Intelligent flood prevention method for water conservancy project

Abstract

The invention provides an intelligent flood prevention method for a water conservancy project, and belongs to the technical field of water conservancy projects. The method comprises the following steps: constructing a digital twin model of a water conservancy project and establishing a digital map; obtaining an engineering operation state and meteorological and hydrological data of an area where a water conservancy project is located in real time, extracting change characteristics of water yield, rainfall and engineering conditions, and updating the state of the digital twin model; the operation process of the water conservancy project is simulated to predict the flood situation situation and the disaster development trend; the surrounding environment of the water conservancy project is monitored in real time, and possible abnormal conditions are sensed in time; real-time analysis and optimization decision making are carried out, timely and effective flood prevention suggestions are provided, and flood prevention measures are automatically triggered when necessary; the digital twinborn model is updated and optimized regularly, the accuracy and reliability of the model are continuously improved in combination with actual operation data and new technical progress, and it is ensured that the flood prevention system is always kept in the optimal state.

Description

Technical Field

[0001] This application relates to the field of water conservancy engineering technology, specifically to an intelligent flood control method for water conservancy projects. Background Technology

[0002] Water conservancy projects play a vital role in modern society, but they face various challenges from the natural environment, especially the floods during the flood season, which put enormous pressure on the safety and effectiveness of these projects. Traditional flood control methods rely mainly on static design and experience-based judgment, lacking real-time accuracy and making it difficult to cope with the impacts of climate change and extreme weather events.

[0003] With the rapid development of digital technology and artificial intelligence, applying them to flood control in water conservancy projects has become an effective way to improve system response speed and forecast accuracy. However, some existing digital flood control systems still face challenges, such as insufficient accuracy of digital models, inadequate real-time data acquisition, and insufficient intelligence of decision support systems.

[0004] Therefore, it is necessary to provide a new intelligent flood control method for water conservancy projects to better adapt to complex and ever-changing hydrological and meteorological conditions and improve the disaster resistance and operational efficiency of water conservancy projects. Summary of the Invention

[0005] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide an intelligent flood control method for water conservancy projects, which includes the following steps:

[0006] Step 1: Construct a digital twin model of the water conservancy project and create a digital map;

[0007] Step 2: Real-time acquisition of the project's operational status and meteorological and hydrological data of the area where the water conservancy project is located, and extraction of the changing characteristics of water volume, rainfall, and project status to update the status of the digital twin model;

[0008] Step 3: Simulate the operation of water conservancy projects to predict flood conditions and disaster development trends;

[0009] Step 4: Monitor the surrounding environment of water conservancy projects in real time to detect possible abnormalities in a timely manner;

[0010] Step 5: Conduct real-time analysis and optimize decision-making, propose timely and effective flood control suggestions, and automatically trigger flood control measures when necessary;

[0011] Step 6: Regularly update and optimize the digital twin model, combining actual operational data and new technological advancements to continuously improve the model's accuracy and reliability, ensuring the flood control system remains in optimal condition.

[0012] Preferably, step 1 includes the following steps:

[0013] Collect structural parameters and location information of water conservancy facilities, including data on the type, scale, function, design, construction and operation of water conservancy projects, as well as the spatial coordinates, elevation and orientation information of water conservancy projects;

[0014] Using 3D modeling software, a digital twin model of a water conservancy project is constructed based on the structural parameters and location information of the water conservancy facilities. This model includes the main structure, ancillary facilities, and surrounding environment of the water conservancy project.

[0015] Collect topographic, geomorphological, soil, vegetation and other natural geographical data of the area where the water conservancy project is located in order to create a digital map.

[0016] Preferably, step 2 includes the following steps:

[0017] By utilizing satellite cloud images, remote sensing images, and geographic information data, meteorological and hydrological data of the area where the water conservancy project is located can be obtained in real time, including rainfall, evaporation, temperature, humidity, wind speed, wind direction, cloud cover, sunshine, water level, flow rate, and water quality parameters.

[0018] By utilizing satellite cloud images, remote sensing images, and geographic information data, the real-time operational status of water conservancy projects can be obtained, including structural changes, switch status, operating modes, and fault alarm information.

[0019] Based on real-time meteorological and hydrological data and the operational status of the project, the changing characteristics of water volume, rainfall and project conditions are extracted, their impact on water conservancy projects is analyzed, potential problems and risks are predicted, and corresponding countermeasures and optimization plans are formulated.

[0020] The extracted change characteristics, analysis results, prediction results, countermeasures and optimization schemes are fed back into the digital twin model of the water conservancy project in real time, so as to realize the state update of the digital twin model and maintain its synchronization and twin nature with the physical water conservancy project.

[0021] Preferably, step 3 includes the following steps:

[0022] The extracted characteristics of changes in water output, rainfall, and engineering conditions are input into the digital model of the water conservancy project as a basis for parameter correction, so as to ensure the consistency and accuracy between the digital model and the actual project.

[0023] Based on the type, function, structure, and operational characteristics of water conservancy projects, appropriate simulation methods and simulation accuracy are selected to simulate the operation process of water conservancy projects, including simulations of water flow, sediment, water quality, and structure, and to calculate the operational status and output results of water conservancy projects.

[0024] Based on the simulation results, the flood situation and disaster development trend are predicted, and the safety, effectiveness and sustainability of water conservancy projects are assessed, providing scientific basis and suggestions for the decision-making and management of water conservancy projects.

[0025] Preferably, step 4 includes the following steps:

[0026] Based on the type, scale, location, and functional characteristics of the water conservancy project, determine the monitoring content, indicators, frequency, scope, and requirements;

[0027] In accordance with technical specifications and operating procedures, the monitoring equipment was installed in key parts of the water conservancy project;

[0028] The data collected by the monitoring equipment is transmitted to the monitoring center for storage, organization, analysis, and evaluation.

[0029] By comparing the differences between monitoring data and normal data, abnormal changes in the surrounding environment of water conservancy projects were found, including increased seepage flow, abnormal water level, water pollution, surface subsidence, and deformation of support structures.

[0030] Report the type, location, extent, cause, and impact of any abnormal situation to the relevant parties in a timely manner, and take appropriate emergency measures to prevent the situation from escalating or worsening.

