Multi-source surveying and mapping data dynamic three-dimensional visualization system and method based on digital twinning

Abstract

This invention discloses a dynamic 3D visualization system and method for multi-source surveying and mapping data based on digital twins. The system includes a multi-source data acquisition and fusion module, a hydrological dynamic prediction model module, a digital twin engine module, a dynamic visualization and interactive inference module, an early warning module, a model self-calibration and inversion module, and a data storage and management module. This invention constructs a high-precision 3D model integrating land and water, and utilizes a dedicated splash prediction sub-model to quantify and predict the splash height and length of tidal water on the coastline, achieving accurate automatic delineation of hazardous areas and proactive early warning. A closed-loop self-calibration mechanism is introduced. By comparing measured and predicted splash data, and employing inversion analysis methods such as genetic algorithms, the system intelligently diagnoses and updates model deviations caused by changes in riverbed topography and river surface obstacles, thereby continuously improving prediction accuracy and intuitively revealing changes within the river channel, providing in-depth decision support for hydrological management and public safety.

Description

Technical Field

[0001] This invention relates to the field of geographic model image processing technology, and more specifically, to a dynamic three-dimensional visualization system and method for multi-source surveying and mapping data based on digital twins. Background Technology

[0002] Tides, tidal bores, and other dynamic hydrological phenomena are important natural landscapes and water resources in coastal areas, but they also pose ongoing challenges to the safety of people along the coast, the protection of infrastructure, and the management of waterways. Traditional tide monitoring, forecasting, and management mainly rely on limited hydrological station data, two-dimensional chart analