Digital twin hydraulic engineering operation and maintenance monitoring system and method

Abstract

The invention relates to the technical field of operation and maintenance management, in particular to a digital twin hydraulic engineering operation and maintenance monitoring system and method. The method comprises the following steps: acquiring water conservancy project related data; performing three-dimensional geometric feature extraction on the related data of the water conservancy project to obtain three-dimensional geometric data of the water conservancy project; hydrological structure data association is carried out according to the three-dimensional geometric data of the water conservancy project, and a digital twinborn basic model is generated; global optimization is carried out on the digital twinborn basic model, and a dynamic digital twinborn hydraulic engineering model is generated; performing hydraulic engineering remote monitoring based on the dynamic digital twin hydraulic engineering model to generate hydraulic engineering monitoring data; and performing hydraulic engineering composite state sensing on the hydraulic engineering monitoring data to generate a hydraulic engineering dynamic monitoring map. According to the method, the dynamic model is constructed through the digital twinning technology, precision, intelligence and collaboration of operation and maintenance monitoring of the water conservancy project are achieved, and the precision and intelligent level of operation and maintenance monitoring of the water conservancy project are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and particularly to a digital twin water conservancy project operation and maintenance monitoring system and method. Background Art

[0002] Early water conservancy project monitoring mainly relied on manual inspections and single-sensor monitoring, making it difficult to achieve real-time and refined monitoring of large-scale water conservancy facilities. With the development of the Internet of Things, sensor technology, and wireless communication, multi-source data collection and transmission have gradually become possible, forming a primary digital monitoring system for water conservancy projects. Entering the big data era, the improvement of data storage and analysis capabilities has injected new impetus into the operation and maintenance of water conservancy projects. Combining technologies such as GIS, remote sensing, and satellite navigation, traditional monitoring methods have gradually moved towards intelligence and informatization. However, due to limitations in data processing and integration capabilities, monitoring information still faces the problems of isolation and low timeliness, and cannot fully support complex operation and maintenance requirements. In recent years, the rise of digital twin technology has provided a new solution for water conservancy project operation and maintenance monitoring. Through the combination of virtual and real, digital twin can accurately reproduce the physical state, operating conditions, and environmental impacts of water conservancy projects in the virtual space, achieving dynamic monitoring, precise analysis, and predictive optimization of the entire life cycle. However, currently, traditional water conservancy project monitoring relies on fixed sensors or manual inspections, with limited real-time performance and accuracy. At the same time, operation and maintenance decisions mostly rely on manual experience, resulting in a low level of intelligence, thus leading to a low level of accuracy and intelligence in water conservancy project operation and maintenance monitoring. Summary of the Invention

[0003] Based on this, it is necessary to provide a digital twin water conservancy project operation and maintenance monitoring system and method to solve at least one of the above technical problems.

[0004] To achieve the above object, a digital twin water conservancy project operation and maintenance monitoring method, the method includes the following steps: Step S1: Obtain water conservancy project-related data; extract three-dimensional geometric features from the water conservancy project-related data to obtain three-dimensional geometric data of the water conservancy project; perform hydrological structure data association based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; perform global optimization on the digital twin basic model to generate a dynamic digital twin water conservancy project model; Step S2: Perform remote monitoring of the water conservancy project based on the dynamic digital twin water conservancy project model to generate water conservancy project monitoring data; perform water conservancy project composite state perception on the water conservancy project monitoring data to generate a water conservancy project dynamic monitoring atlas; predict water conservancy project fault points based on the water conservancy project dynamic monitoring atlas for the water conservancy project composite state perception data to generate water conservancy project fault point data; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; Step S3: Based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data, construct an operation and maintenance decision-making plan for the water conservancy project to obtain the operation and maintenance decision-making plan for the water conservancy project; send the operation and maintenance decision-making plan for the water conservancy project to the intelligent control system of the water conservancy project for remote control execution, and generate an intelligent operation and maintenance report for the water conservancy project; Step S4: Back up the data of the intelligent operation and maintenance report for the water conservancy project to generate the intelligent operation and maintenance backup data for the water conservancy project; perform multi-platform data sharing on the intelligent operation and maintenance report for the water conservancy project and the intelligent operation and maintenance backup data through the API interface to execute the collaborative operation and maintenance operation of the water conservancy project.

[0005] The present invention realizes the high-precision virtual mapping of the water conservancy project by obtaining the relevant data of the water conservancy project, extracting the three-dimensional geometric features, and generating the digital twin basic model in combination with the hydrological structure data; generates the dynamic digital twin water conservancy project model through global optimization, enhances the dynamic adaptability and accuracy of the model, and lays a foundation for subsequent monitoring and operation and maintenance. Based on the dynamic digital twin model for remote monitoring, it solves the limitations of traditional monitoring means and realizes real-time and intelligent dynamic perception; generates dynamic monitoring maps and fault point data, effectively improving the accuracy of fault prediction; through multi-scenario simulation, verifies the potential risks and the feasibility of solutions under different operating scenarios, providing a scientific basis for operation and maintenance decision-making. Constructing an operation and maintenance decision-making plan by combining scenario simulation data and state perception data improves the scientificity and pertinence of operation and maintenance decision-making; through the remote execution of the intelligent control system, it realizes automated and precise operation and maintenance operations, reducing human intervention; the generated intelligent operation and maintenance report provides data support for continuously optimizing operation and maintenance strategies. Backing up the data of the intelligent operation and maintenance report ensures the security and integrity of operation and maintenance information; realizing multi-platform data sharing through the API interface breaks data islands, promotes multi-party collaborative operation and maintenance, and improves operation and maintenance efficiency and resource utilization. Therefore, the present invention constructs a dynamic model through digital twin technology, realizes the precision, intelligence and collaboration of water conservancy project operation and maintenance monitoring, and improves the precision and intelligence level of water conservancy project operation and maintenance monitoring.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain the relevant data of the water conservancy project, where the relevant data of the water conservancy project includes topographic data, meteorological data and hydrological data; Step S12: Perform data preprocessing on the topographic data, meteorological data and hydrological data, and integrate the preprocessed topographic data, meteorological data and hydrological data into a standard multi-source water conservancy project data set, where the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: Extract three-dimensional geometric features based on the standard multi-source water conservancy project dataset to obtain three-dimensional geometric data of the water conservancy project; correlate the hydrological structure data according to the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; Step S14: Extract the spatio-temporal features of the standard multi-source water conservancy project dataset to obtain spatio-temporal feature data of the water conservancy project; use the spatio-temporal feature data of the water conservancy project to globally optimize the digital twin basic model to generate a dynamic digital twin water conservancy project model.

[0007] In the present invention, through data cleaning, denoising, missing value filling and standardization processing, multi-source topographic, meteorological and hydrological data are integrated into a standard dataset. This standardization processing improves the consistency and quality of the data, facilitating subsequent analysis and modeling. Through three-dimensional geometric feature extraction, three-dimensional geometric data of the water conservancy project are generated, providing an accurate geometric structure description for the establishment of the digital twin basic model. Such refined geometric features help to more accurately simulate the physical form of the water conservancy project. Correlating the three-dimensional geometric data with the hydrological structure data enables the model to dynamically reflect the relationship between the physical and hydrological characteristics of the water conservancy project. This comprehensiveness enhances the practicality of the model, facilitating real-time monitoring and predicting the impact of hydrological changes on the project. By extracting spatio-temporal feature data and using it to globally optimize the basic model, a dynamic digital twin model is generated. This optimization enables the model to dynamically adapt to changes in different time and space conditions, providing a key ability for real-time project monitoring and decision support. The dynamic digital twin water conservancy project model can be updated in real time and accurately simulate the operation state of the project, providing strong data support for water conservancy project design, risk prediction, disaster management and resource allocation.

[0008] Preferably, the correlation of the hydrological structure data according to the three-dimensional geometric data of the water conservancy project includes: Extract topographic features from the three-dimensional geometric data of the water conservancy project to obtain topographic feature data of the water conservancy project; use the topographic feature data of the water conservancy project to match the hydrological element data of the standard multi-source water conservancy project dataset to obtain hydrological element matching data; Conduct a watershed partition boundary analysis on the topographic feature data of the water conservancy project according to the hydrological element matching data to obtain watershed partition boundary data; use the watershed partition boundary data to label the coordinates of the structural key points of the three-dimensional geometric data of the water conservancy project to obtain structural key point coordinate data; Construct hydrological structure patches for the watershed partition boundary data according to the structural key point coordinate data to obtain hydrological structure patch data; conduct hydrological structure attribute correlation on the hydrological structure patch data to obtain hydrological structure correlation data; conduct dynamic spatio-temporal feature mapping on the hydrological structure correlation data to obtain dynamic hydrological structure feature data; Integrate spatial modeling of hydrological and geometric data for dynamic hydrological structure feature data to obtain a digital twin basic model.

[0009] In the present invention, by extracting terrain features from three-dimensional geometric data and combining with a standard multi-source water conservancy project dataset, the accurate matching of hydrological elements is realized, improving the accuracy of the matching of terrain and hydrological characteristics and providing a high-quality data basis for subsequent analysis. Based on the hydrological element matching data, watershed zoning boundary analysis is carried out, which helps to accurately divide different hydrological regions, clearly define the boundary range, and provide accurate spatial information for hydrological watershed management. By annotating the key point coordinates in the three-dimensional geometric data and constructing hydrological structure patches in combination with watershed boundary data, the model's analytical ability for complex hydrological and geometric characteristics is significantly enhanced, laying a foundation for refined modeling. Mapping dynamic spatio-temporal features to hydrological structure associated data realizes the capture of temporal and spatial dynamic changes of hydrological characteristics. The model can reflect the dynamic changes in the hydrological process in real time, improving timeliness and applicability. Through the spatial integrated modeling of dynamic hydrological feature data and geometric data, a comprehensive digital twin basic model is generated, enabling seamless connection of dual information of geometry and hydrology and achieving a leap from single data to multi-dimensional integration. The digital twin basic model takes dynamic hydrological structure feature data as the core, can be updated and optimized in real time, and provides intelligent decision-making support for the design, risk assessment, and disaster management of water conservancy projects. Through the construction and association of watershed zoning and structure patches, the model can be used for hydrological management and optimization, improving the operation efficiency and reliability of water conservancy projects.

[0010] Preferably, step S2 includes the following steps: Step S21: Deploy area sensors based on the dynamic digital twin water conservancy project model to obtain key area sensor deployment data; construct an Internet of Things architecture based on the key area sensor deployment data to generate a water conservancy project Internet of Things architecture; Step S22: Embed the water conservancy project Internet of Things architecture into the dynamic digital twin water conservancy project model for remote monitoring of the water conservancy project to generate water conservancy project monitoring data; perform data time series differencing on the water conservancy project monitoring data to generate water conservancy project time series differencing monitoring data; Step S23: Perform water conservancy project composite state perception on the water conservancy project time series differencing monitoring data to generate water conservancy project composite state perception data; map the water conservancy project composite state perception data to obtain a water conservancy project dynamic monitoring map; Step S24: Predict water conservancy project fault points for the water conservancy project composite state perception data according to the water conservancy project dynamic monitoring map to generate water conservancy project fault point data; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data.

