A digital twin water conservancy project operation and maintenance monitoring system and method

Abstract

This invention relates to the field of operation and maintenance management technology, and in particular to a digital twin water conservancy project operation and maintenance monitoring system and method. The method includes the following steps: acquiring relevant data of the water conservancy project; extracting three-dimensional geometric features from the relevant data to obtain three-dimensional geometric data of the water conservancy project; associating hydrological structure data based on the three-dimensional geometric data to generate a digital twin basic model; globally optimizing the digital twin basic model to generate a dynamic digital twin water conservancy project model; conducting remote monitoring of the water conservancy project based on the dynamic digital twin model to generate water conservancy project monitoring data; and performing composite state perception on the water conservancy project monitoring data to generate a dynamic monitoring map of the water conservancy project. This invention constructs a dynamic model using digital twin technology, achieving precision, intelligence, and collaboration in water conservancy project operation and maintenance monitoring, and improving the accuracy and intelligence level of water conservancy project operation and maintenance monitoring.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management technology, and in particular to a digital twin water conservancy project operation and maintenance monitoring system and method. Background Technology

[0002] Early water conservancy project monitoring relied primarily on manual inspections and single-sensor monitoring, making it difficult to achieve real-time, refined monitoring of large-scale water conservancy facilities. With the development of the Internet of Things (IoT), sensor technology, and wireless communication, multi-source data acquisition and transmission gradually became possible, forming a basic digital monitoring system for water conservancy projects. Entering the era of big data, improved data storage and analysis capabilities have injected new momentum into the operation and maintenance of water conservancy projects. Combined with technologies such as GIS, remote sensing, and satellite navigation, traditional monitoring methods are gradually moving towards intelligence and informatization. However, due to limitations in data processing and integration capabilities, monitoring information still faces problems of silos and low timeliness, failing to fully support complex operation and maintenance needs. In recent years, the rise of digital twin technology has provided a completely new solution for water conservancy project operation and maintenance monitoring. Through a combination of virtual and physical elements, digital twins can accurately reproduce the physical state, operating conditions, and environmental impacts of water conservancy projects in virtual space, achieving dynamic monitoring, precise analysis, and predictive optimization throughout the entire lifecycle. However, current traditional water conservancy project monitoring relies on fixed sensors or manual inspections, which have limited real-time performance and accuracy. At the same time, operation and maintenance decisions rely heavily on human experience, resulting in a low level of intelligence. Consequently, the accuracy and intelligence level of water conservancy project operation and maintenance monitoring are relatively low. Summary of the Invention

[0003] Therefore, 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-mentioned technical problems.

[0004] To achieve the above objectives, a digital twin water conservancy project operation and maintenance monitoring method is provided, the method comprising the following steps:

[0005] Step S1: Obtain relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects 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;

[0006] Step S2: Conduct 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 composite state perception on the water conservancy project monitoring data to generate a dynamic monitoring map of the water conservancy project; predict water conservancy project fault points based on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project 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.

[0007] Step S3: Based on the simulation data of water conservancy engineering scenarios and the composite state perception data of water conservancy engineering, construct the operation and maintenance decision scheme to obtain the operation and maintenance decision scheme of water conservancy engineering; send the operation and maintenance decision scheme of water conservancy engineering to the intelligent control system of water conservancy engineering for remote control execution, and generate the intelligent operation and maintenance report of water conservancy engineering.

[0008] Step S4: Back up the intelligent operation and maintenance report of the water conservancy project to generate intelligent operation and maintenance backup data of the water conservancy project; share the intelligent operation and maintenance report and the intelligent operation and maintenance backup data of the water conservancy project across multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

[0009] This invention acquires relevant data from water conservancy projects and extracts three-dimensional geometric features, then combines this data with hydrological structure data to generate a digital twin basic model, achieving high-precision virtual mapping of water conservancy projects. Through global optimization, a dynamic digital twin water conservancy project model is generated, enhancing the model's dynamic adaptability and accuracy, laying the foundation for subsequent monitoring and operation and maintenance. Remote monitoring based on the dynamic digital twin model overcomes the limitations of traditional monitoring methods, achieving real-time and intelligent dynamic perception. The generation of dynamic monitoring maps and fault point data effectively improves the accuracy of fault prediction. Multi-scenario simulations verify the potential risks and feasibility of solutions under different operating scenarios, providing a scientific basis for operation and maintenance decisions. Combining scenario simulation data and state perception data to construct operation and maintenance decision-making schemes enhances the scientific rigor and relevance of operation and maintenance decisions. Remote execution by the intelligent control system enables automated and precise operation and maintenance, reducing human intervention. The generated intelligent operation and maintenance reports provide data support for continuous optimization of operation and maintenance strategies. Data backup of intelligent operation and maintenance reports ensures the security and integrity of operation and maintenance information; multi-platform data sharing through API interfaces breaks down data silos, promotes multi-party collaborative operation and maintenance, and improves operation and maintenance efficiency and resource utilization. Therefore, this invention constructs a dynamic model using digital twin technology, realizing the precision, intelligence, and collaboration of water conservancy project operation and maintenance monitoring, and improving the accuracy and intelligence level of water conservancy project operation and maintenance monitoring.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain relevant data for water conservancy projects, including topographic data, meteorological data, and hydrological data;

[0012] Step S12: Perform data preprocessing on 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 engineering dataset. The data preprocessing includes data cleaning, data denoising, missing value imputation, and data standardization.

[0013] Step S13: Extract three-dimensional geometric features based on the standard multi-source water conservancy engineering dataset to obtain three-dimensional geometric data of water conservancy engineering; perform hydrological structure data association based on the three-dimensional geometric data of water conservancy engineering to generate a digital twin basic model;

[0014] Step S14: Extract the spatiotemporal features of the standard multi-source water conservancy project dataset to obtain the spatiotemporal feature data of water conservancy projects; use the spatiotemporal feature data of water conservancy projects to perform global optimization of the digital twin basic model to generate a dynamic digital twin water conservancy project model.

[0015] This invention integrates multi-source topographic, meteorological, and hydrological data into a standard dataset through data cleaning, denoising, missing value imputation, and standardization. This standardization process improves data consistency and quality, facilitating subsequent analysis and modeling. By extracting three-dimensional geometric features, three-dimensional geometric data of the water conservancy project is generated, providing a precise geometric structural description for the establishment of a digital twin model. This refined geometric feature helps to more accurately simulate the physical form of the water conservancy project. Correlating the three-dimensional geometric data with 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 model's practicality, facilitating real-time monitoring and prediction of the impact of hydrological changes on the project. By extracting spatiotemporal feature data and using it to globally optimize the basic model, a dynamic digital twin model is generated. This optimization allows the model to dynamically adapt to changes in different time and space conditions, providing crucial capabilities for real-time monitoring and decision support of the project. The dynamic digital twin water conservancy project model can update in real time and accurately simulate the project's operational status, providing strong data support for water conservancy project design, risk prediction, disaster management, and resource allocation.

[0016] Preferably, the correlation of hydrological structure data based on the three-dimensional geometric data of water conservancy projects includes:

[0017] Topographic features are extracted from the three-dimensional geometric data of water conservancy projects to obtain topographic feature data of water conservancy projects; hydrological element data is matched with standard multi-source water conservancy project datasets using the topographic feature data of water conservancy projects to obtain hydrological element matching data;

[0018] Based on the hydrological element matching data, watershed zoning boundary analysis is performed on the topographic feature data of water conservancy projects to obtain watershed zoning boundary data; the coordinates of structural key points are marked on the three-dimensional geometric data of water conservancy projects using the watershed zoning boundary data to obtain structural key point coordinate data.

[0019] Hydrological structural patches are constructed from the watershed boundary data based on the coordinate data of key structural points to obtain hydrological structural patch data; hydrological structural attributes are associated with the hydrological structural patch data to obtain hydrological structural associated data; dynamic spatiotemporal feature mapping is performed on the hydrological structural associated data to obtain dynamic hydrological structural feature data.

[0020] A digital twin basic model is obtained by integrating hydrological and geometric data to perform spatial modeling of dynamic hydrological structural characteristic data.

[0021] This invention extracts topographic features from 3D geometric data and combines them with a standard multi-source water conservancy engineering dataset to achieve precise matching of hydrological elements, improving the accuracy of the matching between topography and hydrological characteristics and providing a high-quality data foundation for subsequent analysis. Based on the matched hydrological element data, watershed boundary analysis helps to accurately delineate different hydrological regions, clearly define boundary ranges, and provide precise spatial information for hydrological watershed management. By annotating the coordinates of key points in the 3D geometric data and constructing hydrological structure patches in conjunction with watershed boundary data, the model's ability to analyze complex hydrological and geometric characteristics is significantly enhanced, laying the foundation for refined modeling. Mapping dynamic spatiotemporal features to hydrological structure-related data enables the capture of the temporal and spatial dynamic changes in hydrological characteristics. The model can reflect the dynamic changes in the hydrological process in real time, improving timeliness and applicability. Through spatial integrated modeling of dynamic hydrological feature data and geometric data, a comprehensive digital twin basic model is generated, enabling seamless integration of geometric and hydrological information and achieving a leap from single data to multi-dimensional fusion. Digital twin foundational models, centered on dynamic hydrological structural feature data, can be updated and optimized in real time, providing intelligent decision support for the design, risk assessment, and disaster management of water conservancy projects. Through the construction and correlation of watershed partitions and structural patches, the models can be used for hydrological management and optimization, improving the operational efficiency and reliability of water conservancy projects.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Deploy regional sensors based on the dynamic digital twin water conservancy engineering model to obtain key area sensor deployment data; construct the Internet of Things (IoT) architecture based on the key area sensor deployment data to generate the water conservancy engineering IoT architecture;

[0024] Step S22: Embed the IoT architecture of the water conservancy project into the dynamic digital twin water conservancy project model for remote monitoring of the water conservancy project, and generate water conservancy project monitoring data; perform time series difference on the water conservancy project monitoring data to generate water conservancy project time series difference monitoring data;

[0025] Step S23: Perform composite state perception on the time-series differential monitoring data of water conservancy projects to generate composite state perception data of water conservancy projects; perform perception mapping on the composite state perception data of water conservancy projects to obtain dynamic monitoring maps of water conservancy projects;

[0026] Step S24: Based on the dynamic monitoring map of the water conservancy project, predict the fault points of the water conservancy project from the composite state perception data of the water conservancy project, and generate water conservancy project fault point data; based on the water conservancy project fault point data, perform multi-scenario simulation on the dynamic digital twin water conservancy project model, and generate water conservancy project scenario simulation data.