[0031] Preferably, step 4 includes the following steps:

[0032] Establish an intelligent decision support system to collect, process, analyze, and display flood data, generating flood situation maps, disaster risk maps, and flood control measure maps;

[0033] Based on the predicted flood situation, forecast rainfall, water level, flow rate and flood peak indicators for a period of time in the future, and assess the scale, scope, degree and impact of possible flood disasters;

[0034] Based on the development trend of disasters, analyze the causes, mechanisms, paths and consequences of floods, identify key nodes, weak links and emergencies of disasters, and predict the evolution process and possible turning points of disasters;

[0035] In light of abnormal situations, monitor the operational status of flood control projects, facilities, and equipment; identify and analyze abnormal signals, causes of malfunctions, scope of impact, and degree of harm; promptly issue alarms and propose emergency measures.

[0036] Real-time analysis and optimization decisions are performed, taking into account flood control objectives, constraints, resource allocation and risk assessment factors, generating multiple feasible flood control plans, and selecting the optimal plan based on different decision criteria and preferences;

[0037] The optimal flood control plan is transformed into specific flood control recommendations, and these recommendations are dynamically adjusted based on changes in the flood situation and decision-making feedback, thereby achieving closed-loop management of flood control decision-making.

[0038] In emergency situations, the intelligent decision support system automatically triggers flood control measures to respond to floods with the fastest speed and highest efficiency.

[0039] Preferably, step 6 includes the following steps:

[0040] Regularly collect and analyze actual operational data, compare and verify it with the digital twin model, and evaluate the deviation and error of the digital twin model;

[0041] Improve and optimize the digital twin model to enhance its accuracy and sensitivity, and increase its functionality and adaptability;

[0042] Using digital twin models for simulation and testing, we can verify the effectiveness and stability of the model, detect its performance and quality, and identify and resolve its problems and defects.

[0043] Update and replace digital twin models in a timely manner to maintain consistency and synchronization between the models and actual operational data, ensuring that the models can accurately reflect and predict the status and behavior of the flood control system.

[0044] Compared with the prior art, this application has at least the following technical effects or advantages.

[0045] This application improves the response speed, forecast accuracy, and response capability of flood control systems in water conservancy projects by comprehensively utilizing digital technology, real-time monitoring, and intelligent decision support systems. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an intelligent flood control method for water conservancy projects disclosed in an embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0048] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0050] like Figure 1 As shown, an intelligent flood control method for water conservancy projects includes the following steps:

[0051] Step 1: Construct a digital twin model and create a digital map of the water conservancy project. A digital twin model is a virtual model designed to accurately reflect a physical object. It can utilize real-time data and simulation, machine learning, and inference to replicate the operational status and behavior of the water conservancy project, and can be used to study performance issues and improvement plans. A digital map is a spatial data management and display platform based on a geographic information system (GIS), which can visualize and analyze elements such as topography, landforms, water systems, and climate in the area where the water conservancy project is located. By constructing a digital twin model and digital map of the water conservancy project, comprehensive, multi-dimensional, and real-time monitoring and simulation of the project can be achieved, providing data support and technical means for subsequent flood control work.

[0052] Step 2 involves acquiring real-time data on the project's operational status and meteorological and hydrological data for the area where the water conservancy project is located, extracting the changing characteristics of water volume, rainfall, and project conditions, and updating the digital twin model's status. Through various sensors and monitoring equipment installed on the water conservancy project, and data sharing with meteorological and hydrological departments, real-time data on the project's operational status and the meteorological and hydrological data for the area can be acquired, such as water level, flow rate, pressure, temperature, humidity, wind speed, wind direction, rainfall, runoff, and water quality. Data analysis and processing can extract the changing characteristics of water volume, rainfall, and project conditions, such as water volume trends, rainfall intensity, and the degree of project damage. By feeding this data and characteristics back to the digital twin model, the model's status can be updated in real-time, ensuring consistency with the physical object and providing accurate input for subsequent simulations and predictions.

[0053] Step 3 involves simulating the operation of water conservancy projects to predict flood conditions and disaster development trends. Using digital twin models, the operation of water conservancy projects under different meteorological and hydrological conditions can be simulated, such as reservoir impoundment, flood discharge, water dispatch, and water transfer, as well as potential anomalies like dam breaches, breaches, overtopping, and scouring. Through simulation, flood conditions and disaster development trends can be predicted, including water level, flow rate, water pressure, water potential, flood peak, flood extent, and disaster impact. Simulation also allows for the assessment of the flood control capacity and safety of water conservancy projects, providing a scientific basis for subsequent flood control decisions.

[0054] Step 4: Monitor the surrounding environment of water conservancy projects in real time to promptly detect potential anomalies. Digital maps can be used to monitor changes in the surrounding environment, such as topography, landforms, vegetation, soil, population, buildings, and transportation. Remote sensing, drones, and satellites can be used to promptly detect potential anomalies, such as landslides, debris flows, collapses, fissures, and water accumulation. Real-time monitoring and detection allow for the timely identification and reporting of risk factors in the surrounding environment, providing information support for subsequent flood control and emergency response.

[0055] Step 5 involves real-time analysis and optimization decision-making to propose timely and effective flood control recommendations and automatically trigger flood control measures when necessary. By comprehensively utilizing information such as digital twin models, digital maps, meteorological and hydrological data, engineering operation status, flood forecasts, and disaster assessments, real-time analysis and optimization decisions can be made to propose timely and effective flood control recommendations, such as adjusting reservoir water storage, flood discharge, scheduling, and water transfer plans; reinforcing or repairing damaged parts of engineering projects; and evacuating or relocating threatened personnel and property. When necessary, flood control measures can be automatically triggered, such as activating floodgates, starting emergency pumping stations, starting emergency generators, and activating emergency broadcasts, to mitigate or avoid disaster losses.

[0056] Step 6: Regularly update and optimize the digital twin model. Incorporate actual operational data and new technological advancements to continuously improve the model's accuracy and reliability, ensuring the flood control system remains in optimal condition. By regularly collecting and analyzing actual operational data, the digital twin model can be updated and optimized, refining model parameters, adding model details, improving model accuracy, eliminating model errors, and enhancing model adaptability. By integrating new technological advancements, the digital twin model can be improved and innovated, introducing new modeling methods, adding new model functions, expanding new model applications, and enhancing model intelligence. Regular updates and optimizations continuously improve the accuracy and reliability of the digital twin model, ensuring the flood control system remains in optimal condition.

[0057] Preferably, step 1 includes the following steps:

[0058] Step 1.1 involves collecting structural parameters and location information of water conservancy facilities, including data on the type, scale, function, design, construction, and operation of the water conservancy project, as well as its spatial coordinates, elevation, and orientation. This step requires collecting detailed structural parameters of the water conservancy facilities, including but not limited to the dimensions, shape, and material properties of structures, the specifications and performance parameters of equipment, and the diameter and length of pipelines. Simultaneously, the coordinates, elevation, and orientation information of the water conservancy project's location must be obtained to ensure that the model accurately reflects the actual engineering characteristics.