[0011] The present invention accurately locates key areas through a dynamic digital twin model and reasonably deploys sensors to ensure the comprehensiveness and efficiency of monitoring data. Based on the sensor deployment data, an Internet of Things (IoT) architecture for water conservancy projects is constructed, realizing the seamless integration of the perception layer, network layer, and application layer, providing technical support for remote monitoring. Embedding the IoT architecture into the digital twin model enables real-time remote dynamic monitoring of water conservancy projects, generating high-precision monitoring data and improving the timeliness of data acquisition. Performing time series differencing on the monitoring data to extract change features and generate time series differencing monitoring data lays a foundation for dynamic trend analysis. Through comprehensive perception of the time series differencing monitoring data, composite state perception data is generated, which can comprehensively reflect the operating state and potential problems of water conservancy projects. Using the perception data to generate a dynamic monitoring atlas of water conservancy projects provides an important tool for visualizing and intuitively analyzing the project state. Predicting fault points based on the composite state perception data in the dynamic monitoring atlas can quickly identify potential problem locations, winning time for fault handling. The generation of fault point data supports the early deployment of warning measures to avoid major accidents and improve the safety of project operation. Using the fault point data to perform multi-scenario simulations on the digital twin model to generate scenario simulation data supports the design and optimization of multi-dimensional engineering solutions. The scenario simulation data provides the engineering operation situation under different conditions, providing a basis for scientific decision-making and helping to optimize resource allocation and plan adjustment. Through dynamic monitoring and predictive analysis, a water conservancy project management system with real-time, intelligent, and dynamic optimization capabilities is constructed.

[0012] Preferably, the real-time state perception of a water conservancy project for the time series differencing monitoring data of the water conservancy project includes: Performing biodiversity analysis on the time series differencing monitoring data of the water conservancy project to generate biological monitoring data for the water conservancy project area, where the biodiversity analysis includes the distribution analysis of fish, algae, and microbial communities; calculating the nutrient cycle based on the biological monitoring data for the water conservancy project area to obtain the nutrient cycle data for the water conservancy project area; Performing engineering facility state perception on the dynamic digital twin water conservancy project model according to the nutrient cycle data for the water conservancy project area to generate engineering facility perception data for the water conservancy project; performing water body connectivity perception on the nutrient cycle data for the water conservancy project area through the engineering facility perception data for the water conservancy project to generate environmental perception data for the water conservancy project; Integrating the engineering facility perception data and the environmental perception data for the water conservancy project to obtain the composite state perception data for the water conservancy project.

[0013] Through the distribution analysis of fish, algae, and microbial communities, the present invention generates biological monitoring data for the water conservancy project area, which helps to understand the overall health status of the regional ecosystem. Based on the biodiversity analysis of time series differential data, the dynamic changes of biological communities can be tracked in real time, and abnormal signals, such as species reduction or ecological imbalance, can be detected in a timely manner. By calculating the nutrient cycle of biological monitoring data, regional nutrient cycle data is generated, comprehensively revealing the flow path and efficiency of nutrient elements within the region, providing basic data for optimizing the ecological functions of water conservancy projects. The nutrient cycle data provides a scientific basis for the ecological restoration, environmental regulation, and rational utilization of resources of water conservancy projects. Using the nutrient cycle data to sense engineering facilities in the dynamic digital twin model, facility sensing data is generated, which can quickly identify the operating status and potential hidden dangers of facilities, such as blockage, damage, and other problems. Through the facility sensing data, the water body connectivity of the regional nutrient cycle data is sensed, and environmental sensing data is generated to ensure the coherence and functionality of water body flow, improving the overall efficiency of water conservancy projects. Integrating the facility sensing data and environmental sensing data to generate composite state sensing data realizes the comprehensive linkage analysis of the engineering facilities and environmental states. The composite sensing data covers multiple dimensions such as biology, nutrient cycle, facility status, and water body connectivity, enabling a comprehensive assessment of the ecological, environmental, and operating conditions of water conservancy projects. The enhanced real-time monitoring and sensing capabilities provide technical support for the dynamic regulation of the ecological environment of water conservancy projects, enabling timely responses to ecological changes and achieving refined management. The composite state sensing data provides a scientific basis for the planning, operation optimization, and formulation of ecological restoration measures of water conservancy projects, promoting the organic combination of ecological protection and project management.

[0014] Preferably, step S24 includes the following steps: Step S241: According to the dynamic monitoring atlas of the water conservancy project, the structural association of the controllable equipment of the water conservancy project is carried out on the composite state sensing data of the water conservancy project to generate the associated data of the controllable equipment of the water conservancy project; the historical fault data of the equipment is collected from the associated data of the controllable equipment of the water conservancy project to obtain the historical fault records of the controllable equipment of the water conservancy project; Step S242: Divide the historical fault records of the controllable equipment of the water conservancy project into a model training set and a model test set; perform model training on the model training set through the support vector machine algorithm to generate a preliminary model for predicting the fault points of the water conservancy project; use the model test set to optimize and iterate the preliminary model for predicting the fault points of the water conservancy project, thereby generating a prediction model for the fault points of the water conservancy project; Step S243: Import the associated data of the controllable equipment of the water conservancy project into the prediction model for the fault points of the water conservancy project to predict the fault points and generate the fault point data of the water conservancy project; Step S244: Based on the water conservancy project fault point data, set the simulation scenario for the dynamic digital twin water conservancy project model to generate a water conservancy project fault simulation setting scenario; through the water conservancy project fault simulation setting scenario, conduct an emergency operation and maintenance response simulation on the associated data of the controllable equipment of the water conservancy project to generate water conservancy project scenario simulation data.

[0015] The present invention effectively integrates equipment operation and environmental information by associating composite state perception data with the equipment structure based on the dynamic monitoring atlas, providing data support for accurately identifying potential risks of the equipment. By collecting and analyzing historical fault records, a comprehensive equipment fault database is constructed, which helps to deeply understand the characteristics of equipment faults and key risk factors. Using the support vector machine algorithm to train and optimize the historical fault data, a high-precision fault point prediction model is generated, effectively improving the equipment fault prediction ability. Through iterative optimization of the prediction pre-model using the test set, the reliability and adaptability of the prediction model are ensured, and its practicability in different scenarios is improved. After importing the associated data of the controllable equipment into the fault prediction model, potential fault point data can be accurately identified, shortening the fault diagnosis time and reducing the risk of misjudgment. The generation of fault point data provides detailed basic data for further risk assessment and emergency strategy formulation. The simulation setting scenario constructed based on the fault point data can simulate various fault scenarios, improving the ability to quickly respond to emergencies. Through the simulation scenario, the equipment operation and maintenance response simulation is carried out to verify and optimize the effectiveness of the emergency strategy, ensuring rapid and accurate decision-making in actual operation and maintenance. The improvement of the fault point prediction ability can give early warnings and handle problems before they occur, reducing the downtime losses and maintenance costs caused by equipment failures. The scenario simulation reduces the resource requirements for on-site testing and at the same time reduces secondary problems caused by test errors. The fault point prediction and scenario simulation combine the digital twin model with the actual project operation, realizing the refined management of complex engineering systems. Based on the dynamic update and simulation verification of the fault point data, the digital twin model can be continuously optimized and adapted to changing engineering requirements.

[0016] Preferably, the emergency operation and maintenance response simulation of the associated data of the controllable equipment of the water conservancy project through the water conservancy project fault simulation setting scenario includes the following steps: Divide the water conservancy project fault simulation setting scenario to obtain a normal operation scenario, a fault trigger scenario, and a post-fault expansion scenario, and respectively set the scenario simulation sequence for the normal operation scenario, the fault trigger scenario, and the post-fault expansion scenario to obtain a scenario simulation sequence; Extract the gate equipment associated data and the pump station equipment associated data from the associated data of the controllable equipment of the water conservancy project; conduct data association analysis on the gate equipment associated data, the pump station equipment associated data, and the water conservancy project fault point data respectively to generate gate opening influence data and pump station reservoir capacity regulation data; Through the scenario simulation sequence, the simulation abnormal feedback is carried out on the gate opening influence data and the pumping station reservoir regulation data to obtain the simulation abnormal feedback data, including the gate abnormal feedback data and the pumping station abnormal feedback data; the abnormal type of the simulation abnormal feedback data is discriminated. When the simulation abnormal feedback data is the gate abnormal feedback data, the gate opening and closing circuit is restricted for the corresponding gate to generate the control circuit restriction data; When the simulation abnormal feedback data is the pumping station abnormal feedback data, the valve high-lift switching is carried out on the corresponding pumping station to generate the pumping station valve restriction data; based on the control circuit restriction data and the pumping station valve restriction data, the emergency operation and maintenance response simulation is carried out on the associated data of the controllable equipment of the water conservancy project to generate the water conservancy project scenario simulation data.

[0017] In the present invention, the simulation scenario is divided into three stages: normal operation, fault trigger, and post-fault expansion, which finely simulates the dynamic change process of engineering equipment, making the simulation more in line with the actual situation. After setting the scenario simulation sequence, the simulation process can be sequentially unfolded according to the time axis or logical order, providing a more flexible coping strategy for emergency operation and maintenance in complex situations. Combining the associated data of the gate equipment and the pumping station equipment with the fault point data can clarify the linkage relationship between the equipment, providing decision-making support for accurately adjusting the gate opening and the pumping station reservoir capacity. Conducting simulation abnormal feedback analysis on the gate opening influence data and the pumping station reservoir regulation data can quickly discover abnormalities and take targeted control measures to reduce the risk of equipment damage. Restricting the opening and closing circuit of the gate abnormal feedback data can effectively prevent water conservancy project safety accidents caused by incorrect gate operations. Switching the valve to the high-lift mode for the pumping station abnormal feedback data can quickly adjust the operation state of the pumping station to cope with emergencies. The linkage simulation based on the control circuit restriction data and the pumping station valve restriction data improves the accuracy and real-time performance of the emergency operation and maintenance response. The emergency operation and maintenance response simulation can quickly locate problems, shorten the maintenance time, and improve the operation efficiency of the water conservancy project.