[0027] This invention uses a dynamic digital twin model to accurately locate key areas and rationally deploy sensors, ensuring the comprehensiveness and efficiency of monitoring data. Based on sensor deployment data, an Internet of Things (IoT) architecture for water conservancy projects is constructed, achieving 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. Time-series differential processing is performed on the monitoring data to extract change features and generate time-series differential monitoring data, laying the foundation for dynamic trend analysis. By comprehensively sensing the time-series differential monitoring data, composite state perception data is generated, which can comprehensively reflect the operational status and potential problems of water conservancy projects. Dynamic monitoring maps of water conservancy projects are generated using the perception data, providing an important tool for the visualization and intuitive analysis of project status. Fault point prediction based on the composite state perception data from the dynamic monitoring map can quickly identify potential problem locations, gaining time for fault handling. The generation of fault point data supports the early deployment of early warning measures, avoiding major accidents and improving the safety of project operation. By utilizing fault point data to perform multi-scenario simulations on digital twin models, scenario simulation data is generated, supporting the design and optimization of multi-dimensional engineering solutions. The scenario simulation data provides the operational status of the project under different conditions, offering a basis for scientific decision-making and assisting in optimizing resource allocation and adjusting solutions. Through dynamic monitoring and predictive analysis, a water conservancy engineering management system with real-time, intelligent, and dynamic optimization capabilities has been constructed.

[0028] Preferably, the real-time status perception of water conservancy projects based on time-series differential monitoring data includes:

[0029] Biodiversity analysis was performed on time-series differential monitoring data of water conservancy projects to generate regional biological monitoring data for water conservancy projects. The biodiversity analysis included the distribution analysis of fish, algae, and microbial communities. Nutrient cycling was calculated based on the regional biological monitoring data of water conservancy projects to obtain regional nutrient cycling data for water conservancy projects.

[0030] Based on the nutrient cycle data of the water conservancy project area, the dynamic digital twin water conservancy project model is used to perceive the status of engineering facilities and generate water conservancy project facility perception data; through the water conservancy project facility perception data, the nutrient cycle data of the water conservancy project area is used to perceive the water body connectivity, thereby generating water conservancy project environmental perception data.

[0031] By integrating the perception data of water conservancy engineering facilities and the perception data of water conservancy engineering environment, composite state perception data of water conservancy engineering is obtained.

[0032] This invention generates biomonitoring data for water conservancy projects by analyzing the distribution of fish, algae, and microbial communities, which helps to understand the overall health status of the regional ecosystem. Biodiversity analysis based on time-series differential data can track the dynamic changes of biological communities in real time and promptly detect abnormal signals, such as species reduction or ecological imbalance. Nutrient cycle calculations are performed on the biomonitoring data to generate regional nutrient cycle data, comprehensively revealing the flow paths and efficiency of nutrient elements within the region, providing fundamental data for optimizing the ecological functions of water conservancy projects. Nutrient cycle data provides a scientific basis for ecological restoration, environmental regulation, and rational resource utilization in water conservancy projects. Using nutrient cycle data to sense engineering facilities in a dynamic digital twin model generates facility perception data, which can quickly identify the operational status and potential hazards of facilities, such as blockages and damage. Water connectivity sensing is performed on regional nutrient cycle data using facility perception data, generating environmental perception data to ensure the continuity and functionality of water flow and improve the overall effectiveness of water conservancy projects. Integrating facility perception data and environmental perception data to generate composite state perception data enables comprehensive linkage analysis between engineering facilities and environmental conditions. Composite sensing data encompasses multiple dimensions, including biological data, nutrient cycling, facility status, and water connectivity, enabling a comprehensive assessment of the ecology, environment, and operational status of water conservancy projects. Enhanced real-time monitoring and sensing capabilities provide technical support for the dynamic regulation of the ecological environment of water conservancy projects, allowing for timely responses to ecological changes and achieving refined management. Composite status sensing data provides a scientific basis for water conservancy project planning, operational optimization, and the formulation of ecological restoration measures, promoting the organic integration of ecological protection and project management.

[0033] Preferably, step S24 includes the following steps:

[0034] Step S241: Based on the dynamic monitoring map of the water conservancy project, perform structural association of controllable equipment of the water conservancy project on the composite state perception data of the water conservancy project to generate controllable equipment association data of the water conservancy project; collect historical fault data of the controllable equipment of the water conservancy project on the controllable equipment association data of the water conservancy project to obtain historical fault records of the controllable equipment of the water conservancy project.

[0035] Step S242: Divide the historical fault records of controllable equipment in water conservancy projects into a model training set and a model test set; train the model on the model training set using the support vector machine algorithm to generate a pre-model for predicting fault points in water conservancy projects; use the model test set to perform model optimization and iteration on the pre-model for predicting fault points in water conservancy projects, thereby generating a model for predicting fault points in water conservancy projects.

[0036] Step S243: Import the associated data of controllable equipment in the water conservancy project into the water conservancy project fault point prediction model to predict fault points and generate water conservancy project fault point data;

[0037] Step S244: Based on the fault point data of the water conservancy project, set up simulation scenarios for the dynamic digital twin water conservancy project model to generate water conservancy project fault simulation setting scenarios; through the water conservancy project fault simulation setting scenarios, perform emergency operation and maintenance response simulation on the associated data of controllable equipment of the water conservancy project to generate water conservancy project scenario simulation data.

[0038] This invention, based on dynamic monitoring maps, associates composite state-sensing data with equipment structure, effectively integrating equipment operation and environmental information to provide data support for accurately identifying potential equipment risks. By collecting and analyzing historical fault records, a comprehensive equipment fault database is constructed, facilitating a deeper understanding of equipment fault characteristics and key risk factors. Support vector machine algorithms are used to train and optimize historical fault data, generating a high-precision fault point prediction model, effectively improving equipment fault prediction capabilities. Iterative optimization of the prediction model using a test set ensures its reliability and adaptability, enhancing its practicality in different scenarios. Importing controllable equipment-related data into the fault prediction model allows for accurate identification of potential fault points, shortening fault diagnosis time and reducing the risk of misjudgment. The generation of fault point data provides detailed foundational data for further risk assessment and emergency strategy development. Simulation scenarios built based on fault point data can simulate various fault scenarios, improving the ability to respond quickly to emergencies. Simulation scenarios are used to simulate equipment operation and maintenance responses, verifying and optimizing the effectiveness of emergency strategies, ensuring rapid and accurate decision-making in actual operation and maintenance. Improved fault prediction capabilities enable early warning and handling of problems before they occur, reducing downtime losses and maintenance costs caused by equipment failures. Scenario simulation reduces the resource requirements of on-site testing and mitigates secondary problems caused by testing errors. Fault prediction and scenario simulation combine digital twin models with actual engineering operations to achieve refined management of complex engineering systems. Based on dynamic updates and simulation verification of fault point data, digital twin models can continuously optimize and adapt to changing engineering requirements.

[0039] Preferably, the emergency operation and maintenance response simulation of controllable equipment in water conservancy projects through setting up scenarios for water conservancy project fault simulation includes the following steps:

[0040] The simulation scenarios for water conservancy project faults are divided into normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios. The simulation sequence of the normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios is then set to obtain the scenario simulation sequence.

[0041] Extract gate equipment association data and pump station equipment association data from the controllable equipment association data of water conservancy projects; perform data association analysis on the gate equipment association data, pump station equipment association data and water conservancy project fault point data respectively to generate gate opening degree impact data and pump station reservoir capacity regulation data;

[0042] The simulation anomaly feedback data is obtained by performing a scenario simulation sequence on the gate opening influence data and the pump station capacity adjustment data. This includes gate anomaly feedback data and pump station anomaly feedback data. The anomaly type of the simulation anomaly feedback data is then determined. When the simulation anomaly feedback data is gate anomaly feedback data, the corresponding gate is restricted by the gate opening and closing loop, and control loop restriction data is generated.

[0043] When the simulation anomaly feedback data is pump station anomaly feedback data, the valves of the corresponding pump station are switched to high head, and pump station valve limit data is generated; based on the control loop limit data and pump station valve limit data, emergency operation and maintenance response simulation is performed on the controllable equipment correlation data of the water conservancy project to generate water conservancy project scenario simulation data.

[0044] This invention refines the simulation of the dynamic changes in engineering equipment by dividing the simulation scenario into three stages: normal operation, fault triggering, and post-fault expansion, making the simulation more realistic. After setting the scenario simulation sequence, the simulation process can unfold sequentially according to a timeline or logical order, providing more flexible response strategies for emergency operation and maintenance in complex situations. Combining the correlation data of gate equipment and pumping station equipment with fault point data clarifies the linkage relationship between equipment, providing decision support for accurately adjusting gate opening and pumping station capacity. Simulation anomaly feedback analysis of gate opening impact data and pumping station capacity adjustment data allows for rapid anomaly detection and targeted control measures to reduce the risk of equipment damage. Limiting the opening and closing loops of gate anomaly feedback data effectively prevents water conservancy project safety accidents caused by gate misoperation. Switching valves to high-lift mode based on pumping station anomaly feedback data allows for rapid adjustment of the pumping station's operating status to cope with emergencies. The linkage simulation based on control loop limitation data and pumping station valve limitation data improves the accuracy and real-time performance of emergency operation and maintenance response. Emergency operation and maintenance response simulation can quickly locate problems, shorten repair time, and improve the operational efficiency of water conservancy projects.

[0045] Preferably, step S3 includes the following steps:

[0046] Step S31: Construct an operation and maintenance decision-making scheme based on water conservancy project scenario simulation data and water conservancy project composite state perception data to obtain the water conservancy project operation and maintenance decision-making scheme; calculate the comprehensive score of the water conservancy project operation and maintenance decision-making scheme to generate the water conservancy project operation and maintenance decision-making scheme score;

[0047] Step S32: Based on the score of the water conservancy project operation and maintenance decision scheme, the optimal operation and maintenance scheme is selected from the water conservancy project operation and maintenance decision schemes, and the selected optimal operation and maintenance scheme is sent to the water conservancy project intelligent control system for remote control execution in order to obtain remote control feedback data of the water conservancy project.

