Water conservancy project digital management method based on BIM model

Abstract

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to a digital management method for water conservancy projects based on a BIM model. Background Art

[0002] Traditional water conservancy project management mainly relies on two-dimensional drawings, manual experience and simple hydrological models, and there are problems such as data dispersion, information islands, and insufficient analytical capabilities. With the development of BIM (Building Information Modeling) technology, its advantages such as three-dimensional visualization, information integration, and collaborative management are gradually being applied to the field of water conservancy projects. The existing digital management methods of water conservancy projects based on BIM models are mainly reflected in the following aspects, such as BIM and GIS integration, BIM and hydraulic model integration, and BIM and database integration.

[0003] Although the existing methods have improved the management level of water conservancy projects to a certain extent, they still have the following disadvantages in risk prediction and early warning:

[0004] 1. Lack of real-time performance: Existing methods are mostly based on static data analysis, which makes it difficult to reflect the dynamic changes of water conservancy projects and their surrounding environment in real time, such as real-time water level, rainfall, terrain deformation, etc., making it impossible to provide timely risk warnings.

[0005] 2. Insufficient prediction accuracy: Due to the lack of effective fusion and in-depth analysis of multi-source data, the risk prediction accuracy of existing methods is limited, making it difficult to accurately assess risk levels and potential impacts.

[0006] 3. Imperfect early warning mechanism: The early warning threshold is often set statically and lacks a dynamic adjustment mechanism, making it difficult to adapt to complex and changing actual situations, resulting in insufficient accuracy and timeliness of the early warning.

[0007] 4. Lack of linkage with emergency response: Risk prediction and early warning results are difficult to effectively link with emergency plans and emergency response measures, limiting the practical application value of early warning information. Summary of the invention

[0008] Based on this, it is necessary to provide a digital management method for water conservancy projects based on the BIM model to solve at least one of the above technical problems.

[0009] To achieve the above purpose, a digital management method for water conservancy projects based on BIM model includes the following steps:

[0010] Step S1: Acquire real-time monitoring data; collect high-precision terrain data in the water conservancy project area, and perform data preprocessing to obtain preprocessed data; perform hydrological information integrated modeling using the BIM platform based on the preprocessed data to obtain a hydrological information integrated model; perform multi-dimensional terrain dynamic updating on the hydrological information integrated model using real-time monitoring data to obtain digital terrain data;

[0011] Step S2: performing flood simulation calculation based on digital terrain data to obtain flood simulation calculation results; performing flood scenario simulation based on the flood simulation calculation results to obtain flood simulation data;

[0012] Step S3: Integrate infrastructure models according to flood simulation data to obtain infrastructure models; analyze the potential impact of floods on different infrastructures according to the infrastructure models and flood simulation data to obtain flood impact analysis data; classify infrastructure risk levels according to the flood impact analysis data to obtain risk level classification data; determine early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas; set dynamic thresholds according to early warning indicators for different risk areas to obtain early warning dynamic thresholds; construct early warning models according to early warning dynamic thresholds to obtain early warning models; use early warning models to perform flood early warning processing on real-time monitoring data to obtain flood early warning information data; generate risk assessment reports according to flood impact analysis data, risk level classification data, and flood early warning information data to obtain risk assessment reports;

[0013] Step S4: Optimize the engineering design according to the risk assessment report to obtain engineering design optimization data; adjust emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters; perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model.

[0014] The present invention realizes the comprehensive digitization and real-time topography and hydrological information of the water conservancy project area through high-precision terrain data acquisition, multi-source data preprocessing and integrated modeling of hydrological information on the BIM platform, and dynamically updates in combination with real-time monitoring data, and provides high-precision and reliable digital terrain data for subsequent flood simulation and risk assessment. Flood simulation calculation and scenario simulation based on digital terrain data can accurately predict the evolution process and impact range of floods in different scenarios, provide a scientific basis for the formulation of flood control measures and emergency plans, and effectively improve the accuracy of flood risk prediction and assessment. Through infrastructure model integration, flood impact analysis, risk level classification, early warning indicator determination, dynamic threshold setting and early warning model construction, a comprehensive assessment and early warning of flood risks are achieved, providing effective technical means for the safety management of water conservancy projects and minimizing flood losses. Through risk assessment reports, engineering design optimization and emergency parameter adjustment are carried out, and flood early warning and emergency response operations are carried out in combination with early warning models, which realizes real-time monitoring, accurate prediction and timely early warning of water conservancy project risks, forms a closed-loop management, continuously improves and optimizes the flood control capacity of water conservancy projects, and ensures the safe operation of water conservancy projects. Therefore, the present invention provides a digital management method for water conservancy projects based on the BIM model, which effectively solves the deficiencies of existing methods in risk prediction and early warning capabilities, realizes real-time monitoring, accurate prediction and timely early warning of water conservancy project risks, and provides more effective technical means for flood prevention and disaster reduction and water conservancy project safety management.

[0015] Preferably, step S1 comprises the following steps:

[0016] Step S11: collect high-precision terrain data in the water conservancy project area to obtain high-precision terrain data; obtain historical flood information data, long-term meteorological data and river hydrological data;

[0017] Step S12: preprocessing the high-precision terrain data, historical flood information data, long-term meteorological data, and river hydrological data to obtain preprocessed data;

[0018] Step S13: constructing a three-dimensional terrain model using the BIM platform according to the preprocessed data to obtain a three-dimensional terrain model;

[0019] Step S14: integrating hydrological information of the three-dimensional terrain model according to the preprocessed data to obtain a hydrological information integration model;

[0020] Step S15: Acquire real-time monitoring data; use the real-time monitoring data to dynamically update the multi-dimensional terrain of the hydrological information integration model to obtain digital terrain data.

[0021] The present invention realizes the comprehensive digital collection of the terrain and hydrological historical information of the water conservancy project area by carrying a laser radar on an unmanned aerial vehicle and obtaining historical data, providing a high-precision and reliable data foundation for subsequent BIM modeling and analysis, and avoiding the problems of low efficiency and large errors in traditional manual measurement methods. By preprocessing multi-source data, including point cloud data denoising, classification, filtering, and cleaning, interpolation and format conversion of other data, the data quality and consistency are guaranteed, the accuracy and reliability of subsequent models are improved, and a solid data foundation is laid for subsequent hydrological information integration and flood simulation. The preprocessed data is used to build a three-dimensional terrain model on the BIM platform, and the two-dimensional terrain data is converted into an intuitive three-dimensional visualization model, which more clearly shows the terrain and geomorphic characteristics of the water conservancy project area, facilitates subsequent hydrological information integration and flood simulation analysis, and improves the efficiency and accuracy of engineering design. The preprocessed historical flood, meteorological and hydrological data are integrated into the three-dimensional terrain model, and a hydrological information integration model containing rich hydrological information is constructed, which realizes the effective fusion of terrain data and hydrological information, provides more comprehensive data support for flood simulation and risk assessment, and can more accurately reflect the actual situation of water conservancy projects. The real-time monitoring data is used to dynamically update the hydrological information integration model, so that the digital terrain data can reflect the real-time status of water conservancy projects, such as changes in water level, flow, sediment content, etc., which improves the accuracy and real-time performance of the model, provides more timely and reliable data support for flood warning and emergency response, and enhances the ability to respond to emergencies.

[0022] Preferably, step S15 comprises the following steps:

[0023] Step S151: performing multi-source data integration on the hydrological information integration model and the real-time monitoring data to obtain multi-source integrated data;

[0024] Step S152: performing data fusion and assimilation on the multi-source integrated data to obtain data fusion and assimilation results;

[0025] Step S153: constructing a sediment transport model according to the data fusion and assimilation results to obtain a sediment transport model;

[0026] Step S154: acquiring historical observation data; calibrating the model parameters of the sediment transport model using the historical observation data to obtain a calibrated model;

[0027] Step S155: Calculate terrain deformation based on the calibrated model and the data fusion and assimilation results to obtain terrain deformation simulation data;

[0028] Step S156: applying the deformation amount of the terrain deformation simulation data to the three-dimensional terrain model to update the three-dimensional terrain model to obtain a dynamically updated three-dimensional terrain model; generating digital terrain data according to the dynamically updated three-dimensional terrain model to obtain digital terrain data.