[0018] Preferably, step S3 includes the following steps: Step S31: Based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data, construct an operation and maintenance decision-making plan to obtain the water conservancy project operation and maintenance decision-making plan; calculate the comprehensive score of the water conservancy project operation and maintenance decision-making plan to generate the score of the water conservancy project operation and maintenance decision-making plan; Step S32: Screen the optimal operation and maintenance plan for the water conservancy project operation and maintenance decision-making plan according to the score of the water conservancy project operation and maintenance decision-making plan, and send the selected optimal operation and maintenance plan to the intelligent control system of the water conservancy project for remote control execution to obtain the water conservancy project remote control feedback data; Step S33: Convert the water conservancy project remote feedback data into an operation and maintenance data chart to generate the intelligent operation and maintenance report of the water conservancy project.

[0019] By combining the simulation data of water conservancy project scenarios with composite state perception data, this invention constructs an operation and maintenance decision-making plan, making the decision-making process more scientific and data-driven, and effectively reducing the subjective errors of traditional experience-based decision-making. By calculating the comprehensive scores of the operation and maintenance decision-making plans, quantitative evaluations can be carried out on multiple alternative plans, so as to select the optimal plan and provide support for refined management in complex scenarios. By screening out the optimal operation and maintenance plan through comprehensive scores, the decision-making time is significantly shortened, the implementation of inefficient or ineffective plans is avoided, and the operation and maintenance efficiency is improved. The optimal plan is directly transmitted to the intelligent control system of the water conservancy project for remote control execution, simplifying the traditional manual operation steps and realizing an automated control process. After the operation and maintenance decision-making plan is remotely executed, the feedback data is analyzed and processed to form a closed-loop management system, providing a basis for subsequent optimization. By converting the feedback data into operation and maintenance data charts and generating an intelligent operation and maintenance report, the operation and maintenance process and results are intuitively displayed, enhancing the decision-making support ability of management personnel. The screening and implementation of the optimal operation and maintenance plan can effectively reduce risks, reduce resource waste, and ensure the safe operation of water conservancy project equipment. The remote feedback mechanism of the intelligent control system can quickly respond to emergencies and timely adjust operation and maintenance strategies, enhancing the reliability of the project. By combining scenario simulation, intelligent control, and data-driven decision-making, the full intelligence and automation of the operation and maintenance process are realized, promoting the construction of a modern management model for water conservancy projects.

[0020] Preferably, step S4 includes the following steps: Step S41: Store the intelligent operation and maintenance report of the water conservancy project in the cloud platform and set up an automatic backup mechanism; perform data backup on the intelligent operation and maintenance report of the water conservancy project according to the automatic backup mechanism to generate the intelligent operation and maintenance backup data of the water conservancy project; Step S42: Perform multi-platform data sharing on the intelligent operation and maintenance report of the water conservancy project and the intelligent operation and maintenance backup data through the API interface to execute the collaborative operation and maintenance operations of the water conservancy project.

[0021] By storing the intelligent operation and maintenance reports of water conservancy projects in the cloud platform, the present invention not only ensures the centralized management and efficient retrieval of data, but also ensures the security of data through an automatic backup mechanism, reducing the risk of data loss. The automatic backup mechanism guarantees the real-time backup of operation and maintenance reports, effectively avoiding data loss caused by system failures or operation errors, and ensuring the integrity and continuity of information. Through the API interface, the intelligent operation and maintenance reports and backup data are shared among multiple platforms, enabling different departments or different operation platforms to obtain operation and maintenance data in real time, promoting information sharing and resource collaboration. The realization of multi-platform data sharing strengthens the collaborative operation of all links of water conservancy projects, promotes efficient cross-departmental cooperation, and improves the overall coordination of water conservancy project operation and maintenance. The real-time sharing of intelligent operation and maintenance reports and backup data of water conservancy projects can provide a unified decision-making basis for all parties, reduce the time difference in information transmission, and optimize the response speed. Through multi-platform sharing, relevant decision-makers can access operation and maintenance reports in real time, thus making quick responses, timely adjusting work plans and operation and maintenance strategies, and enhancing the emergency response ability of water conservancy projects. Cloud platform storage not only ensures the long-term availability of water conservancy project operation and maintenance data, but also ensures the rapid recovery of data in case of failures or losses through regular automatic backups. Multi-platform data sharing through the API interface can support data interconnection between different systems, overcome the problem of data islands caused by platform incompatibility, and enable various management systems and monitoring platforms to work efficiently in coordination.

[0022] In this specification, a digital twin water conservancy project operation and maintenance monitoring system is provided for implementing the above-mentioned digital twin water conservancy project operation and maintenance monitoring method. The digital twin water conservancy project operation and maintenance monitoring system includes: A digital twin module, configured to obtain water conservancy project-related data; extract three-dimensional geometric features from the water conservancy project-related data to obtain three-dimensional geometric data of the water conservancy project; perform hydrological structure data association based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; and perform global optimization on the digital twin basic model to generate a dynamic digital twin water conservancy project model; An engineering analysis module, configured to perform remote monitoring of the water conservancy project based on the dynamic digital twin water conservancy project model to generate water conservancy project monitoring data; perform water conservancy project composite state perception on the water conservancy project monitoring data to generate a water conservancy project dynamic monitoring atlas; predict water conservancy project fault points based on the water conservancy project dynamic monitoring atlas for the water conservancy project composite state perception data to generate water conservancy project fault point data; and perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; An operation and maintenance decision-making module, which is used to construct an operation and maintenance decision-making plan based on the simulation data of the water conservancy project scenario and the perception data of the composite state of the water conservancy project, and obtain the operation and maintenance decision-making plan for the water conservancy project; send the operation and maintenance decision-making plan for the water conservancy project to the intelligent control system of the water conservancy project for remote control execution, and generate an intelligent operation and maintenance report for the water conservancy project; A data sharing module, which is used to back up the data of the intelligent operation and maintenance report of the water conservancy project to generate backup data for the intelligent operation and maintenance of the water conservancy project; perform multi-platform data sharing on the intelligent operation and maintenance report of the water conservancy project and the backup data for the intelligent operation and maintenance of the water conservancy project through the API interface to execute the collaborative operation and maintenance operation of the water conservancy project.

[0023] The beneficial effects of the present invention are as follows: The digital twin module constructs a dynamic digital twin water conservancy project model through three-dimensional geometric feature extraction and hydrological structure data association, realizing the comprehensive digital and virtual management of the water conservancy project. It supports the global optimization of complex water conservancy projects and provides a high-precision data model for subsequent monitoring and simulation. The engineering analysis module real-time collects the operation data of the water conservancy project through remote monitoring, generates a dynamic monitoring atlas, and comprehensively perceives the operation state of the project. Using the fault point prediction technology, it can identify potential risk points before the occurrence of a fault, improve the early warning ability, and reduce the probability of sudden accidents. The operation and maintenance decision-making module combines the scenario simulation data and the state perception data to construct an efficient operation and maintenance decision-making plan, ensuring the scientificity and pertinence of the decision-making. The remote control function reduces the dependence on on-site manual intervention, greatly improves the operation and maintenance efficiency, and reduces the operation cost. The data sharing module realizes the efficient distribution and collaborative operation of the intelligent operation and maintenance report through data backup and multi-platform data sharing: each department and platform carry out work synchronously based on the shared data, improving the collaborative efficiency. It supports cross-platform data access and operation, promoting the intelligentization and informatization of water conservancy project management. The data backup function ensures the safe storage and disaster recovery ability of the operation and maintenance data, and can be quickly restored even in case of emergencies. Through the monitoring, analysis, decision-making and sharing of the whole life cycle, a closed-loop water conservancy project management process is established to ensure the long-term stable operation of the system. The multi-scenario simulation function can simulate different operating conditions, providing decision-making support for the design optimization, risk assessment and emergency response of water conservancy projects. It improves the adaptability and resilience of water conservancy projects and effectively responds to complex and changeable operating environments. Therefore, the present invention constructs a dynamic model through digital twin technology, realizing the precision, intelligence and collaboration of the operation and maintenance monitoring of water conservancy projects, and improving the precision and intelligence level of the operation and maintenance monitoring of water conservancy projects. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the step flow of a digital twin water conservancy project operation and maintenance monitoring method; Figure 2 It is for Figure 1 the detailed implementation step flow diagram of step S2 in Figure 3 is Figure 1 a detailed implementation step flow diagram of step S3 in The realization, functional characteristics, and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Specific Embodiments

[0025] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly, the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0028] To achieve the above object, please refer to Figures 1 to 3 , a digital twin water conservancy project operation and maintenance monitoring method, the method includes the following steps: Step S1: Obtain water conservancy project-related data; extract three-dimensional geometric features from the water conservancy project-related data to obtain three-dimensional geometric data of the water conservancy project; perform hydrological structure data association based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; perform global optimization on the digital twin basic model to generate a dynamic digital twin water conservancy project model; Step S2: Based on the dynamic digital twin water conservancy project model, conduct remote monitoring of the water conservancy project to generate water conservancy project monitoring data; perform water conservancy project composite state perception on the water conservancy project monitoring data to generate a water conservancy project dynamic monitoring atlas; predict the water conservancy project fault points based on the water conservancy project dynamic monitoring atlas to generate water conservancy project fault point data; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; Step S3: Construct an operation and maintenance decision-making scheme based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain a water conservancy project operation and maintenance decision-making scheme; send the water conservancy project operation and maintenance decision-making scheme to the water conservancy project intelligent control system for remote control execution to generate a water conservancy project intelligent operation and maintenance report; Step S4: Back up the data of the water conservancy project intelligent operation and maintenance report to generate water conservancy project intelligent operation and maintenance backup data; perform multi-platform data sharing on the water conservancy project intelligent operation and maintenance report and the water conservancy project intelligent operation and maintenance backup data through the API interface to execute the collaborative operation and maintenance operation of the water conservancy project.

[0029] The present invention realizes the high-precision virtual mapping of the water conservancy project by obtaining the relevant data of the water conservancy project and extracting the three-dimensional geometric features, and generating a digital twin basic model in combination with the hydrological structure data; generates a dynamic digital twin water conservancy project model through global optimization, enhancing the dynamic adaptability and accuracy of the model, and laying a foundation for subsequent monitoring and operation and maintenance. Conducting remote monitoring based on the dynamic digital twin model solves the limitations of traditional monitoring means and realizes real-time and intelligent dynamic perception; generating a dynamic monitoring atlas and fault point data effectively improves the accuracy of fault prediction; through multi-scenario simulation, verifies the potential risks and the feasibility of solutions under different operating scenarios, providing a scientific basis for operation and maintenance decision-making. Constructing an operation and maintenance decision-making scheme by combining scenario simulation data and state perception data improves the scientificity and pertinence of operation and maintenance decision-making; through the remote execution of the intelligent control system, realizes automated and precise operation and maintenance operations, reducing human intervention; the generated intelligent operation and maintenance report provides data support for continuously optimizing the operation and maintenance strategy. Backing up the data of the intelligent operation and maintenance report ensures the security and integrity of the operation and maintenance information; realizing multi-platform data sharing through the API interface breaks the data island, promotes multi-party collaborative operation and maintenance, and improves the operation and maintenance efficiency and resource utilization rate. Therefore, the present invention constructs a dynamic model through digital twin technology, realizing the precision, intelligence and collaboration of water conservancy project operation and maintenance monitoring, and improving the precision and intelligence level of water conservancy project operation and maintenance monitoring.