[0048] Step S33: Convert the remote feedback data of the water conservancy project into operation and maintenance data charts to generate an intelligent operation and maintenance report for the water conservancy project.

[0049] This invention constructs an operation and maintenance (O&M) decision-making scheme by combining simulation data from water conservancy engineering scenarios with composite state perception data. This makes the decision-making process more scientific and data-driven, effectively reducing the subjective errors of traditional experience-based decision-making. By calculating a comprehensive score for the O&M decision-making scheme, multiple alternative schemes can be quantitatively evaluated to select the optimal scheme, providing support for refined management in complex scenarios. Selecting the optimal O&M scheme through comprehensive scoring significantly shortens decision-making time, avoids the implementation of inefficient or ineffective schemes, and improves O&M efficiency. The optimal scheme is directly transmitted to the intelligent control system of the water conservancy project for remote control execution, simplifying traditional manual operation steps and realizing automated control processes. After remote execution, the feedback data of the O&M decision-making scheme is analyzed and processed to form a closed-loop management system, providing a basis for subsequent optimization. By converting the feedback data into O&M data charts and generating intelligent O&M reports, the O&M process and results are intuitively displayed, improving the decision support capabilities of management personnel. The selection and implementation of the optimal O&M scheme can effectively reduce risks, reduce resource waste, and ensure the safe operation of water conservancy engineering equipment. The remote feedback mechanism of the intelligent control system can quickly respond to emergencies, adjust operation and maintenance strategies in a timely manner, and improve the reliability of the project. By combining scenario simulation, intelligent control, and data-driven decision-making, it achieves comprehensive intelligence and automation of the operation and maintenance process, and promotes the construction of a modern management model for water conservancy projects.

[0050] Preferably, step S4 includes the following steps:

[0051] Step S41: Store the intelligent operation and maintenance report of the water conservancy project to the cloud platform and set up an automatic backup mechanism; back up the data of 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.

[0052] Step S42: Share water conservancy project intelligent operation and maintenance reports and backup data across multiple platforms via API interface to perform collaborative operation and maintenance of water conservancy projects.

[0053] This invention stores intelligent operation and maintenance reports for water conservancy projects on a cloud platform, ensuring centralized data management and efficient retrieval. An automatic backup mechanism also guarantees data security and reduces the risk of data loss. The automatic backup mechanism ensures real-time backup of operation and maintenance reports, effectively preventing data loss due to system failures or operational errors, and ensuring information integrity and continuity. The sharing of intelligent operation and maintenance reports and backup data across multiple platforms via API interfaces enables different departments or operating platforms to access operation and maintenance data in real time, promoting information sharing and resource collaboration. The implementation of multi-platform data sharing strengthens collaborative operations across various stages of water conservancy projects, promotes efficient cross-departmental cooperation, and improves the overall coordination of water conservancy project operation and maintenance. Real-time sharing of intelligent operation and maintenance reports and backup data provides a unified decision-making basis for all parties, reducing information transmission time lags and optimizing response speed. Through multi-platform sharing, relevant decision-makers can access operation and maintenance reports in real time, enabling rapid responses, timely adjustments to work plans and operation and maintenance strategies, and enhancing the emergency response capabilities 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 rapid data recovery in the event of failure or loss through regular automatic backups. Multi-platform data sharing via API interfaces supports data interoperability between different systems, overcoming the data silo problem caused by platform incompatibility, and enabling various management systems and monitoring platforms to work efficiently together.

[0054] This specification provides a digital twin water conservancy project operation and maintenance monitoring system for executing the aforementioned digital twin water conservancy project operation and maintenance monitoring method. The digital twin water conservancy project operation and maintenance monitoring system includes:

[0055] The digital twin module is used to acquire relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects to generate a basic digital twin model; and perform global optimization on the basic digital twin model to generate a dynamic digital twin water conservancy project model.

[0056] The engineering analysis module is used for remote monitoring of water conservancy projects based on a dynamic digital twin water conservancy engineering model, generating water conservancy engineering monitoring data; performing composite state perception on the water conservancy engineering monitoring data, generating a dynamic monitoring map of the water conservancy engineering; predicting water conservancy engineering fault points based on the composite state perception data of the water conservancy engineering according to the dynamic monitoring map, generating water conservancy engineering fault point data; and performing multi-scenario simulation on the dynamic digital twin water conservancy engineering model based on the water conservancy engineering fault point data, generating water conservancy engineering scenario simulation data.

[0057] The operation and maintenance decision module is used to construct operation and maintenance decision schemes based on water conservancy project scenario simulation data and water conservancy project composite state perception data, and obtain water conservancy project operation and maintenance decision schemes; the water conservancy project operation and maintenance decision schemes are sent to the water conservancy project intelligent control system for remote control execution, and water conservancy project intelligent operation and maintenance reports are generated;

[0058] The data sharing module is used to back up the intelligent operation and maintenance report of water conservancy projects and generate intelligent operation and maintenance backup data of water conservancy projects; it also enables multi-platform data sharing of intelligent operation and maintenance reports and backup data of water conservancy projects through API interfaces to perform collaborative operation and maintenance of water conservancy projects.

[0059] The beneficial effects of this invention lie in the fact that the digital twin module constructs a dynamic digital twin water conservancy engineering model through three-dimensional geometric feature extraction and hydrological structure data association, realizing comprehensive digital and virtual management of water conservancy projects. It supports global optimization of complex water conservancy projects, providing high-precision data models for subsequent monitoring and simulation. The engineering analysis module collects real-time water conservancy project operation data through remote monitoring, generating dynamic monitoring maps to comprehensively perceive the project's operational status. Utilizing fault point prediction technology, it can identify potential risk points before faults occur, improving early warning capabilities and reducing the probability of sudden accidents. The operation and maintenance decision module combines scenario simulation data and status perception data to construct efficient operation and maintenance decision-making schemes, ensuring the scientific and targeted nature of decisions. The remote control function reduces reliance on on-site manual intervention, significantly improving operation and maintenance efficiency and reducing operating costs. The data sharing module, through data backup and multi-platform data sharing, realizes efficient distribution and collaborative operation of intelligent operation and maintenance reports: various departments and platforms work synchronously based on shared data, improving 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 secure storage and disaster recovery capabilities of operation and maintenance data, enabling rapid recovery even in the event of emergencies. Through full lifecycle monitoring, analysis, decision-making, and sharing, a closed-loop water conservancy project management process is established, guaranteeing the long-term stable operation of the system. The multi-scenario simulation function can simulate different operating conditions, providing decision support for water conservancy project design optimization, risk assessment, and emergency response. This enhances the adaptability and resilience of water conservancy projects, effectively coping with complex and ever-changing operating environments. Therefore, this invention, through the construction of a dynamic model using digital twin technology, achieves precise, intelligent, and collaborative operation and maintenance monitoring of water conservancy projects, improving the accuracy and intelligence level of water conservancy project operation and maintenance monitoring. Attached Figure Description

[0060] Figure 1 A schematic diagram illustrating the steps of a digital twin water conservancy project operation and maintenance monitoring method;

[0061] Figure 2 for Figure 1A detailed flowchart illustrating the implementation steps of step S2.

[0062] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0063] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0065] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0066] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] To achieve the above objectives, please refer to Figures 1 to 3 A digital twin water conservancy project operation and maintenance monitoring method, the method comprising the following steps:

[0068] Step S1: Obtain relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects 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;

[0069] Step S2: Conduct 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 composite state perception on the water conservancy project monitoring data to generate a dynamic monitoring map of the water conservancy project; predict water conservancy project fault points based on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project 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.

[0070] Step S3: Based on the simulation data of water conservancy engineering scenarios and the composite state perception data of water conservancy engineering, construct the operation and maintenance decision scheme to obtain the operation and maintenance decision scheme of water conservancy engineering; send the operation and maintenance decision scheme of water conservancy engineering to the intelligent control system of water conservancy engineering for remote control execution, and generate the intelligent operation and maintenance report of water conservancy engineering.

[0071] Step S4: Back up the intelligent operation and maintenance report of the water conservancy project to generate intelligent operation and maintenance backup data of the water conservancy project; share the intelligent operation and maintenance report and the intelligent operation and maintenance backup data of the water conservancy project across multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

[0072] This invention acquires relevant data from water conservancy projects and extracts three-dimensional geometric features, then combines this data with hydrological structure data to generate a digital twin basic model, achieving high-precision virtual mapping of water conservancy projects. Through global optimization, a dynamic digital twin water conservancy project model is generated, enhancing the model's dynamic adaptability and accuracy, laying the foundation for subsequent monitoring and operation and maintenance. Remote monitoring based on the dynamic digital twin model overcomes the limitations of traditional monitoring methods, achieving real-time and intelligent dynamic perception. The generation of dynamic monitoring maps and fault point data effectively improves the accuracy of fault prediction. Multi-scenario simulations verify the potential risks and feasibility of solutions under different operating scenarios, providing a scientific basis for operation and maintenance decisions. Combining scenario simulation data and state perception data to construct operation and maintenance decision-making schemes enhances the scientific rigor and relevance of operation and maintenance decisions. Remote execution by the intelligent control system enables automated and precise operation and maintenance, reducing human intervention. The generated intelligent operation and maintenance reports provide data support for continuous optimization of operation and maintenance strategies. Data backup of intelligent operation and maintenance reports ensures the security and integrity of operation and maintenance information; multi-platform data sharing through API interfaces breaks down data silos, promotes multi-party collaborative operation and maintenance, and improves operation and maintenance efficiency and resource utilization. Therefore, this invention constructs a dynamic model using digital twin technology, realizing the precision, intelligence, and collaboration of water conservancy project operation and maintenance monitoring, and improving the accuracy and intelligence level of water conservancy project operation and maintenance monitoring.