[0059] Step 1.2: Using 3D modeling software, construct a digital twin model of the water conservancy project based on its structural parameters and location information. This model includes the main structure, ancillary facilities, and surrounding environment of the water conservancy project. Based on the collected structural parameters and location information of the water conservancy facilities, use professional 3D modeling software such as AutoCAD, Revit, and SketchUp to construct a digital twin model of the water conservancy project. The model includes the main structure, ancillary facilities, and possible surrounding environmental elements. Ensure the accuracy, completeness, and visibility of the model.

[0060] Step 1.3 involves collecting topographic, geomorphological, soil, vegetation, and other natural geographic data of the area where the water conservancy project is located to create a digital map. This data can be obtained through a Geographic Information System (GIS) by acquiring satellite imagery, digital elevation models (DEMs), vegetation cover maps, soil types, and other data. Integrating this data to create a digital map provides more comprehensive background information, making the digital twin model more realistic and integrated.

[0061] Through the above steps, the established digital twin model can accurately reflect the structural characteristics of water conservancy projects, taking into account the geographical context of the surrounding environment. Such a model not only facilitates real-time monitoring and management of water conservancy facilities but also provides a reliable basis for engineering decision-making and planning.

[0062] The collection of structural parameters for water conservancy facilities specifically includes the geometric dimensions of the gate opening and closing mechanism, material strength parameters, transmission system specifications, hydraulic cylinder working pressure range of 2.5-8.0MPa, and three motor power ratings of 15kW, 22kW, and 37kW, as well as detailed technical specifications for gate plate thickness ranging from 12 to 25mm. The location information also includes the coordinates of the elevation control points of the water conservancy project relative to the riverbed datum, the angle deviation between the project axis and the geographical north direction controlled within ±2°, and the measurement data of the safe distance between the project boundary and surrounding buildings, ensuring that the digital twin model can effectively reflect the actual physical characteristics and spatial relationships of the water conservancy facilities.

[0063] The accuracy levels of 3D modeling are divided into five levels, from L1 to L5. Level L3 requires geometric errors to be controlled within ±20mm, material texture resolution to reach 1024×1024 pixels, and the number of polygons in the model to be controlled within the range of 200,000 to 500,000. The digital twin model also includes a dynamic simulation module, which can simulate in real time the graded change process of the gate opening angle within the range of 0°-90°, the basic flow state calculation of water flow, and the simplified analysis results of structural stress distribution, ensuring that the model has basic dynamic display and analysis capabilities.

[0064] The collection of natural geographic data for the area where the water conservancy project is located includes high-resolution satellite imagery data with a resolution of 1-2m, lidar point cloud data with a density of no less than 4 points per square meter, and soil permeability coefficient measurement range of 10. -6 Up to 10~ 3 m / s, vegetation coverage classification accuracy reaches over 75%; topographic data adopts conventional mapping with contour line spacing of 2-5 meters, and slope analysis accuracy reaches 0.5°, ensuring that the digital map can better reflect the natural environmental characteristics of the project area and provide basic geographic information support for flood control decision-making.

[0065] Preferably, step 2 includes the following steps:

[0066] Step 2.1: Utilize satellite cloud images, remote sensing images, and geographic information data to acquire real-time meteorological and hydrological data of the area where the water conservancy project is located, including rainfall, evaporation, temperature, humidity, wind speed, wind direction, cloud cover, sunshine, water level, flow rate, and water quality parameters. These data are the foundation for the operation of the water conservancy project. Real-time monitoring and acquisition can more accurately reflect changes in hydrology and meteorology.

[0067] Step 2.2: Utilize satellite cloud images, remote sensing images, and geographic information data to obtain the real-time operational status of water conservancy projects, including structural changes, switch status, operating modes, and fault alarm information. This data can be collected in real time through sensors, monitoring equipment, etc., to ensure timely understanding of the operational status of water conservancy projects.

[0068] Step 2.3: Based on real-time acquired meteorological and hydrological data and the project's operational status, data analysis techniques are used to extract the changing characteristics of water volume, rainfall, water level, and flow rate. These characteristics are analyzed to assess their impact on the water conservancy project, and models are used to predict potential problems. Based on the predicted problems and risks, corresponding countermeasures and optimization plans are formulated.

[0069] Step 2.4 involves feeding the extracted change characteristics, analysis results, prediction results, countermeasures, and optimization schemes back into the digital twin model of the water conservancy project in real time. This real-time updating of the digital twin model ensures its synchronization and twinning with the actual water conservancy project. The status updates of the digital twin model will include current meteorological and hydrological conditions, the project's operational status, and potential problems and risks.

[0070] Through the aforementioned series of real-time data acquisition, analysis, prediction, and feedback steps, the digital twin model of a water conservancy project can more accurately reflect its actual operating status, providing support for real-time decision-making and responding to emergencies.

[0071] The real-time acquisition time interval for meteorological and hydrological data is set to update every 15 minutes in encrypted monitoring mode and every hour in conventional monitoring mode; the accuracy of rainfall measurement reaches 0.2mm, the accuracy of water level measurement reaches ±2cm, and the relative error of flow measurement is controlled within ±5%; the temperature sensor operates from -30°C to +70°C, the humidity sensor accuracy reaches ±3%RH, and the wind speed measurement range is 0-50m / s with an accuracy of ±0.5m / s.

[0072] The real-time acquisition of the project's operating status adopts an Internet of Things (IoT) sensor network architecture, with the number of sensor nodes determined according to the project scale; the gate opening sensor uses an absolute encoder with a resolution of 0.5° and a repeatability of ±0.2°; the water pump operating status monitoring includes five parameters: current, voltage, power, vibration, and temperature, with the vibration monitoring frequency range being 10Hz-5kHz and the temperature monitoring accuracy being ±1°C.

[0073] Among them, the extraction of water output change features adopts the sliding window technology, with the window length set to 12-24 hours and the step size of 2 hours, which can identify abnormal situations where the water output changes by more than 30% within 2 hours; the analysis of rainfall change features adopts the rainfall pattern recognition algorithm, which can distinguish different rainfall types such as heavy rain, moderate rain, and light rain, and analyze the spatiotemporal distribution characteristics of rainfall intensity.