[0030] In the embodiment of the present invention, refer to Figure 1As shown in the figure, it is a schematic diagram of the step flow of a digital twin water conservancy project operation and maintenance monitoring method of the present invention. In this example, the digital twin water conservancy project operation and maintenance monitoring method includes the following steps: Step S1: Obtain relevant data of the water conservancy project; extract three-dimensional geometric features from the relevant data of the water conservancy project to obtain three-dimensional geometric data of the water conservancy project; perform hydrological structure data association based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; perform global optimization on the digital twin basic model to generate a dynamic digital twin water conservancy project model; In the embodiment of the present invention, by obtaining relevant data of the water conservancy project, including but not limited to topographic data, structural design drawings, construction records, basin hydrological data, meteorological data, etc. The data sources can be various methods such as remote sensing monitoring, UAV aerial photography, on-site construction sensors, and historical databases. Clean the collected original data, delete redundant information and invalid data. Perform noise filtering, data completion (such as filling missing values), and format standardization to generate a high-quality preprocessed data set. Based on the preprocessed data, construct a three-dimensional model of the water conservancy project through a point cloud reconstruction algorithm or a mesh generation algorithm. Use edge detection and surface fitting techniques to extract key geometric features, such as dam shape, gate structure, channel slope, etc. Digitally encode the extracted geometric features to generate three-dimensional geometric data describing the geometric attributes of the water conservancy project. Match and associate the three-dimensional geometric data with hydrological data (such as flow velocity, flow rate, water level, rainfall, etc.). Use a multi-source data fusion algorithm (such as weighted fusion based on weights or deep learning methods) to construct a multi-dimensional association matrix. Based on the geometric topology structure of the water conservancy project and the dynamic changes of hydrological data, establish a hydrological flow model to generate a preliminary digital twin basic model, which is used to represent the static and dynamic characteristics of the water conservancy project and its surrounding environment. Apply a global optimization algorithm (such as a genetic algorithm, particle swarm optimization algorithm) to the digital twin basic model to improve its accuracy and calculation efficiency. The optimization objectives include structural rationality, real-time and spatial consistency of hydrological data. Introduce the time dimension, and dynamically update the state of the twin model through real-time hydrological monitoring data and prediction models to generate a dynamic digital twin water conservancy project model, which can reflect the operating state and environmental changes of the water conservancy project in real time.

[0031] Step S2: Based on the dynamic digital twin water conservancy project model, perform remote monitoring of the water conservancy project to generate water conservancy project monitoring data; perform water conservancy project composite state perception on the water conservancy project monitoring data to generate a water conservancy project dynamic monitoring atlas; perform water conservancy project fault point prediction on the water conservancy project composite state perception data according to the water conservancy project dynamic monitoring atlas to generate water conservancy project fault point data; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; In the embodiments of the present invention, a remote monitoring system is constructed based on a dynamic digital twin water conservancy project model, integrating a sensor network (such as water level sensors, current meters, pressure sensors) and an IoT gateway to achieve real-time data transmission. Utilizing a cloud platform and a distributed computing architecture to support the remote monitoring of large-scale water conservancy projects. Dynamically collect the operation data of water conservancy projects, including water level, flow rate, dam stress, meteorological changes, etc. After being preprocessed by edge computing devices, the data is uploaded to the cloud monitoring platform to generate water conservancy project monitoring data. Dynamically match and fuse the monitoring data with the dynamic digital twin model. Use multi-dimensional feature extraction algorithms (such as deep learning, principal component analysis) to perceive the composite state of water conservancy projects (the combination of static state and dynamic operation state). Visualize the perceived data to generate dynamic monitoring maps of water conservancy projects, including spatio-temporal evolution maps, stress distribution maps, and basin dynamic change maps. The maps are updated in real time to reflect the operation status and potential risk areas of water conservancy projects. Use historical monitoring data and dynamic monitoring maps to establish a fault point prediction model based on machine learning algorithms (such as random forest, support vector machine, LSTM neural network). The key indicators for fault point prediction include dam cracks, leakage locations, equipment abnormalities, etc. Conduct predictive analysis on real-time monitoring data to locate potential fault points and generate water conservancy project fault point data. Each fault point includes location coordinates, risk level, potential causes, and repair suggestions. Based on the fault point data, design multiple simulation scenarios, including natural disasters (such as floods, earthquakes), equipment failures (such as gate malfunctions), operation limit tests, etc. Each scenario includes external input conditions (such as rainfall, earthquake intensity) and simulation objectives (such as evaluating water level changes, pressure distribution). Run the simulation scenarios in the dynamic digital twin water conservancy project model, and simulate the actual operation conditions through finite element analysis (FEA), computational fluid dynamics (CFD) simulation, and dynamic system simulation tools (such as ANSYS, Simulink) to generate water conservancy project scenario simulation data, including multi-dimensional results such as stress distribution, fluid flow path, and disaster impact range.

[0032] Step S3: Construct an operation and maintenance decision-making plan based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain a water conservancy project operation and maintenance decision-making plan; send the water conservancy project operation and maintenance decision-making plan to the water conservancy project intelligent control system for remote control execution to generate a water conservancy project intelligent operation and maintenance report; In the embodiments of the present invention, by integrating the simulation data of the water conservancy project scenario and the composite state perception data of the water conservancy project, a multi-dimensional data set is formed. The data includes simulation results (such as stress distribution, fluid path, fault risk assessment) and real-time monitoring status (such as water level, flow rate, temperature change). Ensure the operational safety of the water conservancy project. Reduce maintenance costs and improve system efficiency. Construct a decision-making model based on multi-objective optimization algorithms (such as fuzzy logic, genetic algorithm or reinforcement learning). The model outputs include the optimal control parameters (such as gate opening, pumping station operation mode) and maintenance strategies (such as maintenance priority). Generate an operation and maintenance decision-making plan for the water conservancy project according to the optimization results, including: suggestions for daily operation parameters. Response plans in case of emergencies, medium- and long-term maintenance plans, simulate and evaluate the plans to verify their applicability and effectiveness in different scenarios. Convert the operation and maintenance decision-making plan for the water conservancy project into control instructions and send them to the intelligent control system of the water conservancy project through communication protocols (such as Modbus, BACnet). The intelligent control system executes the decision-making plan, including: dynamically adjusting operation parameters (such as adjusting the pump power, changing the angle of the flood discharge gate). Automatically starting maintenance equipment (such as a trash rack cleaner, monitoring instrument). Triggering emergency response plans (such as starting the standby power supply, issuing an alarm). Real-time feedback the execution results through the intelligent control system, collect key operation indicators and environmental status. Analyze and evaluate the execution data, including: the execution effect of the decision-making plan, the change trend of potential risks, the stability of equipment operation, and generate an intelligent operation and maintenance report for the water conservancy project, including: the execution details of the operation and maintenance plan (such as instruction execution time, equipment response situation), the change analysis of the operation status of the water conservancy project (such as water level change curve, mitigation of fault points), and recommended subsequent optimization plans (such as equipment upgrade or operation strategy adjustment).

[0033] Step S4: Back up the data of the intelligent operation and maintenance report of the water conservancy project to generate the intelligent operation and maintenance backup data of the water conservancy project; perform multi-platform data sharing of the intelligent operation and maintenance report of the water conservancy project and the intelligent operation and maintenance backup data through the API interface to execute the collaborative operation and maintenance operation of the water conservancy project.

[0034] In the embodiments of the present invention, by inputting the intelligent operation and maintenance report of the water conservancy project, including execution records, effect evaluation, operation status analysis, optimization suggestions, etc. Classify and store according to data types (such as text data, image data, time series data): Use a distributed storage system (such as Hadoop, Ceph) to ensure the storage capacity of large-scale data. Store frequently accessed data in the cache area and store infrequently accessed data in the cold data storage area. Implement a multi-level data backup strategy: Use RAID technology to create copies in local storage devices. Upload the data to cloud storage services (such as AWS S3, Azure Blob Storage) to ensure off-site disaster recovery capabilities. Use encryption algorithms (such as AES-256) to encrypt the backup data to generate the intelligent operation and maintenance backup data of the water conservancy project to ensure data security. Verify the integrity and availability of the backup data to ensure efficient recovery when needed. Design standardized API interfaces (such as RESTful API or gRPC) for data sharing. The interface functions include data query, download, update, and access permission control. Deploy an API gateway to support efficient access on multiple platforms (such as Web platforms, mobile applications, SCADA systems). Combine load balancing and CDN technologies to improve the interface response speed. Share the intelligent operation and maintenance report and backup data of the water conservancy project to each operation and maintenance platform through the API interface. Support real-time synchronization and historical data access to facilitate data collaboration between multiple platforms. Each platform performs collaborative operation and maintenance tasks based on the shared data. For example: The equipment maintenance team arranges maintenance plans according to the shared data. The operation monitoring platform monitors key parameters in real time and feeds them back to the operation and maintenance management system. The emergency response platform formulates disaster plans based on the shared data. Implement a refined permission control mechanism (such as permission management based on RBAC or ABAC): Ensure that different platforms and users can only access data that matches their permission levels. Record data access logs and generate audit reports to facilitate compliance checks during the data sharing process.

[0035] Preferably, step S1 includes the following steps: Step S11: Obtain water conservancy project-related data, where the water conservancy project-related data includes terrain data, meteorological data, and hydrological data; Step S12: Perform data preprocessing on the terrain data, meteorological data, and hydrological data, and integrate the preprocessed terrain data, meteorological data, and hydrological data into a standard multi-source water conservancy project dataset, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S13: Extract three-dimensional geometric features based on the standard multi-source water conservancy project dataset to obtain three-dimensional geometric data of the water conservancy project; Correlate hydrological structure data according to the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; Step S14: Extract the spatio-temporal features of the standard multi-source water conservancy project dataset to obtain the spatio-temporal feature data of the water conservancy project; use the spatio-temporal feature data of the water conservancy project to globally optimize the digital twin basic model and generate a dynamic digital twin water conservancy project model.

