basic equipment list is divided into water intake unit, water transmission unit, water storage unit, power supply unit, transmission and distribution unit and load unit; Based on the established management hierarchy in the hydropower dispatching system, different types of equipment are grouped into different levels: station level, regional level, and system level. Based on the hierarchical merging results, a multi-level entity mapping set is established to describe the spatial location relationships, operational dependencies, and scheduling control relationships of hydropower equipment.

[0008] Preferably, the process of performing time axis alignment and state fragmentation on the real-time acquired monitoring information is as follows: Based on the time association rules defined in the multi-layer entity mapping set, data from different acquisition systems are corrected to a unified time reference to eliminate time offsets caused by sampling period differences and communication delays. By combining the operation types of each hydropower entity in the multi-layer entity mapping set, the continuous monitoring data is segmented according to the state changes, and state segments reflecting start-up, shutdown, load changes and operating condition switching are extracted. The state fragments are grouped according to the entity correspondence determined in the multi-layer entity mapping set to form a set of state fragments organized by hydropower entities.

[0009] Preferably, the process of forming a sample set of hydropower operation status with stage semantics is as follows: According to the space launch mission process, the launch mission cycle is divided into stages, and corresponding stage identification factors are configured for each mission stage. The stage identification factors are then associated with the state segments in the state segment set that correspond to the time range. The set of state segments is reorganized according to the stage identification factor, and the state segments belonging to the same task stage are connected in series to form a state sequence that reflects the characteristics of hydropower load change. The state sequence is organized into a hydropower operation state sample set with clear task stage semantics.

[0010] The preferred process for constructing digital twin models for each hydropower entity is as follows: Based on the structural parameters, rated operating parameters and historical operating data of the hydropower entity, establish an operating description model corresponding to the entity; The hydropower operation status sample set is used as the model input to initialize the model parameters; By monitoring data in real time, the model parameters are corrected online to form a digital twin model that corresponds one-to-one with each hydropower entity.

[0011] Preferably, the process of dynamically assessing potential instability indicators of critical equipment under current mission conditions is as follows: Introduce state variables that reflect load changes, energy consumption levels, and operational margins into the digital twin model; Based on real-time updated monitoring data, continuous calculations are performed on state variables to obtain equipment operation trend results; By comparing the operational trend results with the equipment's safe operating thresholds, we can identify operational deviations or stability degradations and output potential instability assessment results for critical equipment.

[0012] Preferably, the process of defining relevant water supply units and power supply units as linkage analysis objects according to spatial association and scheduling relationship is as follows: Based on the corresponding equipment identification and anomaly type in the potential instability assessment results, locate the water supply unit and power supply unit that show signs of operational deviation or decreased stability; Based on the spatial layout relationship and scheduling control affiliation of the water supply unit and the power supply unit in the hydropower system, analyze the scope of the resulting linkage impact and identify other units affected by the linkage. Water supply units and power supply units that have mutual influence relationships are combined to form a linkage analysis object.

[0013] Preferably, the process of cross-validating the operational status of the linked analysis object is as follows: Obtain video surveillance footage of the area corresponding to the linked analysis object to confirm the physical status of the equipment; Synchronously read remote measurement and control data of the linked analysis object, including operating parameters and control feedback information; The video surveillance results are compared and analyzed with the remote measurement and control data, and the linkage analysis results after cross-validation are output.

[0014] Preferably, the process of generating hydropower dispatch instructions that match the current launch mission phase is as follows: Based on the results of the linkage analysis, determine the scope of the anomaly's impact and assess the risk level to the launch mission's support. For hydropower units with high risk levels, increase the monitoring frequency and enhance remote control response capabilities; Generate water and electricity dispatch instructions that match the current launch mission phase.

[0015] This invention also discloses another technical solution, a smart hydropower integrated management system based on digital twins, comprising: Entity mapping module: Unifies and reorganizes water supply well groups, pump houses, water tanks, and pipeline nodes to construct a multi-layer entity mapping set for hydropower. Operation Status Module: Performs time axis alignment and status fragmentation processing on the real-time collected hydropower operation monitoring information, and transforms continuous operation data into a hydropower operation status sample set; Digital twin module: Based on the sample set of hydropower operation status, a digital twin model is built and updated to dynamically assess the operation trend and potential instability signs of key equipment under the current task conditions; Cross-validation module: After identifying abnormal trends, it cross-validates the operating status by combining video surveillance and remote monitoring and control data; Assessment and scheduling module: Based on the assessment results of the scope of impact and risk level of the anomaly, it generates water and electricity scheduling instructions that match the current launch mission phase.