[0073] In this embodiment of the invention, reference Figure 1The diagram shown is a flowchart illustrating the steps of a digital twin water conservancy project operation and maintenance monitoring method according to the present invention. In this example, the digital twin water conservancy project operation and maintenance monitoring method includes the following steps:

[0074] Step S1: Obtain relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects 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;

[0075] In this embodiment of the invention, relevant data of water conservancy projects are acquired, including but not limited to topographic data, structural design drawings, construction records, watershed hydrological data, and meteorological data. Data sources can include remote sensing monitoring, drone aerial photography, on-site sensors, historical databases, and other methods. The collected raw data is cleaned, removing redundant and invalid information. Noise filtering, data completion (such as missing value imputation), and format standardization are performed to generate a high-quality preprocessed dataset. Based on the preprocessed data, a three-dimensional model of the water conservancy project is constructed using point cloud reconstruction algorithms or mesh generation algorithms. Edge detection and surface fitting techniques are used to extract key geometric features, such as dam shape, gate structure, and channel slope. The extracted geometric features are digitally encoded to generate three-dimensional geometric data describing the geometric attributes of the water conservancy project. The three-dimensional geometric data is matched and correlated with hydrological data (such as flow velocity, flow rate, water level, and rainfall). A multi-source data fusion algorithm (such as weighted fusion based on weights or deep learning methods) is used to construct a multi-dimensional correlation matrix. Based on the geometric topology of water conservancy projects and the dynamic changes in hydrological data, a hydrological flow model is established to generate a preliminary digital twin model, which represents the static and dynamic characteristics of the water conservancy project and its surrounding environment. Global optimization algorithms (such as genetic algorithms and particle swarm optimization) are applied to the digital twin model to improve its accuracy and computational efficiency. Optimization objectives include structural rationality, real-time hydrological data, and spatial consistency. A time dimension is introduced; the state of the twin model is dynamically updated using real-time hydrological monitoring data and prediction models, generating a dynamic digital twin model of the water conservancy project that can reflect the operational status of the water conservancy project and environmental changes in real time.

[0076] Step S2: Conduct 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 composite state perception on the water conservancy project monitoring data to generate a dynamic monitoring map of the water conservancy project; predict water conservancy project fault points based on the composite state perception data of the water conservancy project according to the dynamic monitoring map of the water conservancy project 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.

[0077] In this embodiment of the invention, a remote monitoring system is constructed based on a dynamic digital twin water conservancy engineering model. This system integrates sensor networks (such as water level sensors, flow meters, and pressure sensors) and IoT gateways to achieve real-time data transmission. Utilizing a cloud platform and distributed computing architecture, it supports remote monitoring of large-scale water conservancy projects. Real-time collection of dynamic operational data from the water conservancy project, including water level, flow rate, dam stress, and meteorological changes, is performed. After preprocessing by edge computing devices, the data is uploaded to the cloud monitoring platform to generate water conservancy project monitoring data. The monitoring data is dynamically matched and fused with the dynamic digital twin model. Multi-dimensional feature extraction algorithms (such as deep learning and principal component analysis) are used to perceive the composite state of the water conservancy project (a combination of static and dynamic operational states). The perceived data is visualized to generate a dynamic monitoring map of the water conservancy project, including a spatiotemporal evolution map, a stress distribution map, and a watershed dynamic change map. The map is updated in real time, reflecting the operational status of the water conservancy project and potential risk areas. Using historical monitoring data and the dynamic monitoring map, a fault point prediction model is established based on machine learning algorithms (such as random forests, support vector machines, and LSTM neural networks). Key indicators for fault point prediction include dam cracks, seepage locations, and equipment anomalies. Predictive analysis of real-time monitoring data is performed to locate potential fault points and generate fault point data for hydraulic engineering projects. Each fault point includes location coordinates, risk level, potential causes, and repair recommendations. Based on the fault point data, various simulation scenarios are designed, including natural disasters (such as floods and earthquakes), equipment failures (such as gate malfunctions), and operational limit tests. Each scenario includes external input conditions (such as rainfall and earthquake intensity) and simulation objectives (such as assessing water level changes and pressure distribution). The simulation scenarios are run in a dynamic digital twin hydraulic engineering model, using finite element analysis (FEA), fluid dynamics (CFD) simulation, and dynamic system simulation tools (such as ANSYS and Simulink) to simulate actual operation, generating simulation data for the hydraulic engineering scenario, including multi-dimensional results such as stress distribution, fluid flow paths, and the scope of disaster impact.

[0078] Step S3: Based on the simulation data of water conservancy engineering scenarios and the composite state perception data of water conservancy engineering, construct the operation and maintenance decision scheme to obtain the operation and maintenance decision scheme of water conservancy engineering; send the operation and maintenance decision scheme of water conservancy engineering to the intelligent control system of water conservancy engineering for remote control execution, and generate the intelligent operation and maintenance report of water conservancy engineering.

[0079] In this embodiment of the invention, a multidimensional dataset is formed by integrating simulation data of water conservancy engineering scenarios and composite state perception data of water conservancy engineering. The data includes simulation results (such as stress distribution, fluid path, and fault risk assessment) and real-time monitoring status (such as water level, flow velocity, and temperature changes). This ensures the operational safety of water conservancy engineering, reduces maintenance costs, and improves system efficiency. A decision model based on multi-objective optimization algorithms (such as fuzzy logic, genetic algorithms, or reinforcement learning) is constructed. The model output includes optimal control parameters (such as gate opening and pump station operation mode) and maintenance strategies (such as maintenance priority). Based on the optimization results, a water conservancy engineering operation and maintenance decision plan is generated, including: daily operating parameter suggestions, emergency response plans, medium- and long-term maintenance plans, and simulation evaluation of the plan to verify its applicability and effectiveness in different scenarios. The water conservancy engineering operation and maintenance decision plan is converted into control commands and sent to the water conservancy engineering intelligent control system via communication protocols (such as Modbus and BACnet). The intelligent control system executes the decision plan, including: dynamically adjusting operating parameters (such as adjusting pump power and changing the angle of the floodgate); and automatically starting maintenance equipment (such as cleaning machines and monitoring instruments). Trigger emergency plans (such as activating backup power or issuing alarms). The intelligent control system provides real-time feedback on execution results, collecting key operational indicators and environmental status. Execution data is analyzed and evaluated, including: the effectiveness of decision-making plans, trends in potential risks, and the stability of equipment operation. An intelligent operation and maintenance report for the water conservancy project is generated, including: execution details of the operation and maintenance plan (such as command execution time and equipment response status), analysis of changes in the water conservancy project's operational status (such as water level change curves and fault mitigation status), and suggested follow-up optimization plans (such as equipment upgrades or adjustments to operational strategies).

[0080] Step S4: Back up the intelligent operation and maintenance report of the water conservancy project to generate intelligent operation and maintenance backup data of the water conservancy project; share the intelligent operation and maintenance report and the intelligent operation and maintenance backup data of the water conservancy project across multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

[0081] In this embodiment of the invention, an intelligent operation and maintenance report for water conservancy projects is input, including execution records, effect evaluations, operational status analysis, and optimization suggestions. Data is categorized and stored according to data type (e.g., text data, image data, time-series data): a distributed storage system (e.g., Hadoop, Ceph) is used to ensure the storage capacity for large-scale data. High-frequency access data is stored in a high-speed cache, while low-frequency access data is stored in a cold data storage area. A multi-level data backup strategy is implemented: RAID technology is used to create replicas on local storage devices. Data is uploaded to cloud storage services (e.g., AWS S3, Azure BlobStorage) to ensure off-site disaster recovery capabilities. Algorithms (e.g., AES-256) are used to encrypt the backup data, generating intelligent operation and maintenance backup data for water conservancy projects to ensure data security. The integrity and availability of the backup data are verified to ensure efficient recovery when needed. Standardized API interfaces (e.g., RESTful API or gRPC) are designed for data sharing. Interface functions include data query, download, update, and access control. An API gateway is deployed to support efficient access from multiple platforms (e.g., web platforms, mobile applications, SCADA systems). Improve interface response speed by combining load balancing and CDN technologies. Share intelligent operation and maintenance reports and backup data of water conservancy projects to various operation and maintenance platforms via API interfaces. Support real-time synchronization and historical data access to facilitate data collaboration between multiple platforms. Each platform executes collaborative operation and maintenance tasks based on shared data; for example, equipment maintenance teams arrange maintenance plans based on shared data. The operation monitoring platform monitors key parameters in real time and feeds back to the operation and maintenance management system. The emergency response platform formulates disaster plans based on shared data. Implement a fine-grained access control mechanism (such as RBAC or ABAC-based access management) to ensure that different platforms and users can only access data matching their permission levels. Record data access logs and generate audit reports to facilitate compliance checks during the data sharing process.

[0082] Preferably, step S1 includes the following steps:

[0083] Step S11: Obtain relevant data for water conservancy projects, including topographic data, meteorological data, and hydrological data;

[0084] Step S12: Perform data preprocessing on 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 engineering dataset. The data preprocessing includes data cleaning, data denoising, missing value imputation, and data standardization.

[0085] Step S13: Extract three-dimensional geometric features based on the standard multi-source water conservancy engineering dataset to obtain three-dimensional geometric data of water conservancy engineering; perform hydrological structure data association based on the three-dimensional geometric data of water conservancy engineering to generate a digital twin basic model;

[0086] Step S14: Extract the spatiotemporal features of the standard multi-source water conservancy project dataset to obtain the spatiotemporal feature data of water conservancy projects; use the spatiotemporal feature data of water conservancy projects to perform global optimization of the digital twin basic model to generate a dynamic digital twin water conservancy project model.

[0087] In this embodiment of the invention, high-precision topographic information is obtained by scanning the target area using technologies such as UAVs, satellite imagery, and LiDAR. Historical and real-time meteorological parameters, including rainfall, temperature, humidity, and wind speed, are acquired from meteorological stations or global meteorological data platforms. Data on water level, flow rate, water quality, and evaporation are collected using sensors and monitoring stations, and historical hydrological records are supplemented. Redundant or erroneous values ​​in the topographic, meteorological, and hydrological data, such as abnormal precipitation records or invalid measurement points, are removed. 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 missing points in the topographic and hydrological data to ensure data continuity. The data format, units, and coordinate system are unified; for example, topographic data is unified to a certain elevation datum, and meteorological and hydrological data are converted to the standard International System of Units (SI) to generate a standard multi-source water conservancy engineering dataset with consistency and high quality. Geometric features of key areas such as river channels, dams, and reservoirs are extracted from the topographic data, and a three-dimensional surface model is generated using TIN meshes. Point cloud segmentation algorithms are used to identify specific structures (such as dams and pumping stations) in the terrain. Based on hydrological data, spatiotemporal distribution data such as flow rate and velocity are mapped onto a 3D geometric model to establish the correlation between hydrological structure and terrain. Time-series features such as water flow changes and rainfall distribution are extracted from standard multi-source water conservancy engineering datasets; through spatiotemporal statistical analysis, the coupling characteristics of meteorology, hydrology, and terrain are extracted. Combining spatiotemporal feature data, global optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) are used to adjust model parameters to improve its dynamic response capability. Dynamic simulation technology is introduced to support real-time updates and prediction functions, generating a dynamic digital twin water conservancy engineering model with real-time dynamic analysis capabilities, which can be applied to scenarios such as flood prediction and optimized scheduling.