[0029] The present invention integrates the hydrological information integration model and real-time monitoring data into multi-source data, builds a unified data platform, integrates data from different sources and in different formats, eliminates data islands, and provides a basis for subsequent data fusion and assimilation, so that the operation status of water conservancy projects can be analyzed more comprehensively. Data fusion and assimilation are performed on multi-source integrated data, and algorithms such as Kalman filtering are used to effectively combine the advantages of model data and observation data, improve the accuracy and reliability of data, reduce the influence of measurement errors and noise, and provide more accurate input data for the construction of sediment transport models. The sediment transport model is constructed based on the results of data fusion and assimilation, which can more accurately simulate the transport and deposition laws of sediment under the action of water flow, provide a scientific basis for predicting changes in riverbed scouring and silting, help to more accurately predict terrain deformation, and improve the prediction accuracy of the model. The parameters of the sediment transport model are calibrated using historical observation data, so that the model parameters are more in line with the actual situation, the simulation accuracy and reliability of the model are improved, the accuracy of subsequent terrain deformation calculations is ensured, and the model can better reflect the actual situation. The terrain deformation calculation based on the calibrated sediment transport model and the data fusion and assimilation results can more accurately predict the terrain deformation caused by riverbed scouring and silting changes, providing an important basis for the safe operation and risk assessment of water conservancy projects, and helping to more effectively manage risks and make decisions. The terrain deformation simulation data is applied to the update of the three-dimensional terrain model, realizing the dynamic update of the terrain model, enabling the model to reflect the latest terrain changes, improving the accuracy and real-time performance of the model, providing more reliable basic data for flood simulation and risk assessment, and ultimately generating digital terrain data that is closer to reality.

[0030] Preferably, step S2 comprises the following steps:

[0031] Step S21: constructing a hydraulic model based on the digital terrain data to obtain a hydraulic model;

[0032] Step S22: setting meteorological scenario parameters according to the hydraulic model to obtain meteorological scenario parameters;

[0033] Step S23: performing flood simulation calculation according to the hydraulic model and meteorological scenario parameters to obtain flood simulation calculation results;

[0034] Step S24: Visualizing the flood simulation calculation results to obtain flood propagation visualization results;

[0035] Step S25: Perform multi-scenario simulation on the flood propagation visualization results, and conduct comparative analysis of the simulation data to obtain flood simulation data.

[0036] The present invention realizes the accurate expression of key parameters such as river channel geometry and riverbed roughness by constructing a hydraulic model based on digital terrain data, provides a reliable basis for flood simulation calculation, and makes the simulation results closer to the actual situation. According to the hydraulic model, meteorological scenario parameters are set to simulate the flood evolution process under various scenarios such as different recurrence periods, different rainfall intensities and initial water levels, which provides conditions for comprehensive assessment of flood risks and makes risk assessment more scientific and comprehensive. According to the hydraulic model and meteorological scenario parameters, flood simulation calculation can predict the evolution process and impact range of floods under different scenarios, provide a scientific basis for formulating flood control measures and emergency plans, and help to better deal with flood disasters. The flood simulation calculation results are visualized, and the complex numerical simulation results are converted into intuitive graphics and animations, which is convenient for understanding and analyzing the propagation law and impact range of floods, improves communication efficiency and decision-making level, and enables relevant personnel to understand flood risks more intuitively. Multi-scenario simulation and comparative analysis can more comprehensively evaluate the potential risks of floods under different scenarios, provide decision support for formulating more scientific and reasonable flood control and disaster reduction measures, and help optimize the design of water conservancy projects and improve flood control capabilities.

[0037] Preferably, step S3 comprises the following steps:

[0038] Step S31: collecting infrastructure data according to the water conservancy project area to obtain infrastructure data; integrating the infrastructure data and flood simulation data into an infrastructure model to obtain an infrastructure model;

[0039] Step S32: Analyze the potential impact of floods on different infrastructures based on the infrastructure model and flood simulation data to obtain flood impact analysis data;

[0040] Step S33: classifying infrastructure risk levels in different regions and types according to the flood impact analysis data and infrastructure data to obtain risk level classification data;

[0041] Step S34: determining early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas;

[0042] Step S35: setting dynamic thresholds according to historical flood information data and early warning indicators of different risk areas to obtain early warning dynamic thresholds;

[0043] Step S36: formulate warning rules according to the warning dynamic threshold to obtain warning rules; construct a warning model according to the warning rules and a preset warning information database to obtain a warning model;

[0044] Step S37: using the early warning model to perform flood early warning processing on the real-time monitoring data to obtain flood early warning information data;

[0045] Step S38: Generate a risk assessment report based on the flood impact analysis data, risk level classification data, and flood warning information data to obtain a risk assessment report.

[0046] The present invention collects infrastructure data and integrates it with flood simulation data to construct a three-dimensional model containing infrastructure information, which provides a basis for flood impact analysis and risk assessment, making the analysis and assessment more targeted and accurate. By analyzing the infrastructure model and flood simulation data, the infrastructure affected by the flood can be accurately identified, and the potential degree of damage can be assessed, providing a basis for risk level division and emergency plan formulation, which is helpful to reduce flood losses. According to the flood impact analysis data and infrastructure data, risk level division can be performed, and limited resources can be used to protect high-risk areas first, which improves the efficiency of flood prevention and disaster reduction and makes resource allocation more reasonable and effective. According to the risk level division data, the early warning indicators of different risk areas are determined, and the differentiated setting of early warning indicators is realized, which improves the accuracy and effectiveness of early warning. According to the historical flood information data and the early warning indicators of different risk areas, the dynamic threshold is set, so that the early warning threshold can be adjusted according to the actual situation, the sensitivity and accuracy of the early warning are improved, and floods of different scales can be more effectively dealt with. According to the early warning dynamic threshold, the early warning rules are formulated and the early warning model is constructed, which realizes the automation and intelligence of the early warning, improves the efficiency and reliability of the early warning, and reduces the interference of human factors. Using early warning models to process flood warnings from real-time monitoring data can issue early warning information in a timely manner, provide early warnings to relevant departments and personnel, gain valuable emergency response time, and minimize flood losses. By integrating flood impact analysis data, risk level classification data, and early warning information to generate risk assessment reports, it provides a comprehensive and scientific basis for risk management and decision-making in water conservancy projects, and helps improve the overall level of flood prevention and disaster reduction.

[0047] Preferably, step S32 includes the following steps:

[0048] Step S321: extracting the flood-inundated area from the flood simulation data to obtain flood-inundated area data;

[0049] Step S322: performing infrastructure collision detection on the flood inundation area data and the infrastructure model to obtain infrastructure collision detection data;

[0050] Step S323: Identify the affected infrastructure according to the infrastructure collision detection data to obtain the affected infrastructure data;

[0051] Step S324: Assess the extent of infrastructure damage based on the affected infrastructure data and the flood simulation data to obtain infrastructure damage extent data;

[0052] Step S325: Generate flood impact analysis data based on the infrastructure damage degree data to obtain flood impact analysis data.

[0053] The present invention can clearly define the scope of flood impact by extracting flood-inundated areas from flood simulation data, provide a spatial basis for subsequent collision detection and impact analysis, and make the analysis results more targeted. By performing collision detection on flood-inundated area data and infrastructure models, infrastructure located in the flood-inundated area can be quickly and accurately identified, providing precise target objects for subsequent impact analysis, and improving analysis efficiency and accuracy. The affected infrastructure is identified based on the collision detection data, the object of flood impact analysis is clarified, basic data is provided for subsequent damage assessment, and the assessment results are more reliable. By assessing the degree of damage to the affected infrastructure, the damage caused by the flood to the infrastructure can be quantified, providing key data for loss assessment and risk management, and helping to more effectively carry out post-disaster reconstruction and recovery. By integrating infrastructure damage degree data and generating flood impact analysis data, comprehensive flood impact information is provided, which provides an important basis for risk level classification, warning threshold setting, and risk assessment report generation, and helps to fully understand the impact of flood disasters.

[0054] Preferably, step S324 is specifically as follows:

[0055] Extracting infrastructure attribute information based on the affected infrastructure data and the infrastructure model to obtain infrastructure attribute information data;

[0056] Calculate flood action force based on flood simulation data and infrastructure attribute information data to obtain flood action force data;

[0057] The damage degree is calculated based on the flood force data and the infrastructure attribute information data to obtain the damage degree data;

[0058] Quantify the loss value according to the damage degree data to obtain the loss value quantification data;

[0059] Use loss value quantification data and infrastructure models to visualize damage results and obtain infrastructure damage maps;

[0060] The infrastructure damage degree data is generated according to the infrastructure damage map to obtain the infrastructure damage degree data.

[0061] By extracting infrastructure attribute information, the present invention can obtain key information such as building height, material, and flood resistance level, provide necessary parameters for flood force calculation and damage degree assessment, and make the assessment result more in line with the actual situation. Calculating the flood force can quantify the hydrostatic pressure and dynamic pressure exerted by the flood on the infrastructure, provide important mechanical parameters for damage degree calculation, and make the damage degree calculation more scientific and accurate. Calculating the damage degree according to the flood force and infrastructure attribute information can more accurately assess the degree of damage caused by the flood to the infrastructure, provide a more reliable basis for loss assessment, and improve the accuracy of the assessment. Quantifying the loss value can convert the damage degree into specific economic losses, provide a reference for post-disaster reconstruction and recovery, and help to better allocate resources and assess losses. Visualizing the damage results can display complex damage information in an intuitive graphical manner, which is convenient for understanding and analyzing the losses caused by the flood, and improves communication efficiency and decision-making level. Generate infrastructure damage degree data, convert the visualized damage results into structured data, facilitate subsequent analysis and application, and provide data support for risk assessment and decision support.