[0016] Compared with existing technologies, the present invention provides a smart hydropower integrated management system and method based on digital twins, which has the following beneficial effects: This invention breaks down the data fragmentation caused by the complex sources and inconsistent standards of water and power supply systems in previous aerospace launch sites by unifying the identification and multi-layered entity mapping of the systems. It achieves holistic association and unified management of water and power operation objects at the structural, functional, and scheduling levels. By introducing mission-phase semantics, time-aligned and state-reconstructed water and power operation data, the invention ensures that the operational characteristics of the water and power system accurately correspond to the actual load changes of the launch support mission, effectively improving the targeting and accuracy of operational situation awareness. By constructing continuously evolving digital twin models for each water and power entity, it not only enables real-time mapping of equipment operating status but also allows for proactive assessment of potential instability risks during mission execution, significantly reducing the impact of sudden failures on launch support. When abnormal trends appear in the system, a water and power linkage analysis and multi-source data cross-validation mechanism avoid misjudgment from a single data source, improving the reliability of anomaly identification and the credibility of decision-making. The monitoring and control strategies are dynamically adjusted according to the risk level, and water and electricity scheduling instructions that match the launch mission phase are generated. This transforms water and electricity support from a reactive response to a proactive management model that is process-controllable and risk-predictable, thereby improving the overall safety, reliability and support efficiency of the space launch site's water and electricity system. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: Please refer to Figure 1 As shown in the figure, the intelligent hydropower integrated management method based on digital twin in this embodiment of the invention includes the following steps: S1: The operating objects distributed in water supply well groups, pump houses, water tanks, pipeline nodes, power stations at all levels, transmission and distribution lines and key load equipment are uniformly identified and reorganized. Based on the equipment structural attributes, operating functions and scheduling affiliations, a multi-layer entity mapping set of the hydropower system is constructed.

[0020] The process of constructing the multi-layer entity mapping set of the hydropower system in S1 is as follows: Collect equipment numbers, installation locations, structural components, and system information for various types of hydropower equipment, and establish basic equipment lists separately for water supply systems and power supply systems. Obtain equipment numbers, installation locations, structural components, and system information for various types of hydropower equipment through a combination of on-site automatic data collection and management system retrieval. Equipment numbers are used to uniquely identify equipment entities, installation locations are described using site names, coordinates, or pipeline section identifiers, structural components are used to distinguish equipment models and key component configurations, and system information is used to distinguish between water supply systems and power supply systems. Establish basic equipment lists for water supply systems and basic equipment lists for power supply systems separately. Based on the functional roles of the equipment in the operating system, the equipment in the basic equipment list is divided into water intake units, water transmission units, water storage units, power supply units, transmission and distribution units, and load units. Equipment responsible for raw water acquisition or water source introduction is identified as water intake units, equipment responsible for water transportation and pressure transmission is identified as water transmission units, equipment with water volume regulation or water level stabilization functions is identified as water storage units, power generation, transformation or energy conversion equipment is identified as power supply units, equipment related to power transmission and distribution is identified as transmission and distribution units, and terminal equipment that directly provides services to water and electricity users is identified as load units. Through functional division, each piece of equipment has a clear operational function in the system. Based on the established management hierarchy in the hydropower dispatching system, different types of equipment are hierarchically grouped at the station level, regional level, and system level. Equipment within the same station or the same pumping station or power station is grouped into station-level entities. Multiple station-level entities managed by the same dispatching unit or the same regional control center are grouped into regional-level entities. Furthermore, multiple regional-level entities are aggregated into system-level entities. Through this hierarchical grouping method, a multi-layered equipment organization structure consistent with the actual dispatching management architecture is formed. Based on the hierarchical merging results, a multi-layer entity mapping set is established to describe the spatial location relationships, operational dependencies, and scheduling control relationships of hydropower equipment. Spatial location relationships are used to describe the physical adjacency or upstream and downstream relationships between equipment; operational dependencies are used to characterize the mutual influence of water supply and power supply equipment in terms of load changes, start-up and shutdown sequences, and capacity matching; and scheduling control relationships are used to reflect the logical association of equipment being controlled by the same scheduling command or control strategy. By organizing the above relationships in a multi-layer structure, a multi-layer entity mapping set of hydropower system for state analysis and digital twin modeling is formed.