[0074] The digital twin model's status is updated regularly, with the update frequency dynamically adjusted according to the severity of the flood situation. It is updated every 2 hours under normal circumstances, every 30 minutes during the flood season, and every 10 minutes in emergencies. The model's synchronization verification adopts a multi-dimensional verification method, including three levels: geometric consistency verification, physical parameter consistency verification, and operational status consistency verification. The data consistency check uses statistical analysis methods, which can identify and mark abnormal data, and the data validity reaches more than 95%, ensuring that the digital twin model and the physical project maintain reasonable synchronization.

[0075] Preferably, step 3 includes the following steps:

[0076] Step 3.1: Input the extracted characteristics of water output, rainfall, and engineering conditions into the digital model of the water conservancy project as the basis for parameter correction to ensure the consistency and accuracy between the digital model and the actual project. This step aims to ensure that the digital model can accurately reflect the changes in the actual project and achieve consistency and accuracy between the digital model and the actual project.

[0077] Step 3.2: Based on the type, function, structure, and operation characteristics of the water conservancy project, select an appropriate simulation method and simulation accuracy to simulate the operation process of the water conservancy project, including simulations of water flow, sediment, water quality, and structure, and calculate the operation status and output results of the water conservancy project.

[0078] Step 3.3: Based on the simulation results, predict the flood situation and disaster development trend, assess the safety, effectiveness and sustainability of water conservancy projects, and provide scientific basis and suggestions for the decision-making and management of water conservancy projects.

[0079] Through the above series of calibration, simulation and evaluation steps, the digital model of water conservancy project can more accurately reflect the actual operation status and provide scientific basis and suggestions for the decision-making and management of water conservancy projects.

[0080] The parameter correction is based on an adaptive adjustment algorithm, which periodically adjusts the model parameters according to the deviation between the actual measured data and the model prediction results. The water output correction coefficient ranges from 0.7 to 1.3, and parameter adjustment is triggered when the deviation between the measured value and the predicted value exceeds 15%. The rainfall correction takes into account the topographic influence factor, with a correction coefficient of 1.0 to 1.4 for mountainous areas and 0.8 to 1.2 for plains. The engineering condition parameter correction includes material aging coefficient, wear coefficient, performance degradation coefficient, etc.

[0081] The simulation method selects different numerical calculation methods according to the type of project. For reservoir projects, a simplified hydrodynamic model is used, with a calculation grid accuracy of 20m×20m and a time step of 5 minutes. For river projects, a one-dimensional hydraulic equation system is used to solve the problem, with a river section division accuracy of 200-500m. For sluice gate projects, empirical formulas combined with CFD simplification are used for calculation. The simulation accuracy is divided into three levels: basic, standard, and fine. The standard level simulation requires the calculation error to be controlled within 10%, and the ratio of calculation time to actual time to not exceed 1:5, meeting the real-time requirements while ensuring basic calculation accuracy.

[0082] The flood situation forecasting method employs a multi-model integrated forecasting approach, combining numerical weather prediction, statistical forecasting, and empirical forecasting models. The forecast lead time is divided into two time scales: short-term (1-3 days) and medium-term (4-7 days). The disaster development trend assessment uses scenario analysis, setting three levels of disaster scenarios: mild, moderate, and severe. Each scenario includes key indicators such as rainfall, peak flow, inundation area, and duration. The risk assessment results are expressed in a graded manner, including forecasts for high, medium, and low risk levels.

[0083] Preferably, step 4 includes the following steps:

[0084] Step 4.1: Based on the type, scale, location, and functional characteristics of the water conservancy project, determine the monitoring content, indicators, frequency, scope, and requirements. Specifically, based on different types of water conservancy projects, such as reservoirs, dams, sluices, and water pipelines, determine the monitoring content, such as water level, flow rate, pressure, temperature, humidity, wind speed, wind direction, rainfall, runoff, and water quality; based on different scales of water conservancy projects, such as large, medium, and small, determine the monitoring indicators, such as the upper and lower limits of water level, the rate of change, the maximum and minimum values ​​of flow rate, the normal and abnormal values ​​of pressure, the normal and abnormal values ​​of temperature, and the normal and abnormal values ​​of humidity. Constant values, rates of change, etc.; normal values, abnormal values, rates of change, etc. of wind speed; normal values, abnormal values, rates of change, etc. of wind direction; normal values, abnormal values, rates of change, etc. of rainfall; normal values, abnormal values, rates of change, etc. of runoff; normal values, abnormal values, rates of change, etc. of water quality; depending on the different locations of the water conservancy project, such as upstream, midstream, downstream, etc., determine the monitoring frequency, such as hourly, daily, weekly, monthly, etc.; the monitoring scope, such as single point, multiple points, entire area, etc.; monitoring requirements, such as accuracy, stability, reliability, etc.

[0085] Step 4.2: Install the monitoring equipment at key locations in the water conservancy project according to technical specifications and operating procedures. Specifically, select appropriate monitoring equipment based on the monitoring content, indicators, frequency, scope, and requirements, such as water level gauges, flow meters, pressure gauges, thermometers, hygrometers, anemometers, wind vanes, rain gauges, and water quality meters. Install the monitoring equipment at key locations in the water conservancy project according to technical specifications and operating procedures, such as the dam crest, toe, shoulder, and body of a reservoir; the top, inner slope, outer slope, and foundation of a dam; the upstream, downstream, gate, and pier of a sluice gate; and the inlet, outlet, intermediate, and branch pipes of a water pipeline. Perform debugging, calibration, and testing on the monitoring equipment to ensure its normal operation.

[0086] Step 4.3 involves transmitting the data collected by the monitoring equipment to the monitoring center for storage, organization, analysis, and evaluation. Specifically, technologies such as the Internet of Things (IoT), wireless communication, and satellite communication are used to transmit the data collected by the monitoring equipment to the monitoring center in real-time or at scheduled intervals. The monitoring center is a platform integrating data processing, data analysis, data display, and data management functions. It can store, organize, analyze, and evaluate monitoring data. For example, it uses technologies such as databases, data warehouses, and data lakes for data storage, backup, and recovery; data cleaning, data transformation, and data integration for data organization, standardization, and normalization; data mining, machine learning, and deep learning for data analysis, mining, and prediction; and data evaluation, data visualization, and data reporting for data evaluation, display, and reporting.