[0036] In the embodiment of the present invention, the target area is scanned by using technologies such as unmanned aerial vehicles, satellite images, and light detection and ranging (LiDAR) to obtain high-precision topographic information. Historical and real-time meteorological parameters, including rainfall, temperature and humidity, wind speed, etc., are obtained from meteorological stations or global meteorological data platforms. Data such as basin water level, flow rate, water quality, and evaporation are collected through sensors, monitoring stations, etc., and historical hydrological records are supplemented. Redundant or incorrect values in topographic, meteorological, and hydrological data are removed, such as abnormal precipitation records or invalid measurement points. Wavelet transform or low-pass filtering algorithms are applied to reduce measurement noise and improve data accuracy. Interpolation methods (such as linear interpolation or Kriging interpolation) are used to fill in the missing points in topographic and hydrological data to ensure data continuity. The data format, unit, and coordinate system are unified. For example, the topographic data is unified to a certain elevation datum, and the meteorological and hydrological data are converted to the standard International System of Units (SI) to generate a standard multi-source water conservancy project dataset with consistency and high quality. Geometric features of key areas such as river channels, dams, and reservoir areas are extracted from the topographic data, and a three-dimensional surface model is generated through a TIN grid. Point cloud segmentation algorithms are used to identify specific structures (such as dams and pumping stations) in the terrain. According to the hydrological data, spatio-temporal distribution data such as flow rate and flow velocity are mapped onto the three-dimensional geometric model to form the correlation between the hydrological structure and the terrain. Time series features such as water flow changes and rainfall distribution are extracted from the standard multi-source water conservancy project dataset; through spatio-temporal statistical analysis, the coupling characteristics of meteorology, hydrology, and topography are extracted. Combining the spatio-temporal feature data, the model parameters are adjusted through global optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) to improve its dynamic response ability. Dynamic simulation technology is introduced to support real-time update and prediction functions, and a dynamic digital twin water conservancy project model is generated, which has the ability of real-time dynamic analysis and can be applied to scenarios such as flood prediction and optimal scheduling.

[0037] Preferably, the correlation of hydrological structure data according to the three-dimensional geometric data of the water conservancy project includes: Extract the topographic features of the three-dimensional geometric data of the water conservancy project to obtain the topographic feature data of the water conservancy project; use the topographic feature data of the water conservancy project to match the hydrological element data of the standard multi-source water conservancy project dataset to obtain the hydrological element matching data; Perform watershed partition boundary analysis on the topographic feature data of the water conservancy project according to the hydrological element matching data to obtain the watershed partition boundary data; use the watershed partition boundary data to label the coordinates of the structural key points of the three-dimensional geometric data of the water conservancy project to obtain the coordinate data of the structural key points; Construct hydrological structure patches for the watershed partition boundary data based on the structural key point coordinate data to obtain hydrological structure patch data; associate hydrological structure attributes with the hydrological structure patch data to obtain hydrological structure association data; map the dynamic spatio-temporal characteristics of the hydrological structure association data to obtain dynamic hydrological structure characteristic data; Perform an integrated spatial modeling of hydrological and geometric data on the dynamic hydrological structure characteristic data to obtain a digital twin basic model.

[0038] In the embodiments of the present invention, based on the three-dimensional geometric data of water conservancy projects, terrain analysis algorithms (such as calculations of slope, aspect, curvature, etc.) are used to extract terrain features, including river channels, ridge lines, depressions, etc. The terrain analysis results are utilized to generate water conservancy project terrain feature data, including feature points (such as the lowest point, the highest point), feature lines (such as contour lines), feature surfaces (such as watershed surfaces), etc. GIS tools (such as ArcGIS, QGIS) or programming tools (such as the GDAL library of Python) are used to calculate terrain features. The slope, aspect, and curvature information are extracted and output in a vector data format (such as Shapefile, GeoJSON). By matching the terrain feature data with a standard multi-source water conservancy project dataset (including river networks, rain gauges, water storage areas, etc.), hydrological elements (such as watershed scope, confluence path) are associated. Using spatial overlay analysis, the three-dimensional geometric data is aligned with the hydrological dataset to generate hydrological element matching data. Spatial analysis tools (such as the Spatial Analyst module of ArcGIS) or the Geopandas library of Python are used for spatial matching. Euclidean distance or shape similarity algorithms are adopted to evaluate the matching degree of feature points, lines, and surfaces with hydrological elements. A hydrological analysis model (such as a watershed line extraction algorithm) is used to determine the watershed partition boundary based on the hydrological element matching data. The catchment area of each watershed is extracted to generate watershed partition boundary data, which is combined with the water conservancy project terrain features. The ArcGIS Hydrology tool or the TauDEM library in Python is used to extract the watershed boundary, generating vector data containing the watershed boundary, area, and confluence path. Based on the watershed partition boundary data, the coordinates of structural key points (such as the center point of the dam, the position of the flood discharge outlet, the monitoring station) are marked in the three-dimensional geometric data of the water conservancy project. The three-dimensional coordinates of the key points and their hydrological significance in the watershed are determined. Point cloud processing tools (such as CloudCompare) are used to extract the coordinates of the key points. The extracted key point data is output in a CSV or 3D file format (such as PLY, OBJ). Using the structural key point coordinate data and the watershed partition boundary, three-dimensional hydrological structure patches are constructed, including dams, diversion channels, spillways, etc. The patches are combined with the terrain surface to generate a refined three-dimensional hydrological structure model. Triangulation algorithms (such as Delaunay triangulation) are used to generate hydrological structure patches. Patch optimization and visualization are performed through 3D modeling tools (such as Blender, Rhino). The hydrological structure patch data is associated with hydrological attributes (such as flow rate, water storage capacity) to generate a structure attribute table, describing the hydrological characteristics of each patch. The attributes include flow distribution, rainfall response time, drainage capacity, etc. A database (such as PostGIS) is used to store and manage the hydrological structure attribute association data. SQL queries are written to achieve dynamic update and extraction of hydrological structure attributes. Dynamic hydrological data (such as real-time rainfall, reservoir water level) is used to perform spatio-temporal mapping on the hydrological structure to generate dynamic hydrological feature data.Predict hydrological events (such as flood peaks) by combining the temporal and spatial change trends of the watershed. Use spatio-temporal analysis models (such as Hec-RAS, MIKE FLOOD) to simulate hydrological dynamics. Visualization tools (such as Cesium, ParaView) are used to display the dynamic changes. Integrate the dynamic hydrological structure feature data with 3D geometric data to construct a digital twin basic model for hydrological prediction, structure monitoring and optimization. The model integrates three elements: terrain, structure, and hydrology to achieve real-time simulation and intelligent analysis. Use digital twin platforms (such as Unity, Unreal Engine) for model construction. Utilize physical engines and simulation tools (such as Ansys, COMSOL) to enhance the dynamic simulation ability.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Deploy area sensors based on the dynamic digital twin water conservancy project model to obtain key area sensor deployment data; construct an Internet of Things architecture based on the key area sensor deployment data to generate a water conservancy project Internet of Things architecture; Step S22: Embed the water conservancy project Internet of Things architecture into the dynamic digital twin water conservancy project model for remote monitoring of the water conservancy project to generate water conservancy project monitoring data; perform data time series differencing on the water conservancy project monitoring data to generate water conservancy project time series differencing monitoring data; Step S23: Perform composite state perception of the water conservancy project on the water conservancy project time series differencing monitoring data to generate water conservancy project composite state perception data; map the composite state perception data of the water conservancy project to obtain a water conservancy project dynamic monitoring map; Step S24: Predict the water conservancy project fault points on the water conservancy project composite state perception data according to the water conservancy project dynamic monitoring map to generate water conservancy project fault point data; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data.

[0040] In the embodiments of the present invention, key points in the water conservancy project area are analyzed through a dynamic digital twin water conservancy project model. Considering terrain, water flow pressure, structural stress, and environmental factors comprehensively, key monitoring areas are determined, and planning data for sensor deployment in key areas is generated. Based on the planning data for sensor deployment in key areas, appropriate sensor types (such as pressure sensors, flow sensors, and stress sensors) are selected, the physical installation and networking of sensors are completed, and data for sensor deployment in key areas is generated. Based on the sensor deployment data, a multi-level Internet of Things architecture is designed, including a sensing layer, an edge computing layer, and a cloud computing layer. The construction of a data acquisition, transmission, and processing network is completed, and an Internet of Things architecture for the water conservancy project is generated. The Internet of Things architecture for the water conservancy project is integrated with the dynamic digital twin water conservancy project model, and the twin model is updated through real-time data streams to achieve remote monitoring of the water conservancy project, and monitoring data for the water conservancy project is generated. Time series analysis is performed on the monitoring data for the water conservancy project, and differential calculation methods are used to extract the change trends of key variables (such as pressure, flow velocity, and structural deformation), and time series differential monitoring data for the water conservancy project is generated. Data fusion and feature extraction are performed on the time series differential monitoring data for the water conservancy project. Combining structural mechanics, hydrodynamics, and environmental conditions, the composite operation state of the water conservancy project is identified, and composite state perception data for the water conservancy project is generated. The composite state perception data is graphically expressed using a graph data model, and the node representation (such as monitoring points) and edge relationships (such as water flow influence paths) are constructed to generate a dynamic monitoring graph for the water conservancy project, intuitively reflecting the overall operation state of the water conservancy project and the relevance of key areas. The fault point data is applied to the dynamic digital twin water conservancy project model to simulate fault scenarios (such as water flow blockage, structural failure, or pressure overload), and multi-scenario simulations are performed under different parameters to generate scenario simulation data for the water conservancy project, providing decision support for optimized design and emergency plans.

[0041] Preferably, the real-time state perception of the water conservancy project for the time series differential monitoring data of the water conservancy project includes: Performing biodiversity analysis on the time series differential monitoring data of the water conservancy project to generate biological monitoring data for the water conservancy project area, where the biodiversity analysis includes the distribution analysis of fish, algae, and microbial communities; performing nutrient cycle calculations based on the biological monitoring data for the water conservancy project area to obtain nutrient cycle data for the water conservancy project area; Performing state perception of engineering facilities on the dynamic digital twin water conservancy project model based on the nutrient cycle data for the water conservancy project area to generate engineering facility perception data for the water conservancy project; performing water body connectivity perception on the nutrient cycle data for the water conservancy project area through the engineering facility perception data for the water conservancy project to generate environmental perception data for the water conservancy project; Integrating the engineering facility perception data and the environmental perception data for the water conservancy project to obtain composite state perception data for the water conservancy project.