[0062] Preferably, step S34 includes the following steps:

[0063] Step S341: extracting risk areas according to the risk level classification data to obtain risk area classification data;

[0064] Step S342: Identify potential disaster factors according to the risk area division data to obtain potential disaster factors;

[0065] Step S343: screening the risk area division data according to potential disaster factors to obtain analysis and screening indicators;

[0066] Step S344: performing indicator correlation analysis on the analysis and screening indicators to obtain indicator correlation data;

[0067] Step S345: Determine the indicator weights of different risk areas according to the analysis and screening indicators and indicator correlation data to obtain indicator weight values;

[0068] Step S346: Generate early warning indicators for different risk areas according to the indicator weight values ​​and the analysis and screening indicators to obtain early warning indicators for different risk areas.

[0069] The present invention clarifies the spatial distribution of different risk levels by extracting risk areas, providing a spatial basis for subsequent identification of disaster factors and screening of indicators, making subsequent analysis more targeted. Identifying potential disaster factors can clarify the main factors that lead to floods in different risk areas, provide direction for indicator screening, and make the screened indicators more representative and effective. According to the screening indicators of potential disaster factors, indicators closely related to disaster factors can be selected to improve the accuracy and effectiveness of early warnings and avoid interference from irrelevant indicators. Analyzing the correlation of indicators can understand the relationship between different indicators, provide a basis for determining indicator weights, and make weight allocation more scientific and reasonable. Determining indicator weights can distinguish the importance of different indicators, so that the early warning model can better reflect the actual situation and improve the accuracy of early warnings. Early warning indicators for different risk areas are generated to achieve differentiated settings of early warning indicators, making early warnings more targeted and effective, and improving the overall performance of the early warning system.

[0070] Preferably, step S35 includes the following steps:

[0071] Step S351: performing historical flood analysis on the historical flood information data to obtain historical flood analysis data; performing flood feature extraction on the historical flood analysis data to obtain flood feature data;

[0072] Step S352: constructing a correlation model between rainfall and water level based on historical flood analysis data and flood characteristic data to obtain a rainfall-water level relationship model;

[0073] Step S353: extracting data of regions with different risk levels from the risk level classification data to obtain data of regions with different risk levels;

[0074] Step S354: setting an initial threshold value according to flood characteristic data, early warning indicators of different risk areas, and data of areas with different risk levels to obtain an initial regional threshold value;

[0075] Step S355: Obtain weather forecast data; according to the weather forecast data and real-time monitoring data, use the rainfall-water level relationship model to dynamically adjust the regional initial threshold to obtain the early warning dynamic threshold.

[0076] The present invention can understand the occurrence law and characteristics of historical floods by analyzing and extracting features from historical flood information data, and provide basic data for the construction of a rainfall-water level relationship model and the setting of an initial threshold, so that the model and the threshold are more in line with the actual situation. By constructing a rainfall-water level relationship model, the water level change trend can be predicted according to the rainfall, and a prediction basis can be provided for dynamic threshold adjustment, thereby improving the prediction ability of early warning. Data of different risk level areas are extracted to provide a data basis for setting different initial thresholds for different risk level areas, so that the threshold setting is more targeted. According to flood characteristic data, early warning indicators of different risk areas, and data of different risk level areas, an initial threshold can be set for different risk level areas. A reasonable initial early warning threshold can be set, providing a basis for subsequent dynamic adjustment and ensuring the effectiveness of early warning. According to meteorological forecast data and real-time monitoring data, the threshold is dynamically adjusted using a rainfall-water level relationship model, so that the early warning threshold can change according to the real-time situation, thereby improving the accuracy and sensitivity of the early warning, and being able to more effectively respond to sudden flood events.

[0077] Preferably, step S4 comprises the following steps:

[0078] Step S41: Identify weak links in the water conservancy project design according to the risk assessment report to obtain weak link data;

[0079] Step S42: Formulate an optimization strategy based on the weak link data to obtain an engineering optimization strategy;

[0080] Step S43: Optimizing the engineering design according to the engineering optimization strategy to obtain engineering design optimization data;

[0081] Step S44: adjusting emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters;

[0082] Step S45: Perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model.

[0083] By identifying the weak links in the design of water conservancy projects, the present invention can find out the deficiencies in the engineering design, provide direction for the subsequent formulation of optimization strategies, and help improve the flood control capacity of the project. By formulating optimization strategies, specific improvement measures can be proposed for the weak links, providing guidance for engineering design optimization and ensuring the effectiveness of the optimization work. Engineering design optimization can improve the engineering design, improve the flood control capacity and safety of the project, and reduce the risk of floods. Adjusting emergency parameters according to engineering design optimization data and risk assessment reports can make the emergency plan consistent with the actual situation and improve the efficiency of emergency response. Carrying out flood warning and emergency response operations according to emergency adjustment parameters and early warning models can achieve timely and effective flood warning and emergency response, and minimize flood losses.

[0084] The present invention realizes the comprehensive digitization and real-time topography and hydrological information of the water conservancy project area through high-precision terrain data acquisition, multi-source data preprocessing and integrated modeling of hydrological information on the BIM platform, and dynamically updates in combination with real-time monitoring data, and provides high-precision and reliable digital terrain data for subsequent flood simulation and risk assessment. Flood simulation calculation and scenario simulation based on digital terrain data can accurately predict the evolution process and impact range of floods in different scenarios, provide a scientific basis for the formulation of flood control measures and emergency plans, and effectively improve the accuracy of flood risk prediction and assessment. Through infrastructure model integration, flood impact analysis, risk level classification, early warning indicator determination, dynamic threshold setting and early warning model construction, a comprehensive assessment and early warning of flood risks are achieved, providing effective technical means for the safety management of water conservancy projects and minimizing flood losses. Through risk assessment reports, engineering design optimization and emergency parameter adjustment are carried out, and flood early warning and emergency response operations are carried out in combination with early warning models, which realizes real-time monitoring, accurate prediction and timely early warning of water conservancy project risks, forms a closed-loop management, continuously improves and optimizes the flood control capacity of water conservancy projects, and ensures the safe operation of water conservancy projects. Therefore, the present invention provides a digital management method for water conservancy projects based on the BIM model, which effectively solves the deficiencies of existing methods in risk prediction and early warning capabilities, realizes real-time monitoring, accurate prediction and timely early warning of water conservancy project risks, and provides more effective technical means for flood prevention and disaster reduction and water conservancy project safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a schematic diagram of the steps of a digital management method for water conservancy projects based on a BIM model;

[0086] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0087] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0088] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0089] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0090] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0091] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0092] To achieve this, please refer to Figures 1 to 3 , a digital management method for water conservancy projects based on BIM model, comprising the following steps:

[0093] Step S1: Acquire real-time monitoring data; collect high-precision terrain data in the water conservancy project area, and perform data preprocessing to obtain preprocessed data; perform hydrological information integrated modeling using the BIM platform based on the preprocessed data to obtain a hydrological information integrated model; perform multi-dimensional terrain dynamic updating on the hydrological information integrated model using real-time monitoring data to obtain digital terrain data;

[0094] Step S2: performing flood simulation calculation based on digital terrain data to obtain flood simulation calculation results; performing flood scenario simulation based on the flood simulation calculation results to obtain flood simulation data;

[0095] Step S3: Integrate infrastructure models according to flood simulation data to obtain infrastructure models; analyze the potential impact of floods on different infrastructures according to the infrastructure models and flood simulation data to obtain flood impact analysis data; classify infrastructure risk levels according to the flood impact analysis data to obtain risk level classification data; determine early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas; set dynamic thresholds according to early warning indicators for different risk areas to obtain early warning dynamic thresholds; construct early warning models according to early warning dynamic thresholds to obtain early warning models; use early warning models to perform flood early warning processing on real-time monitoring data to obtain flood early warning information data; generate risk assessment reports according to flood impact analysis data, risk level classification data, and flood early warning information data to obtain risk assessment reports;

[0096] Step S4: Optimize the engineering design according to the risk assessment report to obtain engineering design optimization data; adjust emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters; perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model.