[0021] S2: To address the differences in operational characteristics among various hydropower objects in the multi-layer entity mapping set, time axis alignment and state fragmentation processing are performed on the real-time collected monitoring information. By introducing a task stage identifier factor, continuous operational data is split into a state sequence that reflects changes in launch support load, thereby forming a hydropower operational state sample set with stage semantics.

[0022] The process of performing time axis alignment and state fragmentation on the real-time acquired monitoring information in S2 is as follows: Based on the time association rules defined in the multi-layer entity mapping set, data from different acquisition systems are corrected to a unified time reference to eliminate time offsets caused by sampling period differences and communication delays. For monitoring data from different acquisition systems such as water supply monitoring system, power supply monitoring system, remote monitoring and control device and video acquisition device, the timestamp information of each data source is read and combined with the time association rules pre-set in the multi-layer entity mapping set to determine a unified system time reference. By comparing the sampling period, data reporting frequency and communication path delay of each acquisition system, timestamps that are ahead or behind are corrected to align monitoring data from different sources on a unified time axis, thereby eliminating time offset problems caused by inconsistent sampling periods, network transmission delays or system clock differences. By combining the operation types of each hydropower entity in the multi-layer entity mapping set, the continuous monitoring data is segmented according to state changes, and state segments reflecting start-up and shutdown, load changes, and operating condition switching are extracted. Based on the key parameter change characteristics of hydropower equipment during start-up and shutdown operations, load adjustments, and operating mode switching, the change inflection points in the monitoring data are identified, and the continuous data is segmented with the change inflection points as boundaries, thereby forming multiple state segments that can reflect the equipment start-up and shutdown process, load change process, and operating condition switching process respectively, so that each state segment corresponds to a relatively stable or clearly changing operating state. The state segments are aggregated according to the entity correspondence determined in the multi-layer entity mapping set to form a state segment set organized by hydropower entities. After the state segments are segmented, the state segments generated by different acquisition systems are matched and aggregated with their corresponding hydropower entities based on the entity correspondence established in the multi-layer entity mapping set. State segments belonging to the same equipment, the same site, or the same scheduling unit are uniformly organized to form a state segment set indexed by hydropower entities, so that each state segment logically corresponds one-to-one with a specific operating object.

[0023] The process of forming a hydropower operation status sample set with stage semantics in S2 is as follows: Based on the space launch mission process, the launch mission cycle is divided into stages, and a corresponding stage identifier factor is configured for each mission stage. The stage identifier factor is then associated with the state segments in the state segment set that correspond to the time range. Based on the organization process and support requirements of the space launch mission, the complete launch mission cycle is divided into multiple mission stages, such as the preparation stage, the testing stage, the launch implementation stage, and the support and recovery stage. For each mission stage, a unique stage identifier factor is configured, and the stage identifier factor is matched with the state segment set corresponding to each hydroelectric entity in the multi-layer entity mapping set in terms of time association. This allows state segments within the same time interval to be automatically associated with the corresponding mission stage. The state segment set is reorganized according to the stage identification factor, and the state segments belonging to the same task stage are connected in series to form a state sequence reflecting the characteristics of hydropower load change. After the stage identification factor association is completed, the state segment set is classified and organized according to the stage identification factor, and the state segments belonging to the same task stage are connected in series in chronological order to form a state sequence reflecting the characteristics of hydropower system load change within the task stage. This can cover key operating processes such as equipment start-up and shutdown, load increase and decrease, and operating mode switching, so that each state sequence completely corresponds to the continuous operating performance of the hydropower system within a specific task stage. The state sequences are organized into a hydropower operation state sample set with clear task stage semantics. After the state sequences are constructed, the state sequences corresponding to each task stage are uniformly organized and stored to form a hydropower operation state sample set with clear task stage semantic annotations. Using task stage identifiers, hydropower entity identifiers, and time intervals as indexes, it can simultaneously reflect the differences in the operation characteristics of the hydropower system under different task stages.

[0024] S3: Based on the state sample set, construct digital twin models for each hydropower entity, and dynamically assess potential instability signs of key equipment under current task conditions by continuously updating model parameters and utilizing the state calculation mechanism inside the model.