[0087] Step 4.4: By comparing the differences between the monitoring data and the normal data, abnormal changes in the surrounding environment of the water conservancy project are found, including increased seepage flow, abnormal water level, water pollution, surface subsidence and deformation of the support structure. Specifically, the data analysis function of the monitoring center is used to compare the monitoring data with normal data. Normal data refers to the monitoring data of the water conservancy project under normal operating conditions, such as the normal range of water level, flow rate, pressure, temperature, humidity, wind speed, wind direction, rainfall, runoff, and water quality. By comparing the differences between the monitoring data and the normal data, abnormal changes in the surrounding environment of the water conservancy project can be found, such as increased seepage flow, abnormal water level, water pollution, surface subsidence, and deformation of the support structure. For example, increased seepage flow may be caused by structural damage to the water conservancy project, crack expansion, increased permeability, etc.; abnormal water level may be caused by increased rainfall, increased flow, untimely flood discharge, etc.; water pollution may be caused by upstream sewage discharge, downstream backflow, reservoir siltation, etc.; surface subsidence may be caused by decreased groundwater level, geological structure changes, seismic activity, etc.; and deformation of the support structure may be caused by changes in water pressure, soil pressure, temperature, etc.

[0088] Step 4.5: Promptly report the type, location, severity, cause, and impact of the abnormal situation to relevant parties and take corresponding emergency measures to prevent its escalation and deterioration. Specifically, utilize the monitoring center's data reporting function to promptly report the type, location, severity, cause, and impact of the abnormal situation to relevant parties, such as the management department, maintenance department, and emergency response department of the water conservancy project. Based on the type, location, severity, cause, and impact of the abnormal situation, take corresponding emergency measures, such as strengthening monitoring, reinforcing structures, repairing cracks, adjusting water levels, adjusting flow rates, cleaning up pollution, evacuating personnel, and relocating property, to prevent the abnormal situation from escalating and deteriorating, and to ensure the safe operation of the water conservancy project and the safety and stability of the surrounding environment.

[0089] Through the above series of monitoring and emergency response steps, the safe operation of water conservancy projects can be ensured, potential risks can be prevented, abnormal situations can be dealt with in a timely manner, and the surrounding environment and the safety of people's lives and property can be protected to the greatest extent.

[0090] The monitoring content is determined according to the project level; the monitoring frequency is managed in a hierarchical manner, with one monitoring session per day under normal circumstances, two to three monitoring sessions per day during the flood season, and increased monitoring frequency in emergency situations; the monitoring range is centered on the main body of the project, with an upstream extension distance of no less than 1.5 times the characteristic scale of the project and a downstream extension distance of no less than 2 times the characteristic scale of the project; the monitoring accuracy requires displacement measurement accuracy to reach ±3mm, strain measurement accuracy to reach ±10με, and the relative error of seepage flow measurement to be controlled within ±8%, ensuring that obvious potential safety hazards in the project can be detected in a timely manner.

[0091] The monitoring equipment is installed according to the principle of key monitoring, with primary and backup equipment set up at critical monitoring points, and single-set configuration at general monitoring points. The annual equipment failure rate is controlled below 5%. The installation location is selected considering three factors: representativeness, accessibility, and safety. The sensor protection level reaches IP67, enabling it to operate normally in conventional water conservancy engineering environments. The data acquisition unit adopts an industrial-grade design, with an operating temperature range of -20°C to +70°C and a storage capacity of no less than 16GB. The communication method combines wired and wireless methods. Wired transmission uses fiber optic or network cable, while wireless transmission uses 4G or 5G networks, achieving a data transmission success rate of over 95%.

[0092] The data transmission to the monitoring center adopts a three-tier transmission architecture, with on-site, communication, and application layers for hierarchical processing. Data storage uses centralized database technology, supporting TB-level data storage, and data backup employs a dual backup strategy of local and off-site backup. Data processing follows a standardized workflow, including data cleaning, format conversion, and quality checks, achieving a data processing efficiency of 1000 records per second. Data analysis utilizes a big data analytics platform, supporting both real-time monitoring and historical analysis modes, with analysis result latency controlled within 5 minutes. Data evaluation employs a multi-dimensional evaluation index system, including three dimensions: data completeness, accuracy, and timeliness.

[0093] The abnormal changes were detected using a combination of statistical and trend analysis methods. The criteria for determining abnormal seepage flow were: daily average seepage flow exceeding 200% of the historical average for the same period or a continuous 6-hour seepage flow growth rate exceeding 50%. A tiered early warning mechanism was used to determine abnormal water levels: a yellow warning for exceeding the warning level by 0.3 meters, an orange warning for exceeding the warning level by 0.8 meters, and a red warning for exceeding the warning level by 1.2 meters. Water quality monitoring included three basic indicators: pH, turbidity, and dissolved oxygen; alarms were triggered when these indicators showed significant abnormalities. Surface subsidence monitoring accuracy reached ±5mm; a focus was initiated when cumulative subsidence exceeded 20mm.

[0094] The timely reporting of abnormal situations adopts a tiered reporting mechanism: on-site abnormalities must be reported within 15 minutes, regional abnormalities within 30 minutes, and watershed-level abnormalities within 1 hour. Abnormal situations are classified using a four-level system: Level I (major), Level II (significant), Level III (general), and Level IV (minor). Reported information includes the abnormality type, location, severity, scope of impact, and preliminary recommendations. Emergency response time requirements are: Level I abnormalities must activate the emergency plan within 30 minutes, Level II abnormalities within 1 hour, and other levels of abnormalities within 2 hours.

[0095] Preferably, step 5 includes the following steps:

[0096] Step 5.1: Establish an intelligent decision support system to collect, process, analyze, and display flood data, generating flood situation maps, disaster risk maps, and flood control measure maps. Specifically, establish an intelligent decision support system based on technologies such as cloud computing, big data, and artificial intelligence. This system can collect, process, analyze, and display flood data in real time. For example, it can use the Internet of Things, drones, and satellites to collect meteorological and hydrological data such as rainfall, water level, flow rate, and flood peak in the area where water conservancy projects are located. It can use data mining, machine learning, and deep learning methods to process and analyze flood data, extracting the characteristics, patterns, and trends of the flood situation. It can use visualization, graphical, and animation methods to display the flood data, generating flood situation maps, disaster risk maps, and flood control measure maps, providing data support and decision-making basis for subsequent decisions.

[0097] Step 5.2: Based on the predicted flood situation, forecast rainfall, water level, flow rate, and flood peak indicators for the future period, and assess the scale, scope, severity, and impact of potential floods. Specifically, using the flood situation map in the intelligent decision support system, combined with historical data and weather forecasts, forecast rainfall, water level, flow rate, and flood peak indicators for the future period. This can be achieved using methods such as time series analysis, neural networks, and regression analysis to predict rainfall, water level, flow rate, and flood peak at different time scales such as one day, one week, and one month. Using a disaster risk map, assess the scale, scope, severity, and impact of potential floods. This can be achieved using methods such as risk assessment models, risk indices, and risk levels to assess the potential casualties, property losses, and social impacts of floods. The forecast and assessment results are then displayed in the intelligent decision support system to provide reference and suggestions for subsequent decision-making.