[0097] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the digital management method of water conservancy projects based on the BIM model of the present invention. In this example, the digital management method of water conservancy projects based on the BIM model includes the following steps:

[0098] Step S1: Acquire real-time monitoring data; collect high-precision terrain data in the water conservancy project area, and perform data preprocessing to obtain preprocessed data; perform hydrological information integrated modeling using the BIM platform based on the preprocessed data to obtain a hydrological information integrated model; perform multi-dimensional terrain dynamic updating on the hydrological information integrated model using real-time monitoring data to obtain digital terrain data;

[0099] In an embodiment of the present invention, real-time monitoring data is first obtained, and high-precision terrain data is collected for the water conservancy project area, including using an unmanned aerial vehicle equipped with a laser radar to obtain high-precision terrain data, and collecting historical flood, meteorological and hydrological data. These data are then preprocessed, such as point cloud data denoising, classification, filtering, and cleaning, interpolation and format conversion of other data. Next, using the preprocessed data, a three-dimensional terrain model is constructed on a BIM platform (such as Revit), and historical flood, meteorological and hydrological information is integrated to create a hydrological information integration model. Finally, using real-time monitoring data, such as water level, flow, rainfall, etc., the hydrological information integration model is dynamically updated to generate digital terrain data containing real-time information, such as adjusting the river model according to the real-time water level, and simulating riverbed terrain changes according to silt scouring and silting.

[0100] Step S2: performing flood simulation calculation based on digital terrain data to obtain flood simulation calculation results; performing flood scenario simulation based on the flood simulation calculation results to obtain flood simulation data;

[0101] In an embodiment of the present invention, a hydraulic model is constructed in the HEC-RAS software based on the digital terrain data generated by S1, and parameters such as the river section and riverbed roughness are defined, and boundary conditions are set. Then, various meteorological scenario parameters are set according to historical data and different recurrence periods, such as different rainfall intensities and initial water levels. Flood simulation calculations are performed using the hydraulic model and meteorological scenario parameters to obtain data such as water level, flow, and flow velocity at different times. The simulation results are imported into the GIS platform for visualization to display the flood inundation range and water depth distribution. Finally, the simulation results under different scenarios are compared and analyzed, such as the inundation range and peak flow of floods with different recurrence periods, to generate the final flood simulation data.

[0102] Step S3: Integrate infrastructure models according to flood simulation data to obtain infrastructure models; analyze the potential impact of floods on different infrastructures according to the infrastructure models and flood simulation data to obtain flood impact analysis data; classify infrastructure risk levels according to the flood impact analysis data to obtain risk level classification data; determine early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas; set dynamic thresholds according to early warning indicators for different risk areas to obtain early warning dynamic thresholds; construct early warning models according to early warning dynamic thresholds to obtain early warning models; use early warning models to perform flood early warning processing on real-time monitoring data to obtain flood early warning information data; generate risk assessment reports according to flood impact analysis data, risk level classification data, and flood early warning information data to obtain risk assessment reports;

[0103] In an embodiment of the present invention, first, the infrastructure data in the water conservancy project area, including information such as location, type, and elevation, are collected, and these data are integrated with the flood simulation data into the BIM platform to create a three-dimensional model containing infrastructure information. Then, the collision detection function of the BIM platform is used to analyze the potential impact of floods on different infrastructures, such as flooding depth and potential damage. According to the impact analysis results and the importance of the infrastructure, risk levels are divided (high, medium, and low). Next, according to the risk level and potential disaster factors, the early warning indicators of different risk areas are determined, and the early warning dynamic threshold is set in combination with historical flood data and real-time monitoring data. Early warning rules are formulated according to the early warning dynamic threshold, and an early warning model is constructed in combination with the early warning information library. The early warning model is used to perform flood early warning processing on the real-time monitoring data, and early warning information is generated. Finally, the flood impact analysis data, risk level classification data, and early warning information are integrated to generate a risk assessment report.

[0104] Step S4: Optimize the engineering design according to the risk assessment report to obtain engineering design optimization data; adjust the emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters; perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model;

[0105] In an embodiment of the present invention, based on the risk assessment report, the weak links in the design of the water conservancy project are identified, such as insufficient dam height or unreasonable drainage system design. For each weak link, a corresponding optimization strategy is formulated, such as raising the dam or improving the drainage system design. Then, the engineering design is modified according to the optimization strategy in the BIM platform to generate engineering design optimization data. According to the optimized design and risk assessment report, the emergency parameters, such as the warning threshold and the drainage scheduling plan, are adjusted. Finally, the adjusted emergency parameters and warning model are used to perform flood warning and emergency response operations, such as initiating emergency plans and performing corresponding operations based on real-time monitoring data.

[0106] Preferably, step S1 comprises the following steps:

[0107] Step S11: collect high-precision terrain data in the water conservancy project area to obtain high-precision terrain data; obtain historical flood information data, long-term meteorological data and river hydrological data;

[0108] Step S12: preprocessing the high-precision terrain data, historical flood information data, long-term meteorological data, and river hydrological data to obtain preprocessed data;

[0109] Step S13: constructing a three-dimensional terrain model using the BIM platform according to the preprocessed data to obtain a three-dimensional terrain model;

[0110] Step S14: integrating hydrological information of the three-dimensional terrain model according to the preprocessed data to obtain a hydrological information integration model;

[0111] Step S15: Acquire real-time monitoring data; use the real-time monitoring data to dynamically update the multi-dimensional terrain of the hydrological information integration model to obtain digital terrain data.

[0112] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0113] Step S11: collect high-precision terrain data in the water conservancy project area to obtain high-precision terrain data; obtain historical flood information data, long-term meteorological data and river hydrological data;

[0114] In the embodiment of the present invention, a laser radar scanner is used to collect high-precision terrain data of the water conservancy project area by using a drone, the flight altitude is set to 50 meters, the flight zone overlap rate is set to 80%, and high-precision terrain data with a point cloud density of not less than 10 points / square meter is obtained, and it is saved in LAS format. At the same time, historical flood information data for the past 50 years is obtained from the database of the water conservancy project management unit, including the time of flood occurrence, peak flow, flood inundation range, etc.; long-term meteorological data for the past 50 years is obtained from the National Meteorological Center, including daily rainfall, temperature, evaporation, etc.; river hydrological data for the past 50 years is obtained from the hydrological station, including daily water level, flow, flow rate, etc., and all data is stored in the project database.

[0115] Step S12: preprocessing the high-precision terrain data, historical flood information data, long-term meteorological data, and river hydrological data to obtain preprocessed data;

[0116] In the embodiment of the present invention, point cloud processing software is used to perform pre-processing operations such as denoising, classification, and filtering on the acquired high-precision terrain data in LAS format to generate DSM (digital surface model) and DEM (digital elevation model). Missing values ​​are filled and data format conversion is performed on historical flood information data; quality control and data cleaning are performed on long-term meteorological data to remove abnormal values; interpolation processing is performed on river hydrological data to ensure the temporal continuity of the data, and all pre-processed data are saved in a unified format and stored in the project database for subsequent analysis and use.

[0117] Step S13: constructing a three-dimensional terrain model using the BIM platform according to the preprocessed data to obtain a three-dimensional terrain model;

[0118] In the embodiment of the present invention, a three-dimensional terrain model is constructed using a BIM platform such as Revit based on the pre-processed DSM and DEM data. The processed DSM data is imported to generate a surface model, and then the terrain details are adjusted using the DEM data to ensure that the model accuracy is consistent with the actual terrain. At the same time, information such as buildings, roads, and vegetation related to the water conservancy project is imported into the BIM platform to construct a complete three-dimensional scene.

[0119] Step S14: integrating hydrological information of the three-dimensional terrain model according to the preprocessed data to obtain a hydrological information integration model;

[0120] In the embodiment of the present invention, the pre-processed historical flood information data, long-term meteorological data and river hydrological data are imported into the BIM platform and integrated with the three-dimensional terrain model constructed in step S13. The database function of the BIM platform is used to associate different types of hydrological information with corresponding geographical locations. For example, the historical flood inundation range data is superimposed on the three-dimensional terrain model to intuitively display the impact range of historical floods; the location of the hydrological station and the corresponding water level, flow, flow rate and other data are associated with the three-dimensional terrain model to query and display the hydrological information in the model. Finally, a three-dimensional integrated model containing rich hydrological information is generated.

[0121] Step S15: acquiring real-time monitoring data; using the real-time monitoring data to dynamically update the multi-dimensional terrain of the hydrological information integration model to obtain digital terrain data;

[0122] In the embodiment of the present invention, a sensor network, such as a water level meter, a flow meter, a rain gauge, etc., is deployed in the water conservancy project area to collect monitoring data such as water level, flow, and rainfall in real time. The real-time monitoring data is transmitted to the BIM platform via a wireless network, and the hydrological information integration model constructed in step S14 is dynamically updated in multiple dimensions using the data interface and programming functions of the platform. For example, the water level of the river in the model is updated according to the real-time water level data, the surface runoff is simulated according to the real-time rainfall data, and the riverbed terrain is dynamically adjusted according to the sediment scouring and silting conditions, and finally digital terrain data containing real-time information is generated.