[0025] The process of constructing digital twin models for each hydropower entity in S3 is as follows: Based on the structural parameters, rated operating parameters, and historical operating data of hydropower entities, an operational description model corresponding to the entity is established. For each hydropower entity determined in the multi-layer entity mapping set, its structural parameters, rated operating parameters, and historical operating data are collected as the basis for modeling. The structural parameters include equipment model, capacity specifications, interface type, and component information. The rated operating parameters include rated voltage, current, power, flow rate, pressure, and allowable operating range. The historical operating data includes the equipment's operating records and operating condition change information under different task stages. An operational description model is constructed to describe the operating characteristics of the hydropower entity, so that the model can reflect the basic operating behavior of the hydropower entity under normal operating conditions and load change conditions. The hydropower operation status sample set is used as the model input to initialize the model parameters. After the operation description model is established, the formed hydropower operation status sample set is used as the model input basis. Combined with the operation status sequence corresponding to different mission stages, the key parameters in the model are initialized, including determining the initial value range of each operation parameter, the load change response relationship and the state transition conditions, so that the operation description model can correspond to the typical operation scenario of the hydropower system in the aerospace launch support mission in the initial state. By real-time monitoring data, the model parameters are corrected online to form a digital twin model that corresponds one-to-one with each hydropower entity. During system operation, real-time monitoring data from sensors, measurement and control devices, and remote monitoring systems are continuously input. The real-time data is compared with the output results of the operation description model, and the model parameters are dynamically corrected according to the deviation. Through this online correction process, the model can be updated synchronously with the actual operating status of the equipment, thereby forming a digital twin model that corresponds one-to-one with each hydropower entity in terms of structure, status, and operating characteristics, realizing a continuous mapping relationship between physical hydropower entities and their digital models.

[0026] The process of dynamically assessing potential instability indicators of critical equipment under current mission conditions in S3 is as follows: In the digital twin model, state variables reflecting load changes, energy consumption levels, and operational margins are introduced. For the completed digital twin model, a set of state variables is introduced within the model to characterize the stability of equipment operation. State variables include, but are not limited to, load response variables reflecting the degree of load change, energy consumption variables characterizing the energy consumption level per unit time, and operational margin variables describing the safety margin of equipment operation. Load response variables are used to characterize the response intensity of equipment to load changes at different task stages, energy consumption variables are used to reflect the operating efficiency of equipment under the current operating conditions, and operational margin variables are used to represent the difference between the actual operating state of the equipment and its rated operating limit. By introducing state variables, the digital twin model can describe the stability state of key equipment from multiple dimensions. Based on real-time updated monitoring data, continuous calculations are performed on state variables to obtain equipment operation trend results; real-time monitoring data is mapped to corresponding state variables in the digital twin model, and the state calculation process inside the model is driven. By continuously updating the state variables over time, trend results reflecting the evolution of equipment operation status are formed, which are used to describe the overall trend of load changes, energy consumption fluctuations and operating margin changes of the equipment under the current task conditions, thereby obtaining the operation trend results of key equipment. The operational trend results are compared with the equipment's safe operating thresholds to identify operational deviations or stability degradation, and output the potential instability assessment results for critical equipment. After obtaining the equipment's operational trend results, the operational trend results are compared and analyzed with the pre-set equipment safe operating thresholds. The safe operating thresholds are determined based on equipment design parameters, operating specifications, and historical operating experience. When the operational trend results show that the state variables continuously deviate from the safe operating thresholds or the operating margin shows a downward trend, it is determined that the equipment has signs of operational deviation or stability degradation, and the corresponding potential instability assessment results are output to indicate the risk level of critical equipment under the current space launch mission conditions.

[0027] S4: When the digital twin model identifies abnormal trends in the hydropower system, the relevant water supply units and power supply units are designated as linkage analysis objects according to their spatial association and scheduling relationship. The operating status of the linkage analysis objects is then cross-validated by combining video monitoring and remote measurement and control data.