[0098] Step 5.3: Combining the disaster development trend, analyze the causes, mechanisms, paths, and consequences of floods, identify key nodes, weak links, and emergencies, and predict the evolution process and possible turning points of the disaster. Specifically, using the disaster risk map in the intelligent decision support system, and combining it with the disaster development trend, analyze the causes, mechanisms, paths, and consequences of floods. For example, using causal analysis, system dynamics, disaster chains, and other methods, analyze the natural and human factors of floods, the formation process and impact mechanism of floods, the location and spread direction of floods, and the direct and indirect consequences of floods. Identify key nodes, weak links, and emergencies of the disaster. For example, using critical event analysis, weak signal analysis, and emergency event analysis, identify important, sensitive, and uncertain factors affecting the occurrence and development of floods. Predict the evolution process and possible turning points of the disaster. For example, using scenario analysis, simulation analysis, and early warning analysis, predict the possible development paths and change points of floods under different conditions. Display the analysis and prediction results in the intelligent decision support system to provide a basis and direction for subsequent decision-making.

[0099] Step 5.4: In conjunction with abnormal situations, monitor the operational status of flood control projects, facilities, and equipment, identify and analyze abnormal signals, causes of failures, scope of impact, and degree of harm, promptly issue alarms, and propose emergency measures. Specifically, by utilizing the flood control measures diagram in the intelligent decision support system and combining it with abnormal situations, the system monitors the operational status of flood control projects, facilities, and equipment. For example, it uses the Internet of Things, drones, satellites, and other means to monitor the structural integrity, functional normality, and safety stability of water conservancy projects; the working efficiency, work quality, and work safety of flood control facilities; and the operating parameters, operating status, and abnormalities of flood control equipment. It also identifies and analyzes abnormal signals, causes of failures, scope of impact, and degree of harm. For example, it uses methods such as fault diagnosis, fault analysis, and fault assessment to identify and analyze abnormal signals of flood control projects, facilities, and equipment, such as abnormal sounds, abnormal temperatures, and abnormal vibrations. It analyzes and determines the causes of failures, such as structural damage, functional failure, and external interference. It assesses and determines the scope of impact and degree of harm, such as impact on water level, flow rate, and safety. It promptly issues alarms and proposes emergency measures, such as using alarm systems, notification systems, and command systems to promptly report abnormal situations to relevant personnel and departments and propose emergency measures, such as emergency repairs, emergency dispatch, and emergency evacuation. The monitoring and alarm results are displayed in the intelligent decision support system to provide support and assurance for subsequent decision-making.

[0100] Step 5.5 involves real-time analysis and optimization decision-making. This involves comprehensively considering flood control objectives, constraints, resource allocation, and risk assessment factors to generate multiple feasible flood control plans. The optimal plan is then selected based on different decision-making criteria and preferences. Specifically, the data analysis and decision optimization functions of the intelligent decision support system are utilized for real-time analysis and optimization. Methods such as multi-objective programming, multi-criteria decision-making, and multi-stage decision-making are employed to comprehensively consider flood control objectives, constraints, resource allocation, and risk assessment factors, generating multiple feasible flood control plans. These include different plans for reservoir water storage, flood discharge, scheduling, and water transfer; different engineering reinforcement or repair plans; and different plans for personnel and property evacuation or relocation. The optimal plan is then selected based on different decision-making criteria and preferences, such as maximizing flood control benefits, minimizing flood control costs, minimizing flood control risks, or based on the decision-maker's subjective will, experience, and risk attitude.

[0101] Step 5.6: Transform the optimal flood control plan into specific flood control recommendations, and dynamically adjust the flood control recommendations based on changes in the flood situation and decision feedback to achieve closed-loop management of flood control decision-making. Specifically, by utilizing the data display and communication functions of the intelligent decision support system, the optimal flood control plan is transformed into specific flood control recommendations. These recommendations are then communicated to relevant personnel and departments in the form of graphics, text, and voice, such as to water conservancy project management departments, maintenance departments, and emergency response departments. Examples of recommendations include adjusting reservoir storage, discharge, scheduling, and water transfer volumes; reinforcing or repairing damaged parts of projects; and evacuating or relocating threatened personnel and property. Furthermore, the recommendations are dynamically adjusted based on changes in the flood situation and decision feedback. For instance, parameters of the flood control plan are adjusted based on real-time data such as rainfall, water level, flow rate, and flood peak; the content of the plan is adjusted based on actual project operation status, abnormal situations, and emergency measures; and the priority of the plan is adjusted based on actual flood control effects, costs, and risks. This achieves closed-loop management of flood control decisions, improving the timeliness, accuracy, and effectiveness of these decisions.

[0102] Step 5.7: In emergency situations, the intelligent decision support system automatically triggers flood control measures to respond to flood disasters with the fastest speed and highest efficiency. Specifically, utilizing the data early warning and data control functions of the intelligent decision support system, in emergency situations, the system automatically triggers flood control measures. For example, when indicators such as water level, flow rate, and pressure are detected to exceed preset thresholds, the system automatically activates floodgates, starts emergency pumping stations, and starts emergency generators to promptly lower water levels, reduce flow rates, and stabilize pressure. Similarly, when abnormal signals or malfunctions are detected in engineering structures, facilities, or equipment, the system automatically activates alarm systems, notification systems, and command systems to promptly report abnormal situations, notify relevant personnel and departments, and instruct emergency measures to be taken, thus responding to flood disasters with the fastest speed and highest efficiency and mitigating or avoiding disaster losses.

[0103] The intelligent decision support system adopts a distributed computing architecture, with a system processing capacity of 100,000 basic calculation operations per second and data processing latency controlled within 1 second. The flood data collection scope covers a drainage area of ​​no less than 500 square kilometers, and the data sources include multiple channels such as meteorological stations, hydrological stations, and satellite remote sensing. The flood situation map is updated every 30 minutes, with a spatial resolution of 1-kilometer grid accuracy. The disaster risk map is drawn using GIS technology and includes basic risk elements such as inundation depth and hazard. The flood control measures map covers key information such as personnel evacuation routes, material reserve points, and emergency shelters, with a layer accuracy of 1:25,000 scale.