[0123] Preferably, step S15 comprises the following steps:

[0124] Step S151: performing multi-source data integration on the hydrological information integration model and the real-time monitoring data to obtain multi-source integrated data;

[0125] Step S152: performing data fusion and assimilation on the multi-source integrated data to obtain data fusion and assimilation results;

[0126] Step S153: constructing a sediment transport model according to the data fusion and assimilation results to obtain a sediment transport model;

[0127] Step S154: acquiring historical observation data; calibrating the model parameters of the sediment transport model using the historical observation data to obtain a calibrated model;

[0128] Step S155: Calculate terrain deformation based on the calibrated model and the data fusion and assimilation results to obtain terrain deformation simulation data;

[0129] Step S156: applying the deformation amount of the terrain deformation simulation data to the three-dimensional terrain model to update the three-dimensional terrain model to obtain a dynamically updated three-dimensional terrain model; generating digital terrain data according to the dynamically updated three-dimensional terrain model to obtain digital terrain data.

[0130] In the embodiment of the present invention, the hydrological information integration model is imported into the data processing platform, and the real-time monitoring data obtained through the sensor network, including water level, flow, rainfall, sediment content, etc., are also imported into the same platform. Using the database management function of the data processing platform, the hydrological information integration model data and the real-time monitoring data are aligned and associated according to the time and space dimensions to generate a multi-source integrated data set containing multi-source data and store it in HDF5 format for subsequent processing and analysis.

[0131] The Kalman filter algorithm is used to fuse and assimilate multi-source integrated data. The real-time monitoring data is used as the observation value, and the hydrological information integration model data is used as the background value. The two types of data are weighted averaged through the Kalman filter algorithm to obtain more accurate hydrological information, such as water level, flow, sediment content, etc. The fused data can better reflect the real-time status of the water conservancy project, reduce the impact of measurement errors and data noise, and save the results in NetCDF format.

[0132] Based on the results of data fusion and assimilation, the sediment transport model was constructed using HEC-RAS software. The fused water level, flow, sediment content and other data were used as input parameters, and a suitable sediment transport formula was selected, such as the Engelund-Hansen formula, and model parameters were set, such as riverbed roughness and sediment particle size. The HEC-RAS software was used for simulation calculation to obtain the transport and deposition rules of sediment under the action of water flow and generate a sediment transport model.

[0133] Obtain historical observation data from the database of the water conservancy project management unit, including riverbed section measurement data and sediment deposition data at different times. Import the historical observation data into the HEC-RAS software and compare and analyze it with the sediment transport model. By adjusting the model parameters, such as riverbed roughness and sediment particle size, the model simulation results are made to match the historical observation data as much as possible, and finally a calibrated sediment transport model is obtained, and the calibrated parameters are saved and recorded.

[0134] The calibrated sediment transport model and data fusion and assimilation results are used to calculate terrain deformation. The fused hydrological information is used as input parameters to run the calibrated sediment transport model to simulate the scouring and silting of the riverbed under the current water flow conditions, and obtain terrain deformation simulation data, including the terrain deformation at different locations (such as scouring depth or silting height), and save the results in GeoTIFF format.

[0135] The terrain deformation simulation data is imported into the BIM platform, and the deformation amount is applied to the three-dimensional terrain model constructed in step S14. Through the modeling function of the BIM platform, the height of the riverbed in the three-dimensional terrain model is adjusted according to the terrain deformation simulation data to generate a dynamically updated three-dimensional terrain model that more accurately reflects the current riverbed terrain. Finally, the dynamically updated three-dimensional terrain model is exported as digital terrain data, such as DEM format, for subsequent flood simulation calculations and other applications.

[0136] Preferably, step S2 comprises the following steps:

[0137] Step S21: constructing a hydraulic model based on the digital terrain data to obtain a hydraulic model;

[0138] Step S22: setting meteorological scenario parameters according to the hydraulic model to obtain meteorological scenario parameters;

[0139] Step S23: performing flood simulation calculation according to the hydraulic model and meteorological scenario parameters to obtain flood simulation calculation results;

[0140] Step S24: Visualizing the flood simulation calculation results to obtain flood propagation visualization results;

[0141] Step S25: Perform multi-scenario simulation on the flood propagation visualization results, and conduct comparative analysis of the simulation data to obtain flood simulation data.

[0142] In an embodiment of the present invention, a hydraulic model is constructed using HEC-RAS software based on digital terrain data. The digital terrain data is imported into the HEC-RAS software to create a geometric model, and parameters such as the river section, river channel length, and riverbed roughness are defined. According to the design parameters of the water conservancy project, boundary conditions are set, such as upstream inflow flow, downstream water level, etc. After the model is built, the model is verified to ensure that the model can accurately reflect the hydraulic characteristics of the actual river channel. According to the hydraulic model, different meteorological scenario parameters are set in combination with historical meteorological data and hydrological data. For example, rainstorm scenarios with different recurrence periods are set, including once in 20 years, once in 50 years, once in 100 years, etc., and different rainfall intensities and rainfall durations are set respectively. At the same time, considering different initial water level conditions, such as normal water level, warning water level, etc., a variety of meteorological scenario parameter combinations are generated for subsequent flood simulation calculations.

[0143] Import the hydraulic model and meteorological scenario parameters into the HEC-RAS software to perform flood simulation calculations. Set the simulation time step to 1 hour and the simulation duration to 72 hours. Use the calculation engine of the HEC-RAS software to simulate the evolution of floods under different meteorological scenarios, calculate the water level, flow, flow velocity and other parameters at different times, and save the calculation results as data files.

[0144] Import the flood simulation calculation results into the GIS platform for result visualization. Use the spatial analysis function of the GIS platform to overlay the flood inundation range at different times onto the digital terrain data to generate visualization results of flood propagation. Different colors are used to represent different water depths to intuitively display the flood inundation range and water depth distribution at different times, and export the visualization results as video files.

[0145] Based on the visualization results of flood propagation, HEC-RAS software was used to perform multi-scenario simulations. Meteorological scenario parameters under different return periods and different initial water levels were input into the hydraulic model to perform flood simulation calculations. The simulation results under different scenarios were compared and analyzed, such as the inundation range, maximum water depth, peak flow, etc. of floods with different return periods, and the impact of floods under different scenarios was analyzed. The comparative analysis results were compiled into a report, and finally the flood simulation data was obtained to provide a basis for subsequent risk assessment.

[0146] Preferably, step S3 comprises the following steps:

[0147] Step S31: collecting infrastructure data according to the water conservancy project area to obtain infrastructure data; integrating the infrastructure data and flood simulation data into an infrastructure model to obtain an infrastructure model;

[0148] Step S32: Analyze the potential impact of floods on different infrastructures based on the infrastructure model and flood simulation data to obtain flood impact analysis data;

[0149] Step S33: classifying infrastructure risk levels in different regions and types according to the flood impact analysis data and infrastructure data to obtain risk level classification data;

[0150] Step S34: determining early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas;

[0151] Step S35: setting dynamic thresholds according to historical flood information data and early warning indicators of different risk areas to obtain early warning dynamic thresholds;

[0152] Step S36: formulate warning rules according to the warning dynamic threshold to obtain warning rules; construct a warning model according to the warning rules and a preset warning information database to obtain a warning model;

[0153] Step S37: using the early warning model to perform flood early warning processing on the real-time monitoring data to obtain flood early warning information data;

[0154] Step S38: Generate a risk assessment report based on the flood impact analysis data, risk level classification data, and flood warning information data to obtain a risk assessment report.

[0155] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0156] Step S31: collecting infrastructure data according to the water conservancy project area to obtain infrastructure data; integrating the infrastructure data and flood simulation data into an infrastructure model to obtain an infrastructure model;

[0157] In the embodiment of the present invention, the infrastructure data in the water conservancy project area is collected by means of on-site survey, measurement and data obtained from government departments, including the location, type, elevation, material, construction year and other information of buildings, roads and bridges, power facilities, communication facilities, etc. The collected infrastructure data is imported into the BIM platform, and combined with the flood simulation data, the infrastructure model is integrated with the digital terrain model to construct a three-dimensional model containing infrastructure information. Each infrastructure object has a unique ID and attribute information in the BIM model.

[0158] Step S32: Analyze the potential impact of floods on different infrastructures based on the infrastructure model and flood simulation data to obtain flood impact analysis data;

[0159] In the embodiment of the present invention, based on the integrated infrastructure model and flood simulation data, the collision detection function of the BIM platform is used to analyze the potential impact of floods on different infrastructures. The flooding conditions under different water levels are simulated, the infrastructure submerged by the flood is identified, and indicators such as the submergence depth and submergence time are calculated. Combined with the attribute information of the infrastructure, such as material, construction year, etc., the potential damage caused by the flood to the infrastructure, such as structural damage, loss of function, etc., is evaluated, and flood impact analysis data is generated, including the ID, type, location, submergence depth, potential damage and other information of the affected infrastructure.