[0028] In S4, the process of defining relevant water supply units and power supply units as linkage analysis objects according to spatial association and scheduling relationship is as follows: Based on the equipment identification and anomaly type in the potential instability assessment results, locate the water supply unit and power supply unit that show signs of operational deviation or stability decline; based on the potential instability assessment results, read the equipment identification, anomaly type and corresponding risk level information recorded therein, and map the equipment identification to the corresponding water supply unit or power supply unit in the multi-layer entity mapping set. Through this mapping process, locate the abnormal unit that shows signs of operational deviation or stability decline, and clarify the location of its site, pipeline node or power grid node; By combining the spatial layout relationship and scheduling control affiliation of water supply units and power supply units in the hydropower system, the scope of the resulting linkage impact is analyzed, and other units affected by the linkage are identified. After identifying the abnormal unit, the scope of the linkage impact that the abnormal unit may cause is analyzed by combining the spatial layout relationship and scheduling control affiliation described in the multi-layer entity mapping set. Based on the water supply network connectivity, power line topology and scheduling control link, other water supply units and power supply units that have direct or indirect operational dependence on the abnormal unit are identified, and the degree of impact that they may be affected in terms of load transfer, power supply guarantee and scheduling response is analyzed, thereby determining the scope of other units affected by the linkage. Water supply units and power supply units that have mutual influence relationships are combined to form a linkage analysis object. After the linkage influence range analysis is completed, the abnormal unit is combined with other water supply units and power supply units that are affected by the linkage according to the mutual influence relationship to form a linkage analysis object for analysis. The linkage analysis object is defined in the form of a set of units, which can reflect the coupling relationship between the water supply system and the power supply system in terms of spatial location, operation dependence and scheduling control.

[0029] The process of cross-validating the running status of the linkage analysis object in S4 is as follows: The system acquires video surveillance footage of the area corresponding to the linked analysis object to confirm the physical status of the equipment. Based on the spatial location information of the water supply unit and power supply unit included in the linked analysis object, it calls the corresponding fixed camera or pan-tilt camera to acquire video surveillance footage covering the operating area of ​​the water supply unit and power supply unit. By identifying the appearance status of the equipment, the status of the operation indicator lights, the opening and closing position of the valves, the rotation of the pumps, and the status of the power distribution equipment in the video footage, it confirms whether there are obvious physical abnormalities, operation interruptions, or traces of human intervention in the equipment corresponding to the linked analysis object, thereby forming a video surveillance judgment result that reflects the actual physical status of the equipment. The remote monitoring and control data of the linked analysis object are read synchronously, including operating parameters and control feedback information. While acquiring video monitoring images, the remote monitoring and control data corresponding to the linked analysis object are read synchronously through the water and electricity dispatch system or remote monitoring and control platform, including but not limited to the flow, pressure, liquid level and start-stop status parameters of the water supply unit, as well as the voltage, current, power, load rate and control execution feedback information of the power supply unit. The video surveillance results are compared and analyzed with the remote monitoring and control data, and the cross-validated linkage analysis results are output. After completing the video surveillance judgment results and the acquisition of remote monitoring and control data, the two are compared and analyzed to determine whether the physical state of the equipment reflected in the video screen is consistent with the trend of the operating parameters displayed by the remote monitoring and control data. If the video surveillance shows that the equipment is in normal operation but the remote monitoring and control data shows abnormal fluctuations, it is marked as a candidate case of monitoring and control abnormality. If both the video surveillance and the remote monitoring and control data show abnormalities, it is marked as an actual abnormality in equipment operation. Based on the comparison and analysis results, the cross-validated linkage analysis results are output.

[0030] S5: Based on the assessment results of the scope of impact and risk level of anomalies, dynamically adjust the monitoring and control strategies of the hydropower system, and generate hydropower dispatch instructions that match the current launch mission phase.

[0031] The process of generating hydropower scheduling instructions that match the current launch mission phase in S5 is as follows: Based on the results of the linkage analysis, the scope of the anomaly's impact is determined, and the risk level to the launch mission support is assessed. Based on the cross-validated linkage analysis results, the identifiers of water supply units and power supply units with operational anomalies or potential instability are extracted. Combined with the spatial relationships and scheduling affiliations described in the multi-layer entity mapping set, the scope of the anomaly's impact on upstream and downstream hydropower units and key load equipment is determined. Based on the functional importance of the anomaly unit in the launch support system, the duration of the anomaly, and the degree of deviation, risk assessment indicators are constructed to quantify the degree of the anomaly's impact and obtain the corresponding risk level, which is used to represent the potential impact of the anomaly on the current launch mission support. For hydropower units with high risk levels, increase the monitoring frequency and enhance remote control response capabilities; for hydropower units with risk levels higher than the preset threshold in the assessment results, dynamically adjust their monitoring and control strategies, enhance the real-time perception of the unit's operating status by shortening the data sampling cycle, increasing the acquisition frequency of key parameters, and enabling backup sensor channels; for hydropower equipment with remote control capabilities, increase the priority and response level of control commands, so that the dispatching system can quickly execute control actions such as load limiting, switching, or isolation when further anomalies are detected, thereby reducing the risk of anomaly spread. Based on the current launch mission phase, generate hydropower dispatch instructions that match the current launch mission phase; after completing the risk level assessment and monitoring and control strategy adjustment, based on the current launch mission phase identifier, invoke the pre-configured phased hydropower guarantee strategy rules to comprehensively determine the water supply capacity, power load allocation, and backup resource activation plan. Based on the determination results, generate hydropower dispatch instructions that match the current launch mission phase, including water supply unit start-up and shutdown adjustments, power plant output adjustments, load priority switching, and emergency guarantee measure configuration, etc., and issue and execute them through the hydropower dispatch system to ensure that the hydropower system operation status is consistent with the launch mission guarantee requirements.