[0104] Specifically, the predicted future rainfall uses a combination of numerical weather prediction and statistical forecasting, with a forecast accuracy of over 60% within 24 hours and over 50% within 48 hours; the water level prediction uses a hydrological model, considering major factors such as upstream inflow, rainfall, and human regulation, with a prediction accuracy of ±20cm; the flow prediction uses a distributed hydrological model, with model parameters calibrated based on historical data, and the prediction relative error controlled within 25%; the flood peak index prediction includes three elements: flood peak flow, flood peak water level, and flood peak occurrence time, with the flood peak flow prediction accuracy reaching ±30% and the flood peak occurrence time prediction error controlled within ±4 hours.

[0105] The analysis of the causes of floods employs a multi-factor analysis method, considering 15 major influencing factors across three categories: meteorological factors, underlying surface factors, and anthropogenic factors. The disaster mechanism analysis combines physical mechanisms with statistical analysis to explain the basic processes and statistical patterns of disaster occurrence. Disaster path identification utilizes a dynamic tracking method with a tracking accuracy of 200 meters spatially and 15 minutes temporally. Key node identification is based on empirical analysis, identifying important nodes with significant impact on the flood control system. Vulnerability identification employs a risk assessment method, establishing a risk index encompassing both exposure and sensitivity dimensions. Emergency warnings utilize a threshold analysis method, with a warning lead time of at least one hour.

[0106] The generation of multiple feasible flood control plans employs a multi-objective analysis method, with optimization objectives including two main aspects: reducing economic losses and ensuring personnel safety. The number of plans is determined based on the disaster level, with three plans generated for general disasters and five plans generated for major disasters. Each plan includes a combination of engineering and non-engineering measures. Engineering measures include gate scheduling and pump station start-up and shutdown, while non-engineering measures include early warning issuance and personnel evacuation. Plan evaluation combines expert evaluation and quantitative analysis, with the evaluation index system including three primary indicators (technical feasibility, economic rationality, and implementation operability) and twelve secondary indicators.

[0107] The automatic flood control measures adopt a tiered triggering mechanism, including two levels: early warning triggering and automatic triggering. The triggering conditions are set using a threshold triggering method, based on the critical values ​​of key indicators. The automatic triggering delay time is determined according to the type of measure: 10 minutes for gate operation, 20 minutes for pump station start-up and shutdown, and 1 hour for personnel evacuation. The triggering accuracy requires a false alarm rate of less than 15% and a false alarm rate of less than 20%. The response speed requires that the time from triggering to the completion of the measure does not exceed 150% of the preset time, ensuring that flood control measures can be effectively implemented in emergency situations.

[0108] Preferably, step 6 includes the following steps:

[0109] Step 6.1: Regularly collect and analyze actual operational data, compare and verify it with the digital twin model, and evaluate the deviation and error of the digital twin model. Specifically, through sensors, monitoring equipment, data platforms, etc. related to water conservancy projects, regularly collect and analyze actual operational data, such as water level, flow rate, pressure, temperature, humidity, wind speed, wind direction, rainfall, runoff, water quality, etc., as well as the operational status, abnormal situations, and flood control measures of water conservancy projects, and compare and verify it with the data and status of the digital twin model, evaluate the deviation and error of the digital twin model, such as data consistency, model accuracy, and model effectiveness, and record the evaluation results in the evaluation report;

[0110] Step 6.2 involves improving and optimizing the digital twin model to enhance its accuracy and sensitivity, and increase its functionality and adaptability. Specifically, based on the evaluation results in the evaluation report, the digital twin model is improved and optimized, such as correcting model parameters, adding model details, improving model accuracy, eliminating model errors, and enhancing model adaptability. Simultaneously, based on the actual needs of water conservancy projects and new technological advancements, the digital twin model is improved and innovated, such as introducing new modeling methods, adding new model functions, expanding new model applications, and enhancing model intelligence. The results of these improvements and optimizations are recorded in the improvement report.

[0111] Step 6.3: Utilize the digital twin model for simulation and testing to verify the model's effectiveness and stability, assess its performance and quality, and identify and resolve any problems or deficiencies. Specifically, use the digital twin model to conduct simulations and tests, such as simulating the operation of a water conservancy project under different meteorological and hydrological conditions, including reservoir impoundment, flood discharge, scheduling, and water conveyance, as well as potential abnormal situations like dam failure, breach, overtopping, and scouring. Test the model's effectiveness and stability, such as its response speed, output accuracy, and output stability, and record the simulation and testing results in a simulation test report. Simultaneously, use the simulation and testing results to assess the model's performance and quality, such as its operational efficiency, operational resources, and operational safety, and identify and resolve any problems or deficiencies, such as operational errors, operational lag, and crashes, and record the detection and resolution results in a detection and resolution report.

[0112] Step 6.4: Update and replace the digital twin model in a timely manner to maintain consistency and synchronization between the model and actual operational data, ensuring that the model can accurately reflect and predict the state and behavior of the flood control system. Specifically, based on the improvement and optimization results in the improvement reports and detection and resolution reports, update and replace the digital twin model in a timely manner to keep it in line with the latest technical level and actual needs, and record the update and replacement results in the update and replacement report. Simultaneously, based on the evaluation and test results in the evaluation reports and simulation test reports, update and replace the digital twin model in a timely manner to keep it synchronized with the latest actual operational data and state, and record the update and replacement results in the update and replacement report. By updating and replacing the digital twin model in a timely manner, it is ensured that the model can accurately reflect and predict the state and behavior of the flood control system, providing reliable technical support for flood control work.

[0113] The above series of steps helps ensure the continued effectiveness of the digital twin model, making it a reliable decision support tool that provides accurate and timely information and advice for flood control efforts.

[0114] The regular collection of actual operation data adopts a timed acquisition method, with a collection frequency of once every 2 hours, increasing to once per hour during the flood season; the data collection scope includes three major categories and 30 main indicators: engineering operation data, environmental monitoring data, and equipment status data; data quality control adopts a hierarchical verification mechanism, including two levels: sensor self-verification and data acquisition system verification; deviation assessment uses two statistical indicators: root mean square error and mean absolute error, where root mean square error controlled within 10% is considered good, 10% to 20% is considered average, and exceeding 20% ​​requires model adjustment.

[0115] The improved and optimized digital twin model adopts a regular update mechanism, with a model update cycle of once per quarter, and additional special updates after major operating conditions occur; the accuracy improvement adopts a variety of statistical and machine learning methods, and the model accuracy improvement target is to reduce the prediction error by no less than 10% after each update; the sensitivity analysis adopts the local sensitivity analysis method to identify the main parameters that have a significant impact on the model output; the functional expansion includes adding monitoring items, adding analysis functions, etc., and the system stability test pass rate reaches 95% after each functional expansion.