[0160] Step S33: classifying infrastructure risk levels in different regions and types according to the flood impact analysis data and infrastructure data to obtain risk level classification data;

[0161] In the embodiment of the present invention, risk levels are divided for infrastructures of different regions and types based on flood impact analysis data and infrastructure data. The water conservancy project area is divided into several sub-areas, and the infrastructure is divided into three levels of high risk, medium risk, and low risk according to the potential degree of damage and importance of the infrastructure in each sub-area. For example, important public facilities such as hospitals and schools are divided into high risk levels; residential buildings are divided into medium risk levels; general roads, farmlands, etc. are divided into low risk levels. The risk level division data is stored in the database of the BIM model.

[0162] Step S34: determining early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas;

[0163] In the embodiment of the present invention, data is divided according to risk levels to determine early warning indicators for different risk areas. For high-risk areas, key indicators such as water level, flow rate, and rainfall are selected as early warning indicators; for medium-risk areas, indicators such as water level and rainfall are selected as early warning indicators; for low-risk areas, water level is selected as the early warning indicator.

[0164] Step S35: setting dynamic thresholds according to historical flood information data and early warning indicators of different risk areas to obtain early warning dynamic thresholds;

[0165] Dynamic thresholds are set based on historical flood information data and early warning indicators for different risk areas. The early warning indicator values ​​corresponding to different risk levels in historical flood data are analyzed, such as the highest water level values ​​corresponding to different risk levels. Combined with the real-time monitoring data of early warning indicators, the early warning threshold is dynamically adjusted according to the ratio of the current water level to the historical highest water level. For example, when the real-time water level reaches 80% of the historical highest water level, an early warning is triggered.

[0166] Step S36: formulate warning rules according to the warning dynamic threshold to obtain warning rules; construct a warning model according to the warning rules and a preset warning information database to obtain a warning model;

[0167] In the embodiment of the present invention, an early warning rule is formulated according to the early warning dynamic threshold. For example, when the water level in a high-risk area exceeds the early warning dynamic threshold, a red warning is issued; when the water level in a medium-risk area exceeds the early warning dynamic threshold, an orange warning is issued; when the water level in a low-risk area exceeds the early warning dynamic threshold, a yellow warning is issued. According to the early warning rules and the preset early warning information library (including early warning information content and release channels corresponding to different early warning levels), an early warning model is constructed.

[0168] Step S37: using the early warning model to perform flood early warning processing on the real-time monitoring data to obtain flood early warning information data;

[0169] In the embodiment of the present invention, the early warning model is used to process the flood early warning of the real-time monitoring data. The real-time monitoring data is input into the early warning model to determine whether the early warning conditions are met. If the early warning conditions are met, the corresponding early warning information is extracted from the early warning information library according to the early warning rules, and the early warning information is issued to relevant departments and personnel through SMS, email, APP push, etc.

[0170] Step S38: generating a risk assessment report based on the flood impact analysis data, the risk level classification data, and the flood warning information data to obtain a risk assessment report;

[0171] In the embodiment of the present invention, a risk assessment report is automatically generated based on the flood impact analysis data, risk level classification data and flood warning information data. The report content includes flood simulation results, affected infrastructure conditions, risk level classification conditions, warning information release conditions, etc., providing decision support for risk management of water conservancy projects.

[0172] Preferably, step S32 includes the following steps:

[0173] Step S321: extracting the flood-inundated area from the flood simulation data to obtain flood-inundated area data;

[0174] Step S322: performing infrastructure collision detection on the flood inundation area data and the infrastructure model to obtain infrastructure collision detection data;

[0175] Step S323: Identify the affected infrastructure according to the infrastructure collision detection data to obtain the affected infrastructure data;

[0176] Step S324: Assess the extent of infrastructure damage based on the affected infrastructure data and the flood simulation data to obtain infrastructure damage extent data;

[0177] Step S325: Generate flood impact analysis data based on the infrastructure damage degree data to obtain flood impact analysis data.

[0178] In the embodiment of the present invention, the flood simulation data is processed using a GIS platform to extract the flooded areas at different times. According to the water depth data in the simulation results, a flooding threshold is set, for example, a water depth greater than 0.5 meters is considered to be flooded. The water depth area exceeding the threshold is extracted to generate flooded area data, including the boundary, area, water depth and other information of the flooded area, and saved in a vector polygon format.

[0179] Import the flood inundation area data and infrastructure model into the BIM platform. Use the collision detection function of the BIM platform to perform collision detection on the flood inundation area and the infrastructure model. The specific operation is to perform spatial intersection analysis on the polygon representing the flood inundation area and the infrastructure model, identify the infrastructure located in the flood inundation area, and generate infrastructure collision detection data, including the ID of the infrastructure where the collision occurred, the area of ​​the collision area, the collision volume and other information.

[0180] Based on the infrastructure collision detection data, identify the infrastructure affected by the flood. Filter the infrastructure with a collision area greater than 0 in the collision detection data and identify it as the affected infrastructure. Record the ID, type, location and other information of the affected infrastructure to generate the affected infrastructure data.

[0181] Assess the extent of damage to infrastructure based on affected infrastructure data and flood simulation data. Extract flood water depth, flow velocity and other data at the location of the affected infrastructure, combine with infrastructure attribute information, such as material, flood resistance, etc., and use a pre-established flood damage assessment model (such as an empirical formula based on the depth-damage relationship or a numerical simulation method) to calculate the extent of damage to infrastructure, such as minor damage, moderate damage, severe damage, etc., and quantify the extent of damage, for example, as a value between 0 and 1, where 0 means no damage and 1 means complete damage. Save the assessment results as infrastructure damage extent data, including information such as the ID of the affected infrastructure, the extent of damage, and the loss value.

[0182] Generate flood impact analysis data based on infrastructure damage data. Integrate infrastructure damage data with affected infrastructure data to generate flood impact analysis data with more comprehensive information. The data includes the ID, type, location, flood depth, damage level, potential losses and other information of the affected infrastructure, which is used for subsequent risk level classification and risk assessment report generation.

[0183] Preferably, step S324 is specifically as follows:

[0184] Extracting infrastructure attribute information based on the affected infrastructure data and the infrastructure model to obtain infrastructure attribute information data;

[0185] Calculate flood action force based on flood simulation data and infrastructure attribute information data to obtain flood action force data;

[0186] The damage degree is calculated based on the flood force data and the infrastructure attribute information data to obtain the damage degree data;

[0187] Quantify the loss value according to the damage degree data to obtain the loss value quantification data;

[0188] Use loss value quantification data and infrastructure models to visualize damage results and obtain infrastructure damage maps;

[0189] The infrastructure damage degree data is generated according to the infrastructure damage map to obtain the infrastructure damage degree data.

[0190] In an embodiment of the present invention, attribute information of the affected infrastructure is extracted from the BIM model database, including building height, material (such as reinforced concrete, brick-concrete structure), flood resistance level, importance level, construction year, construction cost, etc., and this information is organized into structured data to generate infrastructure attribute information data.

[0191] Based on the flood simulation data, extract the flood depth, flow rate and other data at the location of the affected infrastructure. Use hydraulic formulas, such as the Manning formula, to calculate the shear force of the water flow, and combine the flood depth and the area of ​​the infrastructure's frontal surface to calculate the hydrostatic and hydrodynamic pressures of the flood on the infrastructure. Superimpose the calculated hydrostatic and hydrodynamic pressure vectors to obtain the resultant force of the flood, and generate flood force data, including the magnitude and direction of the force on each affected infrastructure.

[0192] The extent of infrastructure damage is calculated based on flood force data and infrastructure attribute information data. Physical model-based calculation methods, such as finite element analysis, are used to apply flood forces to the infrastructure model to simulate the stress and deformation of the infrastructure under the action of floods. The extent of infrastructure damage is determined based on predefined damage criteria, such as stress exceeding the yield strength or displacement exceeding the allowable value, and the extent of damage is quantified with a value between 0 and 1, where 0 indicates no damage and 1 indicates complete damage.

[0193] Based on the calculated damage data, the loss value of the infrastructure is quantified. A cost-based assessment method is used, and the repair cost or reconstruction cost of the infrastructure is used as the loss value. According to the degree of damage to the infrastructure, the corresponding repair cost or reconstruction cost is calculated. For example, the repair cost of minor damage is 10% of the construction cost, the repair cost of moderate damage is 50% of the construction cost, and the reconstruction cost of severe damage is 100% of the construction cost.

[0194] Using the 3D visualization function of the BIM platform, the loss value quantification data and the infrastructure model are combined to generate an infrastructure damage map. Different colors are used to represent different degrees of damage, such as green for minor damage, yellow for moderate damage, and red for severe damage. At the same time, the loss value of each affected infrastructure is displayed in the damage map to intuitively understand the losses caused by the flood.

[0195] The visualized damage results, including the degree of damage and loss value, are output in the form of structured data to generate the final infrastructure damage degree data for subsequent risk assessment and decision support.