[0088] Preferably, the correlation of hydrological structure data based on the three-dimensional geometric data of water conservancy projects includes:

[0089] Topographic features are extracted from the three-dimensional geometric data of water conservancy projects to obtain topographic feature data of water conservancy projects; hydrological element data is matched with standard multi-source water conservancy project datasets using the topographic feature data of water conservancy projects to obtain hydrological element matching data;

[0090] Based on the hydrological element matching data, watershed zoning boundary analysis is performed on the topographic feature data of water conservancy projects to obtain watershed zoning boundary data; the coordinates of structural key points are marked on the three-dimensional geometric data of water conservancy projects using the watershed zoning boundary data to obtain structural key point coordinate data.

[0091] Hydrological structural patches are constructed from the watershed boundary data based on the coordinate data of key structural points to obtain hydrological structural patch data; hydrological structural attributes are associated with the hydrological structural patch data to obtain hydrological structural associated data; dynamic spatiotemporal feature mapping is performed on the hydrological structural associated data to obtain dynamic hydrological structural feature data.

[0092] A digital twin basic model is obtained by integrating hydrological and geometric data to perform spatial modeling of dynamic hydrological structural characteristic data.

[0093] In this embodiment of the invention, topographic features, including river channels, ridgelines, and depressions, are extracted using topographic analysis algorithms (such as slope, aspect, and curvature calculations) based on three-dimensional geometric data of water conservancy projects. The topographic analysis results are used to generate topographic feature data of the water conservancy projects, including feature points (such as lowest and highest points), feature lines (such as contour lines), and feature surfaces (such as watershed surfaces). GIS tools (such as ArcGIS and QGIS) or programming tools (such as Python's GDAL library) are used to calculate the topographic features. Slope, aspect, and curvature information are extracted and output as vector data formats (such as Shapefile and GeoJSON). The topographic feature data is matched with standard multi-source water conservancy project datasets (including river networks, rainfall observation stations, and reservoirs) to associate hydrological elements (such as watershed extent and confluence paths). Spatial overlay analysis is used to align the three-dimensional geometric data with the hydrological dataset, generating hydrological element matching data. Spatial matching is performed using spatial analysis tools (such as ArcGIS's Spatial Analyst module) or Python's Geopandas library. Euclidean distance or shape similarity algorithms are used to evaluate the matching degree between feature points, lines, and surfaces and hydrological elements. Hydrological analysis models (such as watershed extraction algorithms) are used to determine watershed boundaries based on hydrological element matching data. The catchment area of ​​each watershed is extracted to generate watershed boundary data, which is then combined with the topographic features of the hydraulic engineering projects. Watershed boundaries are extracted using ArcGIS Hydrology tools or the TauDEM library in Python, generating vector data containing watershed boundaries, areas, and catchment paths. Based on the watershed boundary data, the coordinates of key structural points (such as dam center point, spillway location, and monitoring stations) are marked in the 3D geometric data of the hydraulic engineering projects. The 3D coordinates of the key points and their hydrological significance in the watershed are determined. Key point coordinates are extracted using point cloud processing tools (such as CloudCompare). The extracted key point data is output as a CSV or 3D file format (such as PLY or OBJ). Using the structural key point coordinate data and watershed boundaries, 3D hydrological structural patches are constructed, including dams, diversion channels, and spillways. By combining surface patches with the terrain, a refined 3D hydrological structure model is generated. Triangulation algorithms (such as Delaunay triangulation) are used to generate hydrological structure patches. Patches are then optimized and visualized using 3D modeling tools (such as Blender and Rhino). The hydrological structure patch data is correlated with hydrological attributes (such as flow rate and water storage capacity) to generate a structure attribute table describing the hydrological characteristics of each patch. Attributes include flow distribution, rainfall response time, and drainage capacity. A database (such as PostGIS) is used to store and manage the associated data of hydrological structure attributes. SQL queries are written to dynamically update and extract hydrological structure attributes. Dynamic hydrological data (such as real-time rainfall and reservoir water levels) are used to perform spatiotemporal mapping of the hydrological structure, generating dynamic hydrological feature data.By combining the spatiotemporal trends of the watershed, hydrological events (such as flood peaks) are predicted. Spatiotemporal analysis models (such as Hec-RAS and MIKE FLOOD) are used to simulate hydrological dynamics. Visualization tools (such as Cesium and ParaView) are used to display dynamic changes. Dynamic hydrological structural feature data is integrated with 3D geometric data to construct a digital twin basic model for hydrological prediction, structural monitoring, and optimization. The model integrates topography, structure, and hydrology to achieve real-time simulation and intelligent analysis. Digital twin platforms (such as Unity and Unreal Engine) are used for model construction. Physics engines and simulation tools (such as Ansys and COMSOL) are used to enhance dynamic simulation capabilities.

[0094] As an example of the present invention, reference is made to Figure 2 As shown, step S2 in this example includes:

[0095] Step S21: Deploy regional sensors based on the dynamic digital twin water conservancy engineering model to obtain key area sensor deployment data; construct the Internet of Things (IoT) architecture based on the key area sensor deployment data to generate the water conservancy engineering IoT architecture;

[0096] Step S22: Embed the IoT architecture of the water conservancy project into the dynamic digital twin water conservancy project model for remote monitoring of the water conservancy project, and generate water conservancy project monitoring data; perform time series difference on the water conservancy project monitoring data to generate water conservancy project time series difference monitoring data;

[0097] Step S23: Perform composite state perception on the time-series differential monitoring data of water conservancy projects to generate composite state perception data of water conservancy projects; perform perception mapping on the composite state perception data of water conservancy projects to obtain dynamic monitoring maps of water conservancy projects;

[0098] Step S24: Based on the dynamic monitoring map of the water conservancy project, predict the fault points of the water conservancy project from the composite state perception data of the water conservancy project, and generate water conservancy project fault point data; based on the water conservancy project fault point data, perform multi-scenario simulation on the dynamic digital twin water conservancy project model, and generate water conservancy project scenario simulation data.

[0099] In this embodiment of the invention, key points of a water conservancy project area are analyzed based on a dynamic digital twin water conservancy engineering model. Taking into account topography, water flow pressure, structural stress, and environmental factors, key monitoring areas are determined, and sensor deployment planning data for these key areas is generated. Based on this key area sensor deployment planning data, appropriate sensor types (such as pressure sensors, flow sensors, and stress sensors) are selected, and the physical installation and networking of the sensors are completed, generating key area sensor deployment data. Based on the sensor deployment data, a multi-layered Internet of Things (IoT) architecture is designed, including a sensing layer, an edge computing layer, and a cloud computing layer, completing the construction of a data acquisition, transmission, and processing network, generating a water conservancy engineering IoT architecture. The water conservancy engineering IoT architecture is integrated with the dynamic digital twin water conservancy engineering model. The twin model is updated through real-time data streams to achieve remote monitoring of the water conservancy project, generating water conservancy engineering monitoring data. Time series analysis is performed on the water conservancy engineering monitoring data, and differential calculation methods are used to extract the changing trends of key variables (such as pressure, flow velocity, and structural deformation), generating time-series differential monitoring data for the water conservancy project. Data fusion and feature extraction are performed on time-series differential monitoring data of water conservancy projects. Combining structural mechanics, hydrodynamics, and environmental conditions, the composite operational states of water conservancy projects are identified, generating composite state perception data. A graph data model is used to represent the composite state perception data graphically, constructing node representations (e.g., monitoring points) and edge relationships (e.g., water flow influence paths), generating a dynamic monitoring map of the water conservancy project. This map intuitively reflects the overall operational state of the project and its correlation with key areas. Fault point data is applied to a dynamic digital twin water conservancy project model to simulate fault scenarios (e.g., water flow blockage, structural failure, or pressure overload). Multi-scenario simulations are conducted under different parameters to generate water conservancy project scenario simulation data, providing decision support for optimized design and emergency plans.

[0100] Preferably, the real-time status perception of water conservancy projects based on time-series differential monitoring data includes:

[0101] Biodiversity analysis was performed on time-series differential monitoring data of water conservancy projects to generate regional biological monitoring data for water conservancy projects. The biodiversity analysis included the distribution analysis of fish, algae, and microbial communities. Nutrient cycling was calculated based on the regional biological monitoring data of water conservancy projects to obtain regional nutrient cycling data for water conservancy projects.

[0102] Based on the nutrient cycle data of the water conservancy project area, the dynamic digital twin water conservancy project model is used to perceive the status of engineering facilities and generate water conservancy project facility perception data; through the water conservancy project facility perception data, the nutrient cycle data of the water conservancy project area is used to perceive the water body connectivity, thereby generating water conservancy project environmental perception data.

[0103] By integrating the perception data of water conservancy engineering facilities and the perception data of water conservancy engineering environment, composite state perception data of water conservancy engineering is obtained.