[0042] In the embodiments of the present invention, time series data of fish, algae, and microbial communities in the water body are collected by sensors deployed in the surrounding area of the water conservancy project, including biological species, quantities, distributions, etc. The collected data is cleaned to remove noise and outliers, and data smoothing is performed to ensure the stability and accuracy of the data. Missing values are filled by interpolation method, and the data is standardized. The underwater image recognition technology, such as convolutional neural network (CNN), is used to identify the species of fish in the water body, and the types and quantities of fish are marked. Through spatial interpolation technology (such as Kriging interpolation method), spatial distribution analysis is carried out based on the collected fish data to determine the distribution characteristics of fish populations in the water conservancy project area. Combining the time series data, time series analysis methods (such as ARIMA model) are used to analyze the time-varying trends of fish distribution, and fish diversity data is generated. The density and types of algae are monitored by water quality sensors, and remote sensing technology is combined to obtain the algae distribution information in a large area. Genomics technology, such as 16S rRNA sequencing method, is used to collect microbial gene data in the water body, and the types and abundances of microbial communities are analyzed. The fish, algae, and microbial data are fused, and clustering algorithms (such as K-means algorithm) are used to identify different regions and change patterns of biological communities, forming regional biological monitoring data. Time series data of nutrients (such as nitrogen, phosphorus, potassium, etc.) in the water body are obtained through water quality sensors, and biological diversity data and water quality data are integrated to construct a comprehensive water body environment dataset. An ecological model (such as an ecological network model) is used to calculate the nutrient cycle in the water conservancy project area, with a focus on analyzing the flow and transformation of substances such as nitrogen and phosphorus in the water body. The sources of nitrogen in the water body (such as agricultural runoff, urban sewage) and their transformation processes (such as nitrification, denitrification, etc.) are analyzed. The processes of phosphorus release, adsorption, and precipitation are simulated, considering the interactions between algae, microorganisms, and phosphorus. According to the model results, nutrient cycle data in the region is generated and dynamically updated. Based on the design drawings and real-time monitoring data of the water conservancy project, a digital twin model of the water conservancy project is constructed, which includes information such as water body flow and facility status. Real-time sensor data such as water level, flow rate, temperature, and pressure are integrated and connected to the digital twin model to form a dynamically updated virtual water conservancy project model. Through the real-time monitoring system, the operation data of key facilities such as pumps, gates, and water conveyance pipelines are obtained, and the working status of the facilities (normal, faulty, under maintenance, etc.) is evaluated. Combining the real-time monitoring data, the digital twin model is dynamically updated to ensure that the status information of the facilities is reflected in a timely manner. Through data such as water level and flow rate, the connection situation between water bodies is analyzed to identify water flow channels, blocking points, and potential water flow paths. Graph theory algorithms (such as the shortest path algorithm, network analysis) are used to establish a connectivity model between water bodies to analyze the flow relationship between different regions of the water body. Graph theory algorithms (such as the shortest path algorithm, network analysis) are used to establish a connectivity model between water bodies to analyze the flow relationship between different regions of the water body.Update environmental data in real time, combine the facility status and the results of connectivity analysis to generate dynamic environmental perception data for water conservancy projects, and provide comprehensive feedback. Integrate the perception data of water conservancy project facilities with environmental perception data (such as water quality, connectivity, etc.), and use the weighted average method or fuzzy logic method to integrate them into composite status perception data. Based on the composite status perception data, combined with artificial intelligence algorithms (such as deep learning models), intelligently evaluate the operation status of water conservancy projects, and put forward improvement suggestions and early warning information. Based on the composite status perception data, automatically generate a real-time status report of water conservancy projects, and the report content includes information such as biodiversity, nutrient cycling, and facility operation status for the reference of management decision-making.

[0043] Preferably, step S24 includes the following steps: Step S241: Perform structure association of controllable devices in the water conservancy project on the composite status perception data of the water conservancy project according to the dynamic monitoring atlas of the water conservancy project to generate associated data of controllable devices in the water conservancy project; collect historical fault data of the controllable devices in the water conservancy project to obtain historical fault records of the controllable devices in the water conservancy project; Step S242: Divide the historical fault records of the controllable devices in the water conservancy project into a model training set and a model test set; perform model training on the model training set through the support vector machine algorithm to generate a pre-model for predicting fault points in the water conservancy project; use the model test set to perform model optimization iteration on the pre-model for predicting fault points in the water conservancy project, so as to generate a prediction model for fault points in the water conservancy project; Step S243: Import the associated data of controllable devices in the water conservancy project into the prediction model for fault points in the water conservancy project to predict fault points and generate fault point data of the water conservancy project; Step S244: Based on the fault point data of the water conservancy project, set up a simulation scenario for the dynamic digital twin water conservancy project model to generate a simulation scenario for fault simulation of the water conservancy project; perform emergency operation and maintenance response simulation on the associated data of controllable devices in the water conservancy project through the simulation scenario for fault simulation of the water conservancy project to generate simulation data for the water conservancy project scenario.

[0044] In the embodiments of the present invention, by combining time series differential monitoring data, a dynamic monitoring map of the water conservancy project is constructed to monitor data such as water level, flow velocity, and equipment status, and a map of the interaction between equipment operation and the environment is generated. Based on the dynamic monitoring map, the shortest path algorithm and network analysis method in graph theory are used to establish the association structure between various controllable devices inside the water conservancy project. For example, the monitoring data analysis results show that there is a close association between certain devices (such as the linkage between pumps and gates), thereby identifying key device relationships. According to the structural relationship between devices, controllable device association data is automatically generated, and these data include the relationship diagram between devices, operation modes, and potential fault points. Using the historical fault database, relevant device historical fault records are extracted from the equipment management system of the water conservancy project, and these fault records include information such as the time, type, duration, and maintenance records of equipment failures. The historical fault records are integrated with data such as the operation status and maintenance cycle of the equipment to form a complete equipment fault history data set. The historical fault records of the controllable devices in the water conservancy project are divided into a training set and a test set according to a certain proportion. Usually, the training set accounts for 70%-80% of the data set, and the test set accounts for 20%-30%. Feature data helpful for prediction is extracted from the historical fault records, such as equipment fault time, fault frequency, environmental factors, equipment operation parameters, etc. The SVM algorithm is used to train the training set, and common kernel functions such as the RBF kernel function are used to train the data so that the model can capture non-linear features. The classification effect is improved by continuously optimizing parameters (such as C value, γ value). The SVM model separates the fault points from the non-fault points to form a hyperplane, separating the fault samples and non-fault samples in the historical data, and providing the model with the ability to predict future faults. The trained SVM model is verified using the test set to evaluate the accuracy of the model when predicting unknown data. The model parameters are optimized through techniques such as cross-validation and grid search. According to the prediction results of the test set, the model is continuously adjusted, and techniques such as adaptive algorithms or transfer learning are used for optimization to ensure that the model can adapt to changes in different environments of the water conservancy project. The association data of the controllable devices in the water conservancy project (including the relationships between devices, state changes, etc.) is imported into the fault point prediction model. At this time, the model predicts the upcoming faults by associating the historical fault records of the devices with the current operation status of the devices. Based on the prediction model, combined with information such as the current working status of the device, historical fault characteristics, and environmental impacts, the time point and fault type of the fault are predicted (for example, faults caused by excessive equipment pressure, wear faults caused by long-term operation of the equipment, etc.). The prediction results are generated into fault point data, including information such as the predicted fault occurrence time, fault type, and affected area, providing a reference for subsequent emergency response and operation and maintenance management. According to the water conservancy project fault point data, a simulation scenario of the dynamic digital twin water conservancy project model is constructed.For example, if it is predicted that a certain device will malfunction, the operation of the device during the malfunction can be simulated to mimic the impact process after the malfunction occurs. Set the scenarios of the malfunction in the simulation model, such as equipment shutdown, flow rate change, pressure fluctuation, etc., to simulate the impact of different types of malfunctions on the water conservancy project. Set the emergency operation and maintenance response strategies according to the malfunction simulation settings, such as starting backup equipment, adjusting the flow rate, and performing flood discharge operations. Evaluate the effectiveness of the emergency response plan through the simulation system to ensure that effective countermeasures can be taken promptly when a malfunction occurs. Through the emergency operation and maintenance response simulation, complete water conservancy project scenario simulation data is generated, including data such as equipment recovery time, response effect, and resource consumption, to provide support for decision-making.

[0045] Preferably, the emergency operation and maintenance response simulation of the associated data of the controllable equipment of the water conservancy project through the scenario setting of the water conservancy project malfunction simulation includes the following steps: Divide the scenario set by the water conservancy project malfunction simulation into a normal operation scenario, a malfunction trigger scenario, and a post-malfunction expansion scenario, and respectively set the scenario simulation order for the normal operation scenario, the malfunction trigger scenario, and the post-malfunction expansion scenario to obtain a scenario simulation sequence; Extract the associated data of the gate equipment and the associated data of the pumping station equipment in the associated data of the controllable equipment of the water conservancy project; perform data association analysis on the associated data of the gate equipment and the associated data of the pumping station equipment and the water conservancy project malfunction point data respectively to generate gate opening influence data and pumping station reservoir capacity regulation data; Perform simulation anomaly feedback on the gate opening influence data and the pumping station reservoir capacity regulation data through the scenario simulation sequence to obtain simulation anomaly feedback data, which includes gate anomaly feedback data and pumping station anomaly feedback data; discriminate the anomaly type of the simulation anomaly feedback data. When the simulation anomaly feedback data is gate anomaly feedback data, restrict the gate opening and closing circuit of the corresponding gate to generate control circuit restriction data; When the simulation anomaly feedback data is pumping station anomaly feedback data, switch the high lift of the valve of the corresponding pumping station to generate pumping station valve restriction data; perform emergency operation and maintenance response simulation on the associated data of the controllable equipment of the water conservancy project based on the control circuit restriction data and the pumping station valve restriction data to generate water conservancy project scenario simulation data.