[0196] Preferably, step S34 includes the following steps:

[0197] Step S341: extracting risk areas according to the risk level classification data to obtain risk area classification data;

[0198] Step S342: Identify potential disaster factors according to the risk area division data to obtain potential disaster factors;

[0199] Step S343: screening the risk area division data according to potential disaster factors to obtain analysis and screening indicators;

[0200] Step S344: performing indicator correlation analysis on the analysis and screening indicators to obtain indicator correlation data;

[0201] Step S345: Determine the indicator weights of different risk areas according to the analysis and screening indicators and indicator correlation data to obtain indicator weight values;

[0202] Step S346: Generate early warning indicators for different risk areas according to the indicator weight values ​​and the analysis and screening indicators to obtain early warning indicators for different risk areas.

[0203] In the embodiment of the present invention, based on the risk level classification data, the GIS platform is used to extract the areas corresponding to different risk levels. The risk level classification data is imported into the GIS platform, and different layers are created according to the three levels of high risk, medium risk, and low risk. Infrastructures of the same risk level are connected into pieces to form corresponding risk areas. The risk area classification data is output, including the boundary, area, risk level and other information of each risk area, and saved in a vector polygon format.

[0204] Identify potential disaster factors based on risk area classification data. Analyze the characteristics of different risk areas and potential flood threats, such as upstream water, heavy rainfall, and dam breaches. Combine historical flood data and hydrological data to determine the main disaster factors for each risk area. For example, for areas close to rivers, the main disaster factors are upstream water and dam breaches; for low-lying areas, the main disaster factors are heavy rainfall and waterlogging. Organize the identification results into a list of potential disaster factors, including the type of disaster factors corresponding to each risk area.

[0205] According to the potential disaster-causing factors, the risk area division data is screened for indicators. Indicators related to potential disaster-causing factors are screened from the preset indicator library. The indicator library contains multiple indicators such as water level, flow, flow velocity, rainfall, soil moisture content, river slope, etc. For example, for disaster-causing factors such as upstream water and dam breach, screen indicators such as water level, flow, flow velocity, etc.; for disaster-causing factors such as heavy rainfall and waterlogging, screen indicators such as rainfall and soil moisture content. The screening results are organized into an analysis and screening indicator list, which contains the indicator type corresponding to each risk area.

[0206] Conduct indicator correlation analysis on the analysis and screening indicators. Use statistical methods, such as the Pearson correlation coefficient, to calculate the correlation between different indicators. For example, calculate the correlation coefficient between water level and flow, the correlation coefficient between rainfall and soil moisture, etc. Organize the calculation results into indicator correlation data, including the correlation coefficient matrix between different indicators.

[0207] Based on the analysis and screening of indicators and indicator correlation data, determine the indicator weights for different risk areas. Use methods such as the analytic hierarchy process (AHP) or the entropy method to determine the weight of each indicator based on the importance of the indicator and the correlation between the indicators. For example, for high-risk areas, the weight of water level may be higher; for low-risk areas, the weight of rainfall may be lower. The calculation results are organized into indicator weight value data, including the weight value corresponding to each indicator in each risk area.

[0208] According to the indicator weight value and analysis and screening indicators, early warning indicators for different risk areas are generated. The indicators of each risk area and their corresponding weight values ​​are combined to form the early warning indicators for that area. For example, the early warning indicators for a high-risk area may include water level (weight 0.7), flow (weight 0.2), and flow velocity (weight 0.1); the early warning indicators for a low-risk area may only include water level (weight 1.0).

[0209] Preferably, step S35 includes the following steps:

[0210] Step S351: performing historical flood analysis on the historical flood information data to obtain historical flood analysis data; performing flood feature extraction on the historical flood analysis data to obtain flood feature data;

[0211] Step S352: constructing a correlation model between rainfall and water level based on historical flood analysis data and flood characteristic data to obtain a rainfall-water level relationship model;

[0212] Step S353: extracting data of regions with different risk levels from the risk level classification data to obtain data of regions with different risk levels;

[0213] Step S354: setting an initial threshold value according to flood characteristic data, early warning indicators of different risk areas, and data of areas with different risk levels to obtain an initial regional threshold value;

[0214] Step S355: Obtain weather forecast data; according to the weather forecast data and real-time monitoring data, use the rainfall-water level relationship model to dynamically adjust the regional initial threshold to obtain the early warning dynamic threshold.

[0215] In the embodiment of the present invention, the historical flood information data is statistically analyzed to calculate the frequency of occurrence of historical floods, peak flow, flood duration, inundation range and other indicators, and generate historical flood analysis data. Flood characteristic data are further extracted from the historical flood analysis data, such as key parameters such as peak flow and flood duration corresponding to different recurrence periods (e.g., once in 20 years, once in 50 years, and once in 100 years), as well as the historical maximum water depth and maximum flow rate of different risk level areas.

[0216] Based on historical flood analysis data and flood characteristic data, a correlation model between rainfall and water level is constructed. Statistical methods, such as multivariate linear regression or machine learning algorithms, are used to establish a functional relationship between rainfall and water level. The model input is rainfall data, and the output is the corresponding water level prediction value. The construction of the model needs to consider a variety of factors, such as rainfall intensity, rainfall duration, watershed area, soil type, etc. Through model training and verification, ensure that the prediction accuracy of the model meets the requirements.

[0217] Extract data of different risk level areas from risk level classification data. Extract data of high risk, medium risk and low risk areas respectively to generate data of different risk level areas, including information such as boundaries, area and risk level of each risk area.

[0218] The initial threshold is set based on flood characteristic data, warning indicators, and data on areas with different risk levels. For example, for high-risk areas, the historical maximum water depth corresponding to a flood that occurs once every 100 years is set as the initial threshold; for medium-risk areas, the historical maximum water depth corresponding to a flood that occurs once every 50 years is set as the initial threshold; for low-risk areas, the historical maximum water depth corresponding to a flood that occurs once every 20 years is set as the initial threshold. The initial thresholds of warning indicators for areas with different risk levels are stored in the database.

[0219] Obtain weather forecast data for the next 24 hours or 48 hours from the meteorological department, including rainfall, rainfall intensity, etc. Combined with real-time monitoring data, such as current water level, flow, etc., use the rainfall-water level relationship model to predict the water level change trend in the future. Dynamically adjust the regional initial threshold according to the predicted water level change trend. For example, if the future water level is predicted to exceed the initial threshold, the early warning dynamic threshold is adjusted to the predicted highest water level value; if the future water level is predicted to be lower than the initial threshold, the initial threshold remains unchanged. The dynamically adjusted early warning dynamic threshold is updated in real time to the database for the formulation of early warning rules for S36.

[0220] Preferably, step S4 comprises the following steps:

[0221] Step S41: Identify weak links in the water conservancy project design according to the risk assessment report to obtain weak link data;

[0222] Step S42: Formulate an optimization strategy based on the weak link data to obtain an engineering optimization strategy;

[0223] Step S43: Optimizing the engineering design according to the engineering optimization strategy to obtain engineering design optimization data;

[0224] Step S44: adjusting emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters;

[0225] Step S45: Perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model.

[0226] In an embodiment of the present invention, weak links in the design of a water conservancy project are identified based on a risk assessment report. The flood simulation results, the affected infrastructure conditions, the risk level classification, and other contents in the risk assessment report are analyzed to find out the deficiencies in the engineering design. For example, by analyzing the distribution of flood-inundated areas and infrastructure, weak links such as insufficient height of flood control dams and unreasonable drainage system design are identified. The identified weak links and their related data, such as location, type, degree of impact, etc., are recorded in a weak link database to form weak link data.

[0227] Formulate engineering optimization strategies based on weak link data. Develop corresponding optimization measures for each weak link. For example, for the weak link of insufficient height of flood control dam, develop an optimization strategy of raising the dam; for the weak link of unreasonable drainage system design, develop an optimization strategy of improving drainage system design. The optimization strategy should include specific technical solutions, implementation steps, required resources and other information, and be stored in the engineering optimization strategy database.

[0228] Optimize the engineering design according to the engineering optimization strategy. Use the modeling function of the BIM platform to modify and improve the water conservancy project design. For example, according to the optimization strategy of raising the dam, modify the height of the dam in the BIM model; according to the optimization strategy of improving the drainage system design, redesign the layout and size of the drainage pipes in the BIM model. Save the optimized engineering design data to the BIM model database to generate engineering design optimization data.

[0229] Adjust the emergency parameters according to the engineering design optimization data and the risk assessment report generated by S38. For example, adjust the water level threshold in the early warning model according to the engineering design optimization data of the heightened dam; adjust the drainage scheduling scheme in the emergency plan according to the engineering design optimization data of the improved drainage system design. Store the adjusted emergency parameters, such as the early warning threshold, drainage scheduling scheme, etc., in the emergency parameter database to generate emergency adjustment parameters.