[0104] In this embodiment of the invention, time-series data on fish, algae, and microbial communities in the water body are collected by sensors deployed around the water conservancy project area, including the species, quantity, and distribution of organisms. The collected data is cleaned to remove noise and outliers, and smoothed to ensure data stability and accuracy. Missing values ​​are filled using interpolation methods, and the data is standardized. Underwater image recognition technology, such as convolutional neural networks (CNN), is used to identify fish species in the water body, labeling the species and quantity of fish. Spatial interpolation techniques (such as Kriging interpolation) are used to analyze the spatial distribution of fish populations based on the collected fish data, determining the distribution characteristics of fish populations in the water conservancy project area. Combined with time-series data, time series analysis methods (such as the ARIMA model) are used to analyze the time-varying trends of fish distribution, generating fish diversity data. Algae density and species are monitored using water quality sensors, and algae distribution information over a large area is obtained using remote sensing technology. Genomics technology, such as 16S rRNA sequencing, is used to collect microbial gene data in the water body, analyzing the species and abundance of the microbial community. This project integrates data on fish, algae, and microorganisms, using clustering algorithms (such as K-means) to identify different regions and change patterns within biological communities, thus forming regional biological monitoring data. Time-series data on nutrients (such as nitrogen, phosphorus, and potassium) in water bodies are acquired through water quality sensors, and biodiversity data is integrated with water quality data to construct a comprehensive aquatic environment dataset. Ecological models (such as ecological network models) are used to calculate nutrient cycling in the water conservancy project area, focusing on the flow and transformation of nitrogen and phosphorus in the water. The project analyzes the sources of nitrogen in the water (such as agricultural runoff and urban sewage) and their transformation processes (such as nitrification and denitrification). The release, adsorption, and precipitation processes of phosphorus are simulated, considering the interactions between algae, microorganisms, and phosphorus. Based on the model results, nutrient cycling data for the region is generated and dynamically updated. A digital twin model of the water conservancy project is constructed based on design drawings and real-time monitoring data, including information on water flow and facility status. This system integrates real-time sensor data on water level, flow velocity, temperature, and pressure, connecting it to a digital twin model to form a dynamically updated virtual hydraulic engineering model. Through a real-time monitoring system, it acquires operational data from key facilities such as pumps, gates, and pipelines, assessing their operational status (normal, faulty, under maintenance, etc.). The digital twin model is dynamically updated based on real-time monitoring data, ensuring timely reflection of facility status information. By analyzing data such as water level and flow velocity, it identifies the connectivity between water bodies, identifying flow channels, blockages, and potential flow paths. Graph theory algorithms (such as shortest path algorithms and network analysis) are used to establish connectivity models between water bodies, analyzing the flow relationships between different regions of the water body.Real-time updates of environmental data, combined with facility status and connectivity analysis results, generate dynamic environmental perception data for water conservancy projects, providing comprehensive feedback. The perception data of water conservancy project facilities are integrated with environmental perception data (such as water quality and connectivity) using a weighted average method or fuzzy logic method to form composite state perception data. Based on this composite state perception data, and combined with artificial intelligence algorithms (such as deep learning models), the operational status of water conservancy projects is intelligently assessed, and improvement suggestions and early warning information are proposed. Based on the composite state perception data, real-time water conservancy project status reports are automatically generated, including information on biodiversity, nutrient cycling, and facility operational status, for management personnel's decision-making reference.

[0105] Preferably, step S24 includes the following steps:

[0106] Step S241: Based on the dynamic monitoring map of the water conservancy project, perform structural association of controllable equipment of the water conservancy project on the composite state perception data of the water conservancy project to generate controllable equipment association data of the water conservancy project; collect historical fault data of the controllable equipment of the water conservancy project on the controllable equipment association data of the water conservancy project to obtain historical fault records of the controllable equipment of the water conservancy project.

[0107] Step S242: Divide the historical fault records of controllable equipment in water conservancy projects into a model training set and a model test set; train the model on the model training set using the support vector machine algorithm to generate a pre-model for predicting fault points in water conservancy projects; use the model test set to perform model optimization and iteration on the pre-model for predicting fault points in water conservancy projects, thereby generating a model for predicting fault points in water conservancy projects.

[0108] Step S243: Import the associated data of controllable equipment in the water conservancy project into the water conservancy project fault point prediction model to predict fault points and generate water conservancy project fault point data;

[0109] Step S244: Based on the fault point data of the water conservancy project, set up simulation scenarios for the dynamic digital twin water conservancy project model to generate water conservancy project fault simulation setting scenarios; through the water conservancy project fault simulation setting scenarios, perform emergency operation and maintenance response simulation on the associated data of controllable equipment of the water conservancy project to generate water conservancy project scenario simulation data.

[0110] In this embodiment of the invention, a dynamic monitoring map of the water conservancy project is constructed by combining time-series differential monitoring data. This map monitors data such as water level, flow velocity, and equipment status, generating a map showing the relationship between equipment operation and the environment. Based on the dynamic monitoring map, the shortest path algorithm and network analysis methods from graph theory are used to establish the relationship structure between various controllable devices within the water conservancy project. For example, monitoring data analysis results show close correlations between certain devices (such as the linkage between pumps and gates), thus identifying key equipment relationships. Based on the structural relationships between devices, controllable equipment association data is automatically generated. This data includes relationship diagrams between devices, operating modes, and potential fault points. Using a historical fault database, historical fault records of relevant equipment are extracted from the equipment management system of the water conservancy project. 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 equipment operating status and maintenance cycles to form a complete historical equipment fault dataset. The historical fault records of controllable equipment in the water conservancy project are divided into training and testing sets according to a certain proportion. Typically, the training set accounts for 70%-80% of the dataset, and the testing set accounts for 20%-30%. Feature data helpful for prediction, such as equipment failure time, failure frequency, environmental factors, and equipment operating parameters, is extracted from historical failure records. The SVM algorithm is used to train the training set, employing common kernel functions such as the RBF kernel to capture non-linear features. Classification performance is improved by continuously optimizing parameters (e.g., C-value, γ-value). The SVM model separates failure points from non-failure points, forming a hyperplane that distinguishes failure samples from non-failure samples in historical data and provides the model with the ability to predict future failures. The trained SVM model is validated using a test set to evaluate its accuracy in predicting unknown data. Model parameters are optimized using techniques such as cross-validation and grid search. Based on the prediction results of the test set, the model is continuously adjusted, employing adaptive algorithms or transfer learning to ensure it can adapt to changes in different environments of the water conservancy project. The associated data of controllable equipment in the water conservancy project (including relationships between equipment, state changes, etc.) is imported into the failure point prediction model. At this point, the model predicts impending failures by associating historical failure records with the current operating status of the equipment. Based on the predictive model, and combined with information such as the current operating status of the equipment, historical fault characteristics, and environmental impact, the timing and type of faults (e.g., faults caused by excessive equipment pressure, wear and tear faults caused by prolonged equipment operation) are predicted. The prediction results are used to generate fault point data, including the predicted fault occurrence time, fault type, and affected area, providing a reference for subsequent emergency response and operation and maintenance management. Based on the fault point data of the water conservancy project, a simulation scenario of a dynamic digital twin water conservancy project model is constructed.For example, if a device failure is predicted, its operation during the failure can be simulated to illustrate the impact of the failure. The simulation model sets up failure scenarios such as device shutdown, flow rate changes, and pressure fluctuations to simulate the impact of different failure types on water conservancy projects. Based on the simulation scenarios, emergency operation and maintenance response strategies are set, such as activating backup equipment, flow regulation, and flood discharge operations. The effectiveness of the emergency response plan is evaluated through the simulation system to ensure that effective measures can be taken quickly in the event of a failure. Through emergency operation and maintenance response simulation, complete simulation data of water conservancy project scenarios is generated, including data on equipment recovery time, response effectiveness, and resource consumption, providing support for decision-making.

[0111] Preferably, the emergency operation and maintenance response simulation of controllable equipment in water conservancy projects through setting up scenarios for water conservancy project fault simulation includes the following steps:

[0112] The simulation scenarios for water conservancy project faults are divided into normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios. The simulation sequence of the normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios is then set to obtain the scenario simulation sequence.

[0113] Extract gate equipment association data and pump station equipment association data from the controllable equipment association data of water conservancy projects; perform data association analysis on the gate equipment association data, pump station equipment association data and water conservancy project fault point data respectively to generate gate opening degree impact data and pump station reservoir capacity regulation data;

[0114] The simulation anomaly feedback data is obtained by performing a scenario simulation sequence on the gate opening influence data and the pump station capacity adjustment data. This includes gate anomaly feedback data and pump station anomaly feedback data. The anomaly type of the simulation anomaly feedback data is then determined. When the simulation anomaly feedback data is gate anomaly feedback data, the corresponding gate is restricted by the gate opening and closing loop, and control loop restriction data is generated.

[0115] When the simulation anomaly feedback data is pump station anomaly feedback data, the valves of the corresponding pump station are switched to high head, and pump station valve limit data is generated; based on the control loop limit data and pump station valve limit data, emergency operation and maintenance response simulation is performed on the controllable equipment correlation data of the water conservancy project to generate water conservancy project scenario simulation data.

[0116] In this embodiment of the invention, under the scenario where all facilities of the water conservancy project are operating normally and the status of each piece of equipment is maintained within a predetermined working range (e.g., pumping stations are operating normally, gates are maintained at a set opening, and parameters such as water flow and pressure are within a safe range), different types of fault triggering are simulated, such as sudden equipment failure, abnormal water flow, or water level fluctuations. For example, pumping station failure or gate control system malfunctions are simulated by setting external or internal factors that cause these devices to malfunction. After equipment failure, more detailed scenario settings are implemented to simulate changes in other environmental variables such as water flow and pressure after the failure, analyzing the impact of fault propagation on the entire system. Based on the above scenario division, a scenario simulation sequence is determined. The set sequence generally includes: first, executing the normal operation scenario to ensure the system operates under normal conditions; then, triggering the fault trigger scenario to simulate the conditions under which equipment fails; and finally, in the post-fault propagation scenario, analyzing the impact of the fault to ensure the system's emergency response can be effectively implemented. The system arranges the normal operation, fault trigger, and fault propagation scenarios in chronological and conditional order to generate a complete scenario simulation sequence. This system extracts relevant data from the real-time monitoring system of water conservancy projects, primarily including key operational data such as gate opening degree, closing speed, and adjustment parameters. It also extracts equipment status data for gate control, such as gate motor operating status, battery level, and control signals. Relevant data is extracted from the pumping station system, covering information such as pumping station operating status, pump house power consumption, water output, and the opening and closing status of pumping station valves. Combined with reservoir capacity data, it obtains pumping station load status, real-time operating status, and adjustment data. The system analyzes the gate equipment's relevant data to calculate the impact of gate opening degree on water flow and water level, generating gate opening degree impact data. Furthermore, it analyzes the pumping station equipment's relevant data, combining it with reservoir capacity adjustment information, to calculate the pumping station's operating status's effect on flow regulation, generating pumping station reservoir capacity adjustment data. When the scenario simulation sequence is executed, if an abnormal gate opening degree is detected during the simulation (e.g., too large or too small, causing the water flow to be unable to be properly regulated), the system will generate gate abnormality feedback data, recording the time, type, and cause of the abnormality. If problems such as improper valve adjustment or excessive head occur during pump station equipment simulation, abnormal feedback data is generated, recording the fault type, the equipment involved, and the consequences. Using set thresholds or models, the system determines whether the feedback data falls under the gate anomaly category. If the feedback data indicates that the opening adjustment exceeds the safe range, it is determined to be a gate anomaly. Similarly, the system uses set thresholds or feature recognition methods to judge the pump station feedback data. If the feedback data indicates a problem with the pump station valve (e.g., excessive or insufficient head), it is determined to be a pump station anomaly. When a gate anomaly is identified, the system will perform loop restriction operations on the abnormal gate, such as closing or limiting the flow rate through a control loop, to prevent excessive or insufficient water flow that could lead to system malfunction.In the scenario simulation, the effects of control loop limitations are simulated, and their impact on parameters such as water level and flow is analyzed to ensure the normal operation of the water conservancy project. When a pump station malfunction is detected, the system will perform a high-lift valve switching operation, i.e., automatically adjust the pump station valves to ensure reasonable load distribution and avoid equipment damage or efficiency reduction due to excessive lift. The valve switching operation is executed in the simulation scenario, and the working status of the pump station and the water flow regulation effect after the switching are analyzed. Based on the control measures of gate loop limitations and pump station valve switching, water conservancy project scenario simulation data is generated, including the equipment operating status, control effect, and recovery status after emergency response. Through the feedback of scenario simulation data, emergency resources are dispatched, and necessary emergency operations are taken, such as activating backup pump stations and adjusting water levels, to ensure that the system returns to normal operation as soon as possible.