[0116] The simulation and testing verification adopts a multi-scenario verification method, including two methods: historical flood reproduction verification and typical operating condition simulation verification. The historical flood reproduction verification selects 3 to 5 typical flood events that have occurred in the past 10 years, and the reasonableness of reproduction requires that the error of key indicators be controlled within 20%. The typical operating condition simulation includes standard conditions such as common floods and design floods, and the model stability requires that it can operate normally under typical operating conditions. The performance test includes two aspects: calculation speed and system stability. The calculation speed is required to be more than twice as fast as the actual time, and the continuous operation stability reaches more than 95%.

Claims

1. An intelligent flood control method for water conservancy projects, characterized in that, Includes the following steps: Step 1: Construct a digital twin model of the water conservancy project and create a digital map; Step 2: Real-time acquisition of the project's operational status and meteorological and hydrological data of the area where the water conservancy project is located, and extraction of the changing characteristics of water volume, rainfall, and project status to update the status of the digital twin model; Step 3: Simulate the operation of water conservancy projects to predict flood conditions and disaster development trends; Step 4: Monitor the surrounding environment of water conservancy projects in real time to detect possible abnormalities in a timely manner; Step 5: Conduct real-time analysis and optimize decision-making, propose timely and effective flood control suggestions, and automatically trigger flood control measures when necessary; Step 6: Regularly update and optimize the digital twin model, combining actual operational data and new technological advancements to continuously improve the model's accuracy and reliability, ensuring the flood control system remains in optimal condition.

2. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 1 includes the following steps: Collect structural parameters and location information of water conservancy facilities, including data on the type, scale, function, design, construction and operation of water conservancy projects, as well as the spatial coordinates, elevation and orientation information of water conservancy projects; Using 3D modeling software, a digital twin model of a water conservancy project is constructed based on the structural parameters and location information of the water conservancy facilities. This model includes the main structure, ancillary facilities, and surrounding environment of the water conservancy project. Collect topographic, geomorphological, soil, vegetation and other natural geographical data of the area where the water conservancy project is located in order to create a digital map.

3. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 2 includes the following steps: By utilizing satellite cloud images, remote sensing images, and geographic information data, meteorological and hydrological data of the area where the water conservancy project is located can be obtained in real time, including rainfall, evaporation, temperature, humidity, wind speed, wind direction, cloud cover, sunshine, water level, flow rate, and water quality parameters. By utilizing satellite cloud images, remote sensing images, and geographic information data, the real-time operational status of water conservancy projects can be obtained, including structural changes, switch status, operating modes, and fault alarm information. Based on real-time meteorological and hydrological data and the operational status of the project, the changing characteristics of water volume, rainfall and project conditions are extracted, their impact on water conservancy projects is analyzed, potential problems and risks are predicted, and corresponding countermeasures and optimization plans are formulated. The extracted change characteristics, analysis results, prediction results, countermeasures and optimization schemes are fed back into the digital twin model of the water conservancy project in real time, so as to realize the state update of the digital twin model and maintain its synchronization and twin nature with the physical water conservancy project.

4. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 3 includes the following steps: The extracted characteristics of changes in water output, rainfall, and engineering conditions are input into the digital model of the water conservancy project as a basis for parameter correction, so as to ensure the consistency and accuracy between the digital model and the actual project. Based on the type, function, structure, and operation characteristics of water conservancy projects, appropriate simulation methods and simulation accuracy are selected to simulate the operation process of water conservancy projects, including simulations of water flow, sediment, water quality, and structure, and to calculate the operation status and output results of water conservancy projects. Based on the simulation results, the flood situation and disaster development trend are predicted, and the safety, effectiveness and sustainability of water conservancy projects are assessed, providing scientific basis and suggestions for the decision-making and management of water conservancy projects.

5. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 4 includes the following steps: Based on the type, scale, location, and functional characteristics of the water conservancy project, determine the monitoring content, indicators, frequency, scope, and requirements; In accordance with technical specifications and operating procedures, the monitoring equipment was installed in key parts of the water conservancy project; The data collected by the monitoring equipment is transmitted to the monitoring center for storage, organization, analysis, and evaluation. By comparing the differences between monitoring data and normal data, abnormal changes in the surrounding environment of water conservancy projects were found, including increased seepage flow, abnormal water level, water pollution, surface subsidence, and deformation of support structures. Report the type, location, extent, cause, and impact of any abnormal situation to the relevant parties in a timely manner, and take appropriate emergency measures to prevent the situation from escalating or worsening.

6. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 4 includes the following steps: Establish an intelligent decision support system to collect, process, analyze, and display flood data, generating flood situation maps, disaster risk maps, and flood control measure maps; Based on the predicted flood situation, forecast rainfall, water level, flow rate and flood peak indicators for a period of time in the future, and assess the scale, scope, degree and impact of possible flood disasters; Based on the development trend of disasters, analyze the causes, mechanisms, paths and consequences of floods, identify key nodes, weak links and emergencies of disasters, and predict the evolution process and possible turning points of disasters; In light of abnormal situations, monitor the operational status of flood control projects, facilities, and equipment; identify and analyze abnormal signals, causes of malfunctions, scope of impact, and degree of harm; promptly issue alarms and propose emergency measures. Real-time analysis and optimization decisions are performed, taking into account flood control objectives, constraints, resource allocation and risk assessment factors, generating multiple feasible flood control plans, and selecting the optimal plan based on different decision criteria and preferences; The optimal flood control plan is transformed into specific flood control recommendations, and these recommendations are dynamically adjusted based on changes in the flood situation and decision-making feedback, thereby achieving closed-loop management of flood control decision-making. In emergency situations, the intelligent decision support system automatically triggers flood control measures to respond to floods with the fastest speed and highest efficiency.

7. The intelligent flood control method for water conservancy projects according to claim 1, characterized in that, Step 6 includes the following steps: Regularly collect and analyze actual operational data, compare and verify it with the digital twin model, and evaluate the deviation and error of the digital twin model; Improve and optimize the digital twin model to enhance its accuracy and sensitivity, and increase its functionality and adaptability; Using digital twin models for simulation and testing, we can verify the effectiveness and stability of the model, detect its performance and quality, and identify and resolve its problems and defects. Update and replace digital twin models in a timely manner to maintain consistency and synchronization between the models and actual operational data, ensuring that the models can accurately reflect and predict the status and behavior of the flood control system.

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