[0032] Example 2: As Figure 2 As shown, the intelligent hydropower integrated management system based on digital twins includes: Entity mapping module: Unifies and reorganizes water supply well groups, pump houses, water tanks, and pipeline nodes to construct a multi-layer entity mapping set for hydropower. Operation Status Module: Performs time axis alignment and status fragmentation processing on the real-time collected hydropower operation monitoring information, and transforms continuous operation data into a hydropower operation status sample set; Digital twin module: Based on the sample set of hydropower operation status, a digital twin model is built and updated to dynamically assess the operation trend and potential instability signs of key equipment under the current task conditions; Cross-validation module: After identifying abnormal trends, it cross-validates the operating status by combining video surveillance and remote monitoring and control data; Assessment and scheduling module: Based on the assessment results of the scope of impact and risk level of the anomaly, it generates water and electricity scheduling instructions that match the current launch mission phase.

[0033] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart hydropower integrated management method based on digital twins, characterized in that, Includes the following steps: The operating objects distributed in water supply well groups, pump houses, water tanks, pipeline nodes, power stations at all levels, transmission and distribution lines and key load equipment are uniformly identified and reorganized. Based on the equipment structural attributes, operating functions and scheduling affiliations, a multi-layer entity mapping set of the hydropower system is constructed. To address the differences in operational characteristics among various hydropower objects in the multi-layer entity mapping set, time axis alignment and state fragmentation processing are performed on the real-time collected monitoring information. By introducing a task stage identifier factor, continuous operational data is split into state sequences that can reflect changes in launch support load, thereby forming a hydropower operational state sample set with stage semantics. Based on the state sample set, a digital twin model is constructed for each hydropower entity. By continuously updating the model parameters and utilizing the state calculation mechanism inside the model, the potential instability signs of key equipment under the current task conditions are dynamically assessed. When the digital twin model identifies abnormal trends in the hydropower system, the relevant water supply units and power supply units are identified as linkage analysis objects according to their spatial association and scheduling relationship. The operating status of the linkage analysis objects is then cross-validated by combining video monitoring and remote measurement and control data. Based on the assessment results of the scope of impact and risk level of the anomaly, the monitoring and control strategies of the hydropower system are dynamically adjusted, and hydropower dispatch instructions that match the current launch mission phase are generated.

2. The intelligent hydropower integrated management method based on digital twins according to claim 1, characterized in that, The process of constructing a multi-layer entity mapping set for a hydropower system is as follows: Collect equipment numbers, installation locations, structural components, and system information for all types of water and electricity equipment, and establish basic equipment lists separately for water supply systems and power supply systems. Based on the functional roles of the equipment in the operating system, the equipment in the basic equipment list is divided into water intake unit, water transmission unit, water storage unit, power supply unit, transmission and distribution unit and load unit; Based on the established management hierarchy in the hydropower dispatching system, different types of equipment are grouped into different levels: station level, regional level, and system level. Based on the hierarchical merging results, a multi-level entity mapping set is established to describe the spatial location relationships, operational dependencies, and scheduling control relationships of hydropower equipment.