[0230] Carry out flood warning and emergency response operations according to emergency adjustment parameters and early warning models. Monitor key indicators such as water level and rainfall in real time, and input monitoring data into the early warning model. When the monitoring data exceeds the early warning threshold adjusted by S44, immediately start the emergency plan, and according to the emergency adjustment parameters, such as drainage scheduling plan, perform corresponding emergency response operations, such as opening drainage pump stations, dispatching personnel and materials, etc., to minimize the losses caused by flood disasters.

[0231] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0232] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A digital management method for water conservancy projects based on BIM model, characterized in that: The following steps are involved: Step S1: acquiring real-time monitoring data; collecting high-precision terrain data of the water conservancy project area, and performing data preprocessing to obtain preprocessed data; Based on the pre-processed data, the BIM platform is used to perform hydrological information integrated modeling to obtain a hydrological information integrated model; the hydrological information integrated model is dynamically updated with multi-dimensional terrain using real-time monitoring data to obtain digital terrain data; Step S2: performing flood simulation calculation based on digital terrain data to obtain flood simulation calculation results; performing flood scenario simulation based on the flood simulation calculation results to obtain flood simulation data; Step S3: Integrate the infrastructure model according to the flood simulation data to obtain the infrastructure model; analyze the potential impact of the flood on different infrastructures according to the infrastructure model and the flood simulation data to obtain flood impact analysis data; According to the flood impact analysis data, infrastructure risk levels are classified to obtain risk level classification data; Determine early warning indicators for different risk areas based on risk level classification data to obtain early warning indicators for different risk areas; set dynamic thresholds based on early warning indicators for different risk areas to obtain early warning dynamic thresholds; The early warning model is constructed according to the early warning dynamic threshold to obtain the early warning model; Use the early warning model to process the real-time monitoring data for flood warning and obtain flood warning information data; Generate a risk assessment report based on flood impact analysis data, risk level classification data, and flood warning information data to obtain a risk assessment report; Step S4: Optimize the engineering design according to the risk assessment report to obtain engineering design optimization data; adjust emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters; Carry out flood warning and emergency response operations based on emergency adjustment parameters and warning models.

2. The digital management method for water conservancy projects based on the BIM model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collect high-precision terrain data in the water conservancy project area to obtain high-precision terrain data; obtain historical flood information data, long-term meteorological data and river hydrological data; Step S12: preprocessing the high-precision terrain data, historical flood information data, long-term meteorological data, and river hydrological data to obtain preprocessed data; Step S13: constructing a three-dimensional terrain model using the BIM platform according to the preprocessed data to obtain a three-dimensional terrain model; Step S14: integrating hydrological information of the three-dimensional terrain model according to the preprocessed data to obtain a hydrological information integration model; Step S15: Acquire real-time monitoring data; use the real-time monitoring data to dynamically update the multi-dimensional terrain of the hydrological information integration model to obtain digital terrain data.

3. The digital management method for water conservancy projects based on the BIM model according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: performing multi-source data integration on the hydrological information integration model and the real-time monitoring data to obtain multi-source integrated data; Step S152: performing data fusion and assimilation on the multi-source integrated data to obtain data fusion and assimilation results; Step S153: constructing a sediment transport model according to the data fusion and assimilation results to obtain a sediment transport model; Step S154: acquiring historical observation data; calibrating the model parameters of the sediment transport model using the historical observation data to obtain a calibrated model; Step S155: Calculate terrain deformation based on the calibrated model and the data fusion and assimilation results to obtain terrain deformation simulation data; Step S156: applying the deformation amount of the terrain deformation simulation data to the three-dimensional terrain model to update the three-dimensional terrain model to obtain a dynamically updated three-dimensional terrain model; generating digital terrain data according to the dynamically updated three-dimensional terrain model to obtain digital terrain data.

4. The digital management method for water conservancy projects based on the BIM model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: constructing a hydraulic model based on the digital terrain data to obtain a hydraulic model; Step S22: setting meteorological scenario parameters according to the hydraulic model to obtain meteorological scenario parameters; Step S23: performing flood simulation calculation according to the hydraulic model and meteorological scenario parameters to obtain flood simulation calculation results; Step S24: Visualizing the flood simulation calculation results to obtain flood propagation visualization results; Step S25: Perform multi-scenario simulation on the flood propagation visualization results, and conduct comparative analysis of the simulation data to obtain flood simulation data.

5. The digital management method for water conservancy projects based on the BIM model according to claim 2 is characterized in that: Step S3 includes the following steps: Step S31: collecting infrastructure data according to the water conservancy project area to obtain infrastructure data; integrating the infrastructure data and flood simulation data into an infrastructure model to obtain an infrastructure model; Step S32: Analyze the potential impact of floods on different infrastructures based on the infrastructure model and flood simulation data to obtain flood impact analysis data; Step S33: performing risk level classification of infrastructure in different regions and types according to the flood impact analysis data and infrastructure data to obtain risk level classification data; Step S34: determining early warning indicators for different risk areas according to the risk level classification data to obtain early warning indicators for different risk areas; Step S35: setting dynamic thresholds according to historical flood information data and early warning indicators of different risk areas to obtain early warning dynamic thresholds; Step S36: Formulate warning rules according to the warning dynamic threshold to obtain warning rules; construct a warning model according to the warning rules and a preset warning information database to obtain a warning model; Step S37: using the early warning model to perform flood early warning processing on the real-time monitoring data to obtain flood early warning information data; Step S38: Generate a risk assessment report based on the flood impact analysis data, risk level classification data, and flood warning information data to obtain a risk assessment report.

6. The digital management method for water conservancy projects based on the BIM model according to claim 5 is characterized in that: Step S32 includes the following steps: Step S321: extracting the flood-inundated area from the flood simulation data to obtain flood-inundated area data; Step S322: performing infrastructure collision detection on the flood inundation area data and the infrastructure model to obtain infrastructure collision detection data; Step S323: Identify the affected infrastructure according to the infrastructure collision detection data to obtain the affected infrastructure data; Step S324: Assess the extent of infrastructure damage based on the affected infrastructure data and the flood simulation data to obtain infrastructure damage extent data; Step S325: Generate flood impact analysis data based on the infrastructure damage degree data to obtain flood impact analysis data.

7. The digital management method for water conservancy projects based on the BIM model according to claim 6 is characterized in that: Step S324 is specifically as follows: Extracting infrastructure attribute information based on the affected infrastructure data and the infrastructure model to obtain infrastructure attribute information data; Calculate flood action force based on flood simulation data and infrastructure attribute information data to obtain flood action force data; The damage degree is calculated based on the flood force data and the infrastructure attribute information data to obtain the damage degree data; Quantify the loss value according to the damage degree data to obtain the loss value quantification data; Use loss value quantification data and infrastructure models to visualize damage results and obtain infrastructure damage maps; The infrastructure damage degree data is generated according to the infrastructure damage map to obtain the infrastructure damage degree data.

8. The digital management method for water conservancy projects based on the BIM model according to claim 5 is characterized in that: Step S34 includes the following steps: Step S341: extracting risk areas according to the risk level classification data to obtain risk area classification data; Step S342: Identify potential disaster factors according to the risk area division data to obtain potential disaster factors; Step S343: screening the risk area division data according to potential disaster factors to obtain analysis and screening indicators; Step S344: performing indicator correlation analysis on the analysis and screening indicators to obtain indicator correlation data; Step S345: Determine the indicator weights of different risk areas according to the analysis and screening indicators and indicator correlation data to obtain indicator weight values; Step S346: Generate early warning indicators for different risk areas according to the indicator weight values ​​and the analysis and screening indicators to obtain early warning indicators for different risk areas.

9. The digital management method for water conservancy projects based on the BIM model according to claim 5 is characterized in that: Step S35 includes the following steps: Step S351: performing historical flood analysis on the historical flood information data to obtain historical flood analysis data; performing flood feature extraction on the historical flood analysis data to obtain flood feature data; Step S352: constructing a correlation model between rainfall and water level based on historical flood analysis data and flood characteristic data to obtain a rainfall-water level relationship model; Step S353: extracting data of regions with different risk levels from the risk level classification data to obtain data of regions with different risk levels; Step S354: setting an initial threshold value according to flood characteristic data, early warning indicators of different risk areas, and data of areas with different risk levels to obtain an initial regional threshold value; Step S355: Obtain weather forecast data; according to the weather forecast data and real-time monitoring data, use the rainfall-water level relationship model to dynamically adjust the regional initial threshold to obtain the early warning dynamic threshold.

10. The digital management method for water conservancy projects based on BIM model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Identify weak links in the water conservancy project design according to the risk assessment report to obtain weak link data; Step S42: Formulate an optimization strategy based on the weak link data to obtain an engineering optimization strategy; Step S43: Optimizing the engineering design according to the engineering optimization strategy to obtain engineering design optimization data; Step S44: adjusting emergency parameters according to the engineering design optimization data and the risk assessment report to obtain emergency adjustment parameters; Step S45: Perform flood warning and emergency response operations according to the emergency adjustment parameters and the warning model.

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