[0117] As an example of the present invention, reference is made to Figure 3 As shown, step S3 in this example includes:

[0118] Step S31: Construct an operation and maintenance decision-making scheme based on water conservancy project scenario simulation data and water conservancy project composite state perception data to obtain the water conservancy project operation and maintenance decision-making scheme; calculate the comprehensive score of the water conservancy project operation and maintenance decision-making scheme to generate the water conservancy project operation and maintenance decision-making scheme score;

[0119] Step S32: Based on the score of the water conservancy project operation and maintenance decision scheme, the optimal operation and maintenance scheme is selected from the water conservancy project operation and maintenance decision schemes, and the selected optimal operation and maintenance scheme is sent to the water conservancy project intelligent control system for remote control execution in order to obtain remote control feedback data of the water conservancy project.

[0120] Step S33: Convert the remote feedback data of the water conservancy project into operation and maintenance data charts to generate an intelligent operation and maintenance report for the water conservancy project.

[0121] In this embodiment of the invention, a decision-making scheme is constructed based on simulation data of a water conservancy project scenario and composite state perception data of a water conservancy project. The simulation data includes information such as equipment operating status, fault prediction, and emergency response; the composite state perception data covers real-time monitoring information such as water level, flow rate, and pump station operation. Key indicators for operation and maintenance decisions are determined, such as equipment health status, emergency response capability, energy consumption, and cost. Based on these variables, an operation and maintenance decision-making scheme is constructed. Different operation and maintenance decision-making schemes are automatically generated based on a preset objective function (such as minimizing energy consumption, shortest recovery time, and minimum failure probability). For example, different control modes, resource allocation strategies, or backup equipment activation strategies can be set. For each operation and maintenance decision-making scheme, corresponding weights are assigned based on its different influencing factors (such as energy efficiency, equipment operating stability, and fault recovery time). A weighted scoring method is used to calculate the comprehensive score of each decision-making scheme. Define a comprehensive scoring formula, for example: O&M Score = α1⋅Equipment Health + α2⋅Fault Response Time + α3⋅Energy Efficiency + α4⋅Cost-Effectiveness, where α1, α2, α3, and α4 are the weights of different indicators. Calculate the comprehensive score for all generated O&M decision schemes and output the score for each scheme. Sort the O&M decision schemes from highest to lowest based on their comprehensive scores. Select the scheme with the highest score as the optimal O&M scheme. In addition to the score, other screening criteria can be set, such as feasibility, resource matching, and project implementation cycle, to further optimize the screening process. Extract the optimal scheme with the highest score and prepare to send it to the intelligent control system of the water conservancy project. Transmit the selected optimal O&M scheme to the intelligent control system of the water conservancy project, instructing the system to perform control operations according to the optimal scheme. The intelligent control system executes remote control tasks according to the O&M scheme, generating feedback data, including system response status, changes in equipment operating status, and adjustment results. Convert the remote control feedback data of the water conservancy project into charts to generate visualized O&M data icons. This includes, but is not limited to: equipment status icons (such as the opening and closing status of pump stations and gates), dynamic curves of water levels and flow rates, and graphs showing changes in operation and maintenance indicators (such as energy efficiency and frequency of failures). Through real-time data updates, dynamic interactive icons are generated to help decision-makers instantly view the operation and maintenance status. Based on the converted icon data, detailed intelligent operation and maintenance reports are generated.

[0122] Preferably, step S4 includes the following steps:

[0123] Step S41: Store the intelligent operation and maintenance report of the water conservancy project to the cloud platform and set up an automatic backup mechanism; back up the data of 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.

[0124] Step S42: Share water conservancy project intelligent operation and maintenance reports and backup data across multiple platforms via API interface to perform collaborative operation and maintenance of water conservancy projects.

[0125] In this embodiment of the invention, the generated intelligent operation and maintenance report is uploaded to a cloud platform for storage via a data transmission channel. A suitable cloud storage service (such as AWS, Google Cloud, Azure, etc.) is selected to store the report data on a cloud server or database. HTTPS protocol is used for secure transmission. Different storage methods (such as SQL database, object storage, etc.) are used depending on the data type and importance. When storing the report, a unique identifier (such as UUID) is generated to distinguish different versions and time points of the report. A scheduled backup mechanism is set up, and the backup frequency (such as daily, weekly, monthly, etc.) is determined according to business needs. Backups can be incremental or full backups, backing up only data that has changed since the last backup, and backing up all data to ensure complete recovery even in the event of data loss. The intelligent operation and maintenance report for water conservancy projects is backed up regularly through automated scripts or automatic backup services provided by the cloud platform. Backup data includes report content, generation time, version information, etc. Automated tools of the cloud platform, such as AWS Lambda or Google Cloud Functions, are used to set triggers (such as scheduled triggers or triggers when the report is updated) for data backup. Backup data is stored encrypted to ensure data security. After each backup, the system generates a backup file and assigns a unique identifier to the backup data for later restoration or viewing of historical backup versions.

[0126] This specification provides a digital twin water conservancy project operation and maintenance monitoring system for executing the aforementioned digital twin water conservancy project operation and maintenance monitoring method. The digital twin water conservancy project operation and maintenance monitoring system includes:

[0127] The digital twin module is used to acquire relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects to generate a basic digital twin model; and perform global optimization on the basic digital twin model to generate a dynamic digital twin water conservancy project model.

[0128] The engineering analysis module is used for remote monitoring of water conservancy projects based on a dynamic digital twin water conservancy engineering model, generating water conservancy engineering monitoring data; performing composite state perception on the water conservancy engineering monitoring data, generating a dynamic monitoring map of the water conservancy engineering; predicting water conservancy engineering fault points based on the composite state perception data of the water conservancy engineering according to the dynamic monitoring map, generating water conservancy engineering fault point data; and performing multi-scenario simulation on the dynamic digital twin water conservancy engineering model based on the water conservancy engineering fault point data, generating water conservancy engineering scenario simulation data.

[0129] The operation and maintenance decision module is used to construct operation and maintenance decision schemes based on water conservancy project scenario simulation data and water conservancy project composite state perception data, and obtain water conservancy project operation and maintenance decision schemes; the water conservancy project operation and maintenance decision schemes are sent to the water conservancy project intelligent control system for remote control execution, and water conservancy project intelligent operation and maintenance reports are generated;

[0130] The data sharing module is used to back up the intelligent operation and maintenance report of water conservancy projects and generate intelligent operation and maintenance backup data of water conservancy projects; it also enables multi-platform data sharing of intelligent operation and maintenance reports and backup data of water conservancy projects through API interfaces to perform collaborative operation and maintenance of water conservancy projects.

[0131] The beneficial effects of this invention lie in the fact that the digital twin module constructs a dynamic digital twin water conservancy engineering model through three-dimensional geometric feature extraction and hydrological structure data association, realizing comprehensive digital and virtual management of water conservancy projects. It supports global optimization of complex water conservancy projects, providing high-precision data models for subsequent monitoring and simulation. The engineering analysis module collects real-time water conservancy project operation data through remote monitoring, generating dynamic monitoring maps to comprehensively perceive the project's operational status. Utilizing fault point prediction technology, it can identify potential risk points before faults occur, improving early warning capabilities and reducing the probability of sudden accidents. The operation and maintenance decision module combines scenario simulation data and status perception data to construct efficient operation and maintenance decision-making schemes, ensuring the scientific and targeted nature of decisions. The remote control function reduces reliance on on-site manual intervention, significantly improving operation and maintenance efficiency and reducing operating costs. The data sharing module, through data backup and multi-platform data sharing, realizes efficient distribution and collaborative operation of intelligent operation and maintenance reports: various departments and platforms work synchronously based on shared data, improving 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 secure storage and disaster recovery capabilities of operation and maintenance data, enabling rapid recovery even in the event of emergencies. Through full lifecycle monitoring, analysis, decision-making, and sharing, a closed-loop water conservancy project management process is established, guaranteeing the long-term stable operation of the system. The multi-scenario simulation function can simulate different operating conditions, providing decision support for water conservancy project design optimization, risk assessment, and emergency response. This enhances the adaptability and resilience of water conservancy projects, effectively coping with complex and ever-changing operating environments. Therefore, this invention, through the construction of a dynamic model using digital twin technology, achieves precise, intelligent, and collaborative operation and maintenance monitoring of water conservancy projects, improving the accuracy and intelligence level of water conservancy project operation and maintenance monitoring.