[0046] In the embodiments of the present invention, in this scenario, each facility of the water conservancy project operates normally, and the states of each device are maintained within a predetermined working range. For example, the pumping station operates normally, the gate remains at the set opening degree, and various parameters such as water flow and pressure are within the safe range. Different types of fault triggers are simulated, such as sudden device failures, abnormal water flow or water level fluctuations, etc. For example, the pumping station fails, the gate control system has an abnormality, etc. External or internal factors that cause these devices to operate abnormally are set. After a device fails, a more refined scenario setting is carried out to simulate the changes in other environmental variables such as water flow and pressure after the fault occurs, and analyze the impact of the fault expansion on the entire system. According to the above scenario division, a scenario simulation sequence is formulated. The set sequence generally includes: first execute the normal operation scenario to ensure that the system works under normal conditions, then trigger the fault trigger scenario to simulate the conditions for device failures, and finally in the post-fault expansion scenario, analyze the impact after the fault to ensure that the system emergency response can be effectively carried out. The system arranges the scenarios of normal operation, fault trigger, and fault expansion in chronological and conditional order to generate a complete scenario simulation sequence. Extract the associated data of the gate from the real-time monitoring system of the water conservancy project, mainly including key operation data such as the opening degree, closing speed, and adjustment parameters of the gate. Extract the device status data of the gate control, such as the operating condition of the gate motor, battery power, control signal, etc. Extract the associated data from the pumping station system, covering information such as the operating status of the pumping station, power consumption of the pump house, water output, and opening and closing status of the pumping station valves. Combine the reservoir capacity data of the pumping station pool to obtain the pumping station load status, real-time working status of the pumping station, and adjustment data. Analyze the associated data of the gate device, calculate the impact of the opening degree of the gate on the water flow and water level, and generate gate opening degree impact data. Analyze the associated data of the pumping station device, combine the reservoir capacity adjustment information, calculate the adjustment effect of the working status of the pumping station on the flow rate, and generate pumping station reservoir capacity adjustment data. When the scenario simulation sequence is executed, if an abnormality in the gate opening degree is found during the simulation process (for example, too large or too small, resulting in abnormal water flow regulation), the system will generate gate abnormality feedback data, recording the time, type, and reason of the abnormality. If problems such as abnormal adjustment of the pumping station valve and too high head occur during the simulation of the pumping station device, generate pumping station abnormality feedback data, recording the fault type of the pumping station, the device where the fault occurs, and the fault consequences. Through the set threshold or model, judge whether the feedback data belongs to the gate abnormality type. If the feedback data indicates that the opening degree adjustment exceeds the safe range, it is determined as a gate abnormality. Similarly, through the set threshold or feature recognition method, judge the pumping station feedback data. If the feedback data indicates a problem with the pumping station valve (such as too high or too low head), it is determined as a pumping station abnormality. When it is determined as a gate abnormality, the system will perform a loop restriction operation on the abnormal gate, such as closing or limiting the amplitude through the control loop, to prevent the water flow from being too large or too small, resulting in system out of control.In scenario simulation, the effects after simulating the control loop limitations are analyzed to determine their impact on parameters such as water level and water flow, ensuring the normal operation of the water conservancy project. When the pump station is determined to be abnormal, the system will perform a high-lift valve switching operation on the pump station, that is, automatically adjust the pump station valves to ensure a reasonable distribution of the pump station load and avoid equipment damage or reduced efficiency caused by excessive lift. The valve switching operation is carried out in the simulation scenario, and the working state of the pump station and the water flow regulation effect after the switching are analyzed. Based on the control measures of gate loop limitation and pump station valve switching, water conservancy project scenario simulation data is generated, including the equipment operation state, control effect, and recovery situation after the emergency response. Through the feedback of the scenario simulation data, emergency resources are dispatched, and necessary emergency operations are taken, such as starting standby pump stations and adjusting water levels, to ensure the system resumes normal operation as soon as possible.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Construct an operation and maintenance decision-making plan based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain a water conservancy project operation and maintenance decision-making plan; calculate the comprehensive score of the water conservancy project operation and maintenance decision-making plan to generate the score of the water conservancy project operation and maintenance decision-making plan; Step S32: Screen the optimal operation and maintenance plan for the water conservancy project operation and maintenance decision-making plan according to the score of the water conservancy project operation and maintenance decision-making plan, and send the selected optimal operation and maintenance plan to the water conservancy project intelligent control system for remote control execution to obtain water conservancy project remote control feedback data; Step S33: Convert the water conservancy project remote feedback data into an operation and maintenance data chart to generate a water conservancy project intelligent operation and maintenance report.

[0048] In the embodiments of the present invention, a decision-making solution is constructed based on the simulation data of the water conservancy project scenario and the composite state perception data of the water conservancy project. The simulation data includes information such as the operating state of equipment, prediction of fault points, emergency response, etc.; the composite state perception data covers real-time monitoring information such as water level, flow rate, and operation of pumping stations. Determine the key indicators for operation and maintenance decisions, such as equipment health status, emergency response ability, energy consumption, cost, etc. According to these variables, construct a set of operation and maintenance decision-making solutions. According to the preset objective functions (such as minimizing energy consumption, shortest recovery time, minimum failure probability, etc.), different operation and maintenance decision-making solutions are automatically generated. For example, different control modes, resource allocation strategies, or standby equipment activation strategies can be set. For each operation and maintenance decision-making solution, corresponding weights are given according to its different influencing factors (such as energy efficiency, equipment operation stability, fault recovery time, etc.). Calculate the comprehensive score of each decision-making solution through the weighted scoring method. Set the comprehensive score formula, for example: operation and maintenance solution score = α1 ⋅ equipment health degree + α2 ⋅ fault response time + α3 ⋅ energy consumption efficiency + α4 ⋅ cost-benefit, where α1, α2, α3, α4 are the weights of different indicators. Calculate the comprehensive scores of all generated operation and maintenance decision-making solutions and output the scores of each solution. Sort all operation and maintenance decision-making solutions from high to low according to their comprehensive scores. Select the solution with the highest score as the optimal operation and maintenance solution. In addition to the score, other screening criteria can be set, such as implementation feasibility, resource matching degree, project implementation cycle and other factors to further optimize the screening process. Extract the optimal solution with the highest score and prepare to send it to the intelligent control system of the water conservancy project. Transmit the selected optimal operation and maintenance solution to the intelligent control system of the water conservancy project, instructing the system to perform control operations according to the optimal solution. The intelligent control system performs remote control tasks according to the operation and maintenance solution, generating feedback data, which includes system response status, changes in equipment operation status, adjustment results, etc. Convert the remote control feedback data of the water conservancy project into charts to generate visual operation and maintenance data charts. Including but not limited to: equipment status charts (such as the opening and closing status of pumping stations and gates), dynamic curve charts of water level, flow rate, etc., and operation and maintenance index change charts (such as energy efficiency, frequency of fault occurrence, etc.). Generate dynamic interactive charts through real-time data updates to help decision-makers instantly view the operation and maintenance situation. Generate a detailed intelligent operation and maintenance report based on the converted chart data.

[0049] Preferably, step S4 includes the following steps: Step S41: Store the intelligent operation and maintenance report of the water conservancy project in the cloud platform and set an automatic backup mechanism; perform data backup on the intelligent operation and maintenance report of the water conservancy project according to the automatic backup mechanism to generate intelligent operation and maintenance backup data of the water conservancy project; Step S42: Perform multi-platform data sharing on the intelligent operation and maintenance report of the water conservancy project and the intelligent operation and maintenance backup data through the API interface to perform collaborative operation and maintenance operations on the water conservancy project.

[0050] In the embodiments of the present invention, the generated intelligent operation and maintenance report is uploaded to the cloud platform for storage through a data transmission channel. Select a suitable cloud storage service (such as AWS, Google Cloud, Azure, etc.), and store the report data in a cloud server or database. Use the HTTPS protocol for secure transmission. According to the type and importance of the data, different storage methods (such as SQL databases, object storage, etc.) are used. When storing the report, a unique identifier (such as UUID) is generated to distinguish reports of different versions and time points. Set up a regular backup mechanism, and determine the backup frequency according to business needs (such as daily, weekly, monthly, etc.). The backup can be an incremental backup or a full backup. Only back up the data that has changed since the last backup, or back up all the data to ensure complete recovery even in case of data loss. Regularly back up the intelligent operation and maintenance report of the water conservancy project through an automated script or the automatic backup service provided by the cloud platform. The backup data includes the report content, generation time, version information, etc. Use the automated tools of the cloud platform, such as AWS Lambda or Google Cloud Functions, to set triggers (such as triggering at regular intervals or when the report is updated) for data backup. The backup data is encrypted and stored to ensure data security. After each backup, the system generates a backup file and assigns a separate identifier to the backup data for future recovery or viewing of historical backup versions.

[0051] In this specification, a digital twin water conservancy project operation and maintenance monitoring system is provided for implementing the above-mentioned digital twin water conservancy project operation and maintenance monitoring method. The digital twin water conservancy project operation and maintenance monitoring system includes: A digital twin module, which is used to obtain water conservancy project-related data; extract three-dimensional geometric features from the water conservancy project-related data to obtain three-dimensional geometric data of the water conservancy project; perform hydrological structure data association based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; and perform global optimization on the digital twin basic model to generate a dynamic digital twin water conservancy project model; An engineering analysis module, which is used to perform remote monitoring of the water conservancy project based on the dynamic digital twin water conservancy project model to generate water conservancy project monitoring data; perform water conservancy project composite state perception on the water conservancy project monitoring data to generate a water conservancy project dynamic monitoring atlas; predict water conservancy project fault points based on the water conservancy project dynamic monitoring atlas for the water conservancy project composite state perception data to generate water conservancy project fault point data; and perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; An operation and maintenance decision-making module, which is used to construct an operation and maintenance decision-making plan based on the scene simulation data of the water conservancy project and the composite state perception data of the water conservancy project, and obtain the operation and maintenance decision-making plan for the water conservancy project; send the operation and maintenance decision-making plan for the water conservancy project to the intelligent control system of the water conservancy project for remote control execution, and generate an intelligent operation and maintenance report for the water conservancy project; A data sharing module, which is used to back up the data of the intelligent operation and maintenance report of the water conservancy project to generate the intelligent operation and maintenance backup data of the water conservancy project; perform multi-platform data sharing on the intelligent operation and maintenance report of the water conservancy project and the intelligent operation and maintenance backup data through the API interface to execute the collaborative operation and maintenance operation of the water conservancy project.

[0052] The beneficial effects of the present invention are as follows: The digital twin module constructs a dynamic digital twin water conservancy project model through three-dimensional geometric feature extraction and hydrological structure data association, realizing the comprehensive digital and virtual management of the water conservancy project. It supports the global optimization of complex water conservancy projects and provides a high-precision data model for subsequent monitoring and simulation. The engineering analysis module collects the operation data of the water conservancy project in real time through remote monitoring, generates a dynamic monitoring map, and comprehensively perceives the operation state of the project. Using the fault point prediction technology, it can identify potential risk points before the occurrence of a fault, improve the early warning ability, and reduce the probability of sudden accidents. The operation and maintenance decision-making module combines the scene simulation data and the state perception data to construct an efficient operation and maintenance decision-making plan, ensuring the scientificity and pertinence of the decision-making. The remote control function reduces the dependence on on-site manual intervention, greatly improves the operation and maintenance efficiency, and reduces the operation cost. The data sharing module realizes the efficient distribution and collaborative operation of the intelligent operation and maintenance report through data backup and multi-platform data sharing: each department and platform carry out work synchronously based on the shared data, improving the collaborative efficiency. It supports cross-platform data access and operation, promoting the intelligence and informatization of water conservancy project management. The data backup function ensures the safe storage and disaster recovery ability of the operation and maintenance data, and can be quickly restored even in the event of an emergency. Through the whole life cycle monitoring, analysis, decision-making and sharing, a closed-loop water conservancy project management process is established to ensure the long-term stable operation of the system. The multi-scene simulation function can simulate different operating conditions, providing decision-making support for the design optimization, risk assessment and emergency response of water conservancy projects. It improves the adaptability and resilience of water conservancy projects and effectively responds to complex and changeable operating environments. Therefore, the present invention constructs a dynamic model through digital twin technology, realizing the precision, intelligence and collaboration of water conservancy project operation and maintenance monitoring, and improving the precision and intelligence level of water conservancy project operation and maintenance monitoring.