3. The intelligent hydropower integrated management method based on digital twins according to claim 2, characterized in that, The process of performing time axis alignment and state fragmentation on the real-time acquired monitoring information is as follows: Based on the time association rules defined in the multi-layer entity mapping set, data from different acquisition systems are corrected to a unified time reference to eliminate time offsets caused by sampling period differences and communication delays. By combining the operation types of each hydropower entity in the multi-layer entity mapping set, the continuous monitoring data is segmented according to the state changes, and state segments reflecting start-up, shutdown, load changes and operating condition switching are extracted. The state fragments are grouped according to the entity correspondence determined in the multi-layer entity mapping set to form a set of state fragments organized by hydropower entities.

4. The intelligent hydropower integrated management method based on digital twins according to claim 3, characterized in that, The process of forming a sample set of hydropower operation status with stage semantics is as follows: According to the space launch mission process, the launch mission cycle is divided into stages, and corresponding stage identification factors are configured for each mission stage. The stage identification factors are then associated with the state segments in the state segment set that correspond to the time range. The set of state segments is reorganized according to the stage identification factor, and the state segments belonging to the same task stage are connected in series to form a state sequence that reflects the characteristics of hydropower load change. The state sequence is organized into a hydropower operation state sample set with clear task stage semantics.

5. The intelligent hydropower integrated management method based on digital twins according to claim 4, characterized in that, The process of building digital twin models for each hydropower entity is as follows: Based on the structural parameters, rated operating parameters and historical operating data of the hydropower entity, establish an operating description model corresponding to the entity; The hydropower operation status sample set is used as the model input to initialize the model parameters; By monitoring data in real time, the model parameters are corrected online to form a digital twin model that corresponds one-to-one with each hydropower entity.

6. The intelligent hydropower integrated management method based on digital twins according to claim 5, characterized in that, The process of dynamically assessing potential instability indicators of critical equipment under current mission conditions is as follows: Introduce state variables that reflect load changes, energy consumption levels, and operational margins into the digital twin model; Based on real-time updated monitoring data, continuous calculations are performed on state variables to obtain equipment operation trend results; By comparing the operational trend results with the equipment's safe operating thresholds, we can identify operational deviations or stability degradations and output potential instability assessment results for critical equipment.

7. The intelligent hydropower integrated management method based on digital twins according to claim 6, characterized in that, The process of defining relevant water supply units and power supply units as linkage analysis objects according to spatial association and scheduling relationship is as follows: Based on the corresponding equipment identification and anomaly type in the potential instability assessment results, locate the water supply unit and power supply unit that show signs of operational deviation or decreased stability; Based on the spatial layout relationship and scheduling control affiliation of the water supply unit and the power supply unit in the hydropower system, analyze the scope of the resulting linkage impact and identify other units affected by the linkage. Water supply units and power supply units that have mutual influence relationships are combined to form a linkage analysis object.

8. The intelligent hydropower integrated management method based on digital twins according to claim 7, characterized in that, The process of cross-validating the operational status of the linked analysis object is as follows: Obtain video surveillance footage of the area corresponding to the linked analysis object to confirm the physical status of the equipment; Synchronously read remote measurement and control data of the linked analysis object, including operating parameters and control feedback information; The video surveillance results are compared and analyzed with the remote measurement and control data, and the linkage analysis results after cross-validation are output.

9. The intelligent hydropower integrated management method based on digital twins according to claim 8, characterized in that, The process of generating hydropower dispatch instructions that match the current launch mission phase is as follows: Based on the results of the linkage analysis, determine the scope of the anomaly's impact and assess the risk level to the launch mission's support. For hydropower units with high risk levels, increase the monitoring frequency and enhance remote control response capabilities; Generate water and electricity dispatch instructions that match the current launch mission phase.

10. A smart hydropower integrated management system based on digital twins, applied to the smart hydropower integrated management method based on digital twins as described in any one of claims 1-9, characterized in that, include: Entity mapping module: Unifies and reorganizes water supply well groups, pump houses, water tanks, and pipeline nodes to construct a multi-layer entity mapping set for hydropower. Operation Status Module: Performs time axis alignment and status fragmentation processing on the real-time collected hydropower operation monitoring information, and transforms continuous operation data into a hydropower operation status sample set; Digital twin module: Based on the sample set of hydropower operation status, a digital twin model is built and updated to dynamically assess the operation trend and potential instability signs of key equipment under the current task conditions; Cross-validation module: After identifying abnormal trends, it cross-validates the operating status by combining video surveillance and remote monitoring and control data; Assessment and scheduling module: Based on the assessment results of the scope of impact and risk level of the anomaly, it generates water and electricity scheduling instructions that match the current launch mission phase.

Images