[0132] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0133] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A digital twin method for monitoring the operation and maintenance of water conservancy projects, characterized in that, The following steps are involved: Step S1: Obtain relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects to generate a digital twin basic model; A global optimization of the digital twin base model is performed to generate a dynamic digital twin hydraulic engineering model; wherein, step S1 includes the following steps: Step S11: Obtain relevant data for water conservancy projects, including topographic data, meteorological data, and hydrological data; Step S12: Perform data preprocessing on 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 engineering dataset. The data preprocessing includes data cleaning, data denoising, missing value imputation, and data standardization. Step S13: Extract three-dimensional geometric features based on the standard multi-source water conservancy engineering dataset to obtain three-dimensional geometric data of water conservancy engineering; perform hydrological structure data association based on the three-dimensional geometric data of water conservancy engineering to generate a digital twin basic model; Step S14: Extract the spatiotemporal features of the standard multi-source water conservancy project dataset to obtain the spatiotemporal feature data of water conservancy projects; use the spatiotemporal feature data of water conservancy projects to perform global optimization of 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 engineering model, perform remote monitoring of the water conservancy project to generate water conservancy engineering monitoring data; perform composite state perception of the water conservancy project on the monitoring data to generate a dynamic monitoring map of the water conservancy project; predict water conservancy project fault points based on the dynamic monitoring map and the composite state perception data to generate water conservancy project fault point data; perform multi-scenario simulation of the dynamic digital twin water conservancy engineering model based on the water conservancy project fault point data to generate water conservancy project scenario simulation data; wherein, step S2 includes the following steps: Step S21: Deploy regional sensors based on the dynamic digital twin water conservancy engineering model to obtain key area sensor deployment data; construct the Internet of Things (IoT) architecture based on the key area sensor deployment data to generate the water conservancy engineering IoT architecture; Step S22: Embed the IoT architecture of the water conservancy project into the dynamic digital twin water conservancy project model for remote monitoring of the water conservancy project, and generate water conservancy project monitoring data; perform time series difference on the water conservancy project monitoring data to generate water conservancy project time series difference monitoring data; Step S23: Perform composite state perception on the time-series differential monitoring data of water conservancy projects to generate composite state perception data of water conservancy projects; perform perception mapping on the composite state perception data of water conservancy projects to obtain dynamic monitoring maps of water conservancy projects; Step S24: Based on the dynamic monitoring map of the water conservancy project, predict the fault points of the water conservancy project from the composite state perception data of the water conservancy project, and generate water conservancy project fault point data; based on the water conservancy project fault point data, perform multi-scenario simulation on the dynamic digital twin water conservancy project model, and generate water conservancy project scenario simulation data. The process of using time-series differential monitoring data of water conservancy projects to achieve real-time status perception of water conservancy projects includes: Biodiversity analysis was performed on time-series differential monitoring data of water conservancy projects to generate regional biological monitoring data for water conservancy projects. The biodiversity analysis included the distribution analysis of fish, algae, and microbial communities. Nutrient cycling was calculated based on the regional biological monitoring data of water conservancy projects to obtain regional nutrient cycling data for water conservancy projects. Based on the nutrient cycle data of the water conservancy project area, the dynamic digital twin water conservancy project model is used to perceive the status of engineering facilities and generate water conservancy project facility perception data; through the water conservancy project facility perception data, the nutrient cycle data of the water conservancy project area is used to perceive the water body connectivity, thereby generating water conservancy project environmental perception data. By integrating the perception data of water conservancy engineering facilities and the perception data of water conservancy engineering environment, we can obtain the composite state perception data of water conservancy engineering. Step S3: Based on the simulation data of water conservancy engineering scenarios and the composite state perception data of water conservancy engineering, construct the operation and maintenance decision scheme to obtain the operation and maintenance decision scheme of water conservancy engineering; send the operation and maintenance decision scheme of water conservancy engineering to the intelligent control system of water conservancy engineering for remote control execution, and generate the intelligent operation and maintenance report of water conservancy engineering. Step S4: Back up the intelligent operation and maintenance report of the water conservancy project to generate intelligent operation and maintenance backup data of the water conservancy project; share the intelligent operation and maintenance report and the intelligent operation and maintenance backup data of the water conservancy project across multiple platforms through the API interface to perform collaborative operation and maintenance of the water conservancy project.

2. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1, characterized in that, The correlation of hydrological structure data based on the three-dimensional geometric data of water conservancy projects includes: Topographic features are extracted from the three-dimensional geometric data of water conservancy projects to obtain topographic feature data of water conservancy projects; hydrological element data is matched with standard multi-source water conservancy project datasets using the topographic feature data of water conservancy projects to obtain hydrological element matching data; Based on the hydrological element matching data, watershed zoning boundary analysis is performed on the topographic feature data of water conservancy projects to obtain watershed zoning boundary data; the coordinates of structural key points are marked on the three-dimensional geometric data of water conservancy projects using the watershed zoning boundary data to obtain structural key point coordinate data. Hydrological structural patches are constructed from the watershed boundary data based on the coordinate data of key structural points to obtain hydrological structural patch data; hydrological structural attributes are associated with the hydrological structural patch data to obtain hydrological structural associated data; dynamic spatiotemporal feature mapping is performed on the hydrological structural associated data to obtain dynamic hydrological structural feature data. A digital twin basic model is obtained by integrating hydrological and geometric data to perform spatial modeling of dynamic hydrological structural characteristic data.

3. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1, characterized in that, Step S24 includes the following steps: Step S241: Based on the dynamic monitoring map of the water conservancy project, perform structural association of controllable equipment of the water conservancy project on the composite state perception data of the water conservancy project to generate controllable equipment association data of the water conservancy project; collect historical fault data of the controllable equipment of the water conservancy project on the controllable equipment association data of the water conservancy project to obtain historical fault records of the controllable equipment of the water conservancy project. Step S242: Divide the historical fault records of controllable equipment in water conservancy projects into a model training set and a model test set; train the model on the model training set using the support vector machine algorithm to generate a pre-model for predicting fault points in water conservancy projects; use the model test set to perform model optimization and iteration on the pre-model for predicting fault points in water conservancy projects, thereby generating a model for predicting fault points in water conservancy projects. Step S243: Import the associated data of controllable equipment in the water conservancy project into the water conservancy project fault point prediction model to predict fault points and generate water conservancy project fault point data; Step S244: Based on the fault point data of the water conservancy project, set up simulation scenarios for the dynamic digital twin water conservancy project model to generate water conservancy project fault simulation setting scenarios; through the water conservancy project fault simulation setting scenarios, perform emergency operation and maintenance response simulation on the associated data of controllable equipment of the water conservancy project to generate water conservancy project scenario simulation data.

4. The digital twin water conservancy project operation and maintenance monitoring method according to claim 3, characterized in that, The emergency operation and maintenance response simulation of controllable equipment in water conservancy projects by setting up scenarios for fault simulation includes the following steps: The simulation scenarios for water conservancy project faults are divided into normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios. The simulation sequence of the normal operation scenarios, fault triggering scenarios, and post-fault extension scenarios is then set to obtain the scenario simulation sequence. Extract gate equipment association data and pump station equipment association data from the controllable equipment association data of water conservancy projects; perform data association analysis on the gate equipment association data, pump station equipment association data and water conservancy project fault point data respectively to generate gate opening degree impact data and pump station reservoir capacity regulation data; The simulation anomaly feedback data is obtained by performing a scenario simulation sequence on the gate opening influence data and the pump station capacity adjustment data. This includes gate anomaly feedback data and pump station anomaly feedback data. The anomaly type of the simulation anomaly feedback data is then determined. When the simulation anomaly feedback data is gate anomaly feedback data, the corresponding gate is restricted by the gate opening and closing loop, and control loop restriction data is generated. When the simulation anomaly feedback data is pump station anomaly feedback data, the valves of the corresponding pump station are switched to high head, and pump station valve limit data is generated; based on the control loop limit data and pump station valve limit data, emergency operation and maintenance response simulation is performed on the controllable equipment correlation data of the water conservancy project to generate water conservancy project scenario simulation data.

5. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Construct an operation and maintenance decision-making scheme based on water conservancy project scenario simulation data and water conservancy project composite state perception data to obtain the water conservancy project operation and maintenance decision-making scheme; calculate the comprehensive score of the water conservancy project operation and maintenance decision-making scheme to generate the water conservancy project operation and maintenance decision-making scheme score; Step S32: Based on the score of the water conservancy project operation and maintenance decision scheme, the optimal operation and maintenance scheme is selected from the water conservancy project operation and maintenance decision schemes, and the selected optimal operation and maintenance scheme is sent to the water conservancy project intelligent control system for remote control execution in order to obtain remote control feedback data of the water conservancy project. Step S33: Convert the remote feedback data of the water conservancy project into operation and maintenance data charts to generate an intelligent operation and maintenance report for the water conservancy project.

6. The digital twin water conservancy project operation and maintenance monitoring method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Store the intelligent operation and maintenance report of the water conservancy project to the cloud platform and set up an automatic backup mechanism; back up the data of 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: Share water conservancy project intelligent operation and maintenance reports and backup data across multiple platforms via API interface to perform collaborative operation and maintenance of water conservancy projects.

7. A digital twin water conservancy project operation and maintenance monitoring system, characterized in that, For executing the digital twin water conservancy project operation and maintenance monitoring method as described in claim 1, the digital twin water conservancy project operation and maintenance monitoring system includes: The digital twin module is used to acquire relevant data of water conservancy projects; extract three-dimensional geometric features from the relevant data of water conservancy projects to obtain three-dimensional geometric data of water conservancy projects; associate hydrological structure data based on the three-dimensional geometric data of water conservancy projects to generate a basic digital twin model; and perform global optimization on the basic digital twin model to generate a dynamic digital twin water conservancy project model. The engineering analysis module is used for remote monitoring of water conservancy projects based on a dynamic digital twin water conservancy engineering model, generating water conservancy engineering monitoring data; performing composite state perception on the water conservancy engineering monitoring data, generating a dynamic monitoring map of the water conservancy engineering; predicting water conservancy engineering fault points based on the composite state perception data of the water conservancy engineering according to the dynamic monitoring map, generating water conservancy engineering fault point data; and performing multi-scenario simulation on the dynamic digital twin water conservancy engineering model based on the water conservancy engineering fault point data, generating water conservancy engineering scenario simulation data. The operation and maintenance decision module is used to construct operation and maintenance decision schemes based on water conservancy project scenario simulation data and water conservancy project composite state perception data, and obtain water conservancy project operation and maintenance decision schemes; the water conservancy project operation and maintenance decision schemes are sent to the water conservancy project intelligent control system for remote control execution, and water conservancy project intelligent operation and maintenance reports are generated; The data sharing module is used to back up the intelligent operation and maintenance report of water conservancy projects and generate intelligent operation and maintenance backup data of water conservancy projects; it also enables multi-platform data sharing of intelligent operation and maintenance reports and backup data of water conservancy projects through API interfaces to perform collaborative operation and maintenance of water conservancy projects.

Images