[0053] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0054] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital twin water conservancy project operation and maintenance monitoring method, characterized in that: The following steps are involved: Step S1: Acquire relevant data of the water conservancy project; extract three-dimensional geometric features of the relevant data of the water conservancy project to obtain three-dimensional geometric data of the water conservancy project; associate hydrological structure data according to the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; globally optimize the digital twin basic model to generate a dynamic digital twin water conservancy project model; Step S2: remotely monitor the water conservancy project based on the dynamic digital twin water conservancy project model to generate water conservancy project monitoring data; perceive the composite state of the water conservancy project on the water conservancy project monitoring data to generate a dynamic monitoring map of the water conservancy project; predict the fault point of the water conservancy project on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project to generate the fault point data of the water conservancy project; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the fault point data of the water conservancy project to generate the scenario simulation data of the water conservancy project; Step S3: constructing an operation and maintenance decision plan based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain a water conservancy project operation and maintenance decision plan; sending the water conservancy project operation and maintenance decision plan to the water conservancy project intelligent control system for remote control execution, and generating a water conservancy project intelligent operation and maintenance report; Step S4: backing up the water conservancy project intelligent operation and maintenance report to generate water conservancy project intelligent operation and maintenance backup data; Through the API interface, the intelligent operation and maintenance reports and backup data of water conservancy projects are shared on multiple platforms to perform collaborative operation and maintenance of water conservancy projects.

2. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire water conservancy project related data, wherein the water conservancy project related data includes topographic data, meteorological data and hydrological data; Step S12: preprocessing the terrain data, meteorological data and hydrological data, and integrating the preprocessed terrain data, meteorological data and hydrological data into a standard multi-source water conservancy project data set, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: extracting three-dimensional geometric features based on a standard multi-source water conservancy project data set to obtain three-dimensional geometric data of the water conservancy project; associating hydrological structure data based on the three-dimensional geometric data of the water conservancy project to generate a digital twin basic model; Step S14: extract the spatiotemporal characteristics of the standard multi-source water conservancy project data set to obtain the spatiotemporal characteristic data of the water conservancy project; use the spatiotemporal characteristic data of the water conservancy project to globally optimize the digital twin basic model to generate a dynamic digital twin water conservancy project model.

3. The digital twin water conservancy project operation and maintenance monitoring method according to claim 2 is characterized in that: The hydrological structure data association based on the 3D geometric data of the water conservancy project includes: Extract terrain features from the three-dimensional geometric data of the water conservancy project to obtain terrain feature data of the water conservancy project; use the terrain feature data of the water conservancy project to match the hydrological element data of the standard multi-source water conservancy project data set to obtain hydrological element matching data; According to the hydrological element matching data, the watershed division boundary analysis is performed on the water conservancy project terrain feature data, so as to obtain the watershed division boundary data; the watershed division boundary data is used to mark the structural key point coordinates of the water conservancy project three-dimensional geometric data, so as to obtain the structural key point coordinate data; According to the coordinate data of key structural points, the hydrological structure patch is constructed for the watershed partition boundary data, so as to obtain the hydrological structure patch data; the hydrological structure patch data is associated with the hydrological structure attributes, so as to obtain the hydrological structure associated data; the hydrological structure associated data is mapped with dynamic spatiotemporal characteristics, so as to obtain the dynamic hydrological structure characteristic data; The dynamic hydrological structure characteristic data is spatially modeled by integrating hydrological and geometric data to obtain the digital twin basic model.

4. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: deploy regional sensors based on the dynamic digital twin water conservancy project model to obtain key area sensor deployment data; construct an Internet of Things architecture based on the key area sensor deployment data to generate a water conservancy project Internet of Things architecture; Step S22: embedding the water conservancy project Internet of Things architecture into the dynamic digital twin water conservancy project model to perform remote monitoring of the water conservancy project and generate water conservancy project monitoring data; performing data time series differentiation on the water conservancy project monitoring data to generate water conservancy project time series differential monitoring data; Step S23: Performing water conservancy project composite state perception on the water conservancy project time series differential monitoring data to generate water conservancy project composite state perception data; performing perception mapping on the water conservancy project composite state perception data to obtain a water conservancy project dynamic monitoring map; Step S24: Predict the fault points of the water conservancy project based on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project, and generate the fault point data of the water conservancy project; perform multi-scenario simulation on the dynamic digital twin water conservancy project model based on the fault point data of the water conservancy project, and generate the water conservancy project scenario simulation data.

5. The digital twin water conservancy project operation and maintenance monitoring method according to claim 4 is characterized in that: Real-time status perception of water conservancy projects based on time series differential monitoring data of water conservancy projects includes: Conduct biodiversity analysis on the time-series difference monitoring data of water conservancy projects to generate biological monitoring data for water conservancy projects, where the biodiversity analysis includes the distribution analysis of fish, algae and microbial communities; perform nutrient cycle calculation based on the biological monitoring data for water conservancy projects to obtain nutrient cycle data for water conservancy projects; The dynamic digital twin water conservancy project model is used to perceive the status of engineering facilities based on the nutrient cycle data in the water conservancy project area, and the water conservancy project facility perception data is generated; the water body connectivity is perceived based on the nutrient cycle data in the water conservancy project area through the water conservancy project facility perception data, thereby generating water conservancy project environment perception data; Integrate the water conservancy project facility perception data and the water conservancy project environment perception data to obtain the water conservancy project composite state perception data.

6. The digital twin water conservancy project operation and maintenance monitoring method according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: performing water conservancy project controllable equipment structure association with the water conservancy project composite state perception data according to the water conservancy project dynamic monitoring map to generate water conservancy project controllable equipment association data; performing equipment historical fault data collection on the water conservancy project controllable equipment association data to obtain water conservancy project controllable equipment historical fault records; Step S242: dividing the historical fault records of the controllable equipment of the water conservancy project into a model training set and a model test set; performing model training on the model training set by using a support vector machine algorithm to generate a water conservancy project fault point prediction pre-model; using the model test set to perform model optimization iteration on the water conservancy project fault point prediction pre-model, thereby generating a water conservancy project fault point prediction model; Step S243: importing the controllable equipment associated data of the water conservancy project into the water conservancy project fault point prediction model to perform fault point prediction and generate water conservancy project fault point data; Step S244: Based on the water conservancy project fault point data, a simulation scenario is set for the dynamic digital twin water conservancy project model to generate a water conservancy project fault simulation setting scenario; emergency operation and maintenance response simulation is performed on the associated data of the controllable equipment of the water conservancy project through the water conservancy project fault simulation setting scenario to generate water conservancy project scenario simulation data.

7. The digital twin water conservancy project operation and maintenance monitoring method according to claim 6 is characterized in that: The emergency operation and maintenance response simulation of the controllable equipment associated data of the water conservancy project through the water conservancy project fault simulation setting scenario includes the following steps: The water conservancy project fault simulation setting scenario is divided into scenarios to obtain normal operation scenarios, fault triggering scenarios and post-fault expansion scenarios, and the scenario simulation sequence is set for the normal operation scenarios, fault triggering scenarios and post-fault expansion scenarios respectively to obtain scenario simulation sequences; Extract gate equipment-related data and pump station equipment-related data from the controllable equipment-related data of the water conservancy project; perform data association analysis on the gate equipment-related data, pump station equipment-related data and water conservancy project fault point data, respectively, to generate gate opening influence data and pump station storage capacity adjustment data; Through the scenario simulation sequence, the gate opening influence data and the pump station storage capacity regulation data are simulated for abnormal feedback to obtain the simulated abnormal feedback data, which includes the gate abnormal feedback data and the pump station abnormal feedback data; the abnormal type of the simulated abnormal feedback data is identified, and when the simulated abnormal feedback data is the gate abnormal feedback data, the gate opening and closing loop is restricted for the corresponding gate to generate the control loop restriction data; When the simulation abnormal feedback data is the abnormal feedback data of the pump station, the valve high lift switch is performed on the corresponding pump station to generate the pump station valve restriction data; based on the control loop restriction data and the pump station valve restriction data, the emergency operation and maintenance response simulation is performed on the associated data of the controllable equipment of the water conservancy project to generate the water conservancy project scenario simulation data.

8. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: constructing an operation and maintenance decision plan based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain a water conservancy project operation and maintenance decision plan; calculating a comprehensive score of the water conservancy project operation and maintenance decision plan to generate a water conservancy project operation and maintenance decision plan score; Step S32: Screening the optimal operation and maintenance plan for the water conservancy project operation and maintenance decision plan according to the water conservancy project operation and maintenance decision plan score, and sending the screened optimal operation and maintenance plan to the water conservancy project intelligent control system for remote control execution to obtain water conservancy project remote control feedback data; Step S33: converting the remote feedback data of the water conservancy project into operation and maintenance data charts, thereby generating an intelligent operation and maintenance report for the water conservancy project.

9. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: storing the water conservancy project intelligent operation and maintenance report to the cloud platform, and setting an automatic backup mechanism; performing data backup of the water conservancy project intelligent operation and maintenance report according to the automatic backup mechanism, and generating water conservancy project intelligent operation and maintenance backup data; Step S42: Share the water conservancy project intelligent operation and maintenance report and the water conservancy project intelligent operation and maintenance backup data on multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

10. A digital twin water conservancy project operation and maintenance monitoring system, characterized in that: Used to execute the digital twin water conservancy project operation and maintenance monitoring method as claimed in claim 1, the digital twin water conservancy project operation and maintenance monitoring system comprises: The digital twin module is used to obtain water conservancy project related data; extract three-dimensional geometric features of water conservancy project related data to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data with the three-dimensional geometric data of water conservancy projects to generate a digital twin basic model; globally optimize the digital twin basic model to generate a dynamic digital twin water conservancy project model; The engineering analysis module is used to remotely monitor the water conservancy project based on the dynamic digital twin water conservancy project model and generate water conservancy project monitoring data; to perceive the composite state of the water conservancy project based on the water conservancy project monitoring data and generate a dynamic monitoring map of the water conservancy project; to predict the fault points of the water conservancy project based on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project and generate the fault point data of the water conservancy project; to simulate multiple scenarios of the dynamic digital twin water conservancy project model based on the fault point data of the water conservancy project and generate the scenario simulation data of the water conservancy project; The operation and maintenance decision module is used to construct an operation and maintenance decision plan based on the water conservancy project scenario simulation data and the water conservancy project composite state perception data to obtain the water conservancy project operation and maintenance decision plan; send the water conservancy project operation and maintenance decision plan to the water conservancy project intelligent control system for remote control execution, and generate a water conservancy project intelligent operation and maintenance report; The data sharing module is used to back up the intelligent operation and maintenance report of the water conservancy project and generate the intelligent operation and maintenance backup data of the water conservancy project; the intelligent operation and maintenance report of the water conservancy project and the intelligent operation and maintenance backup data of the water conservancy project are shared on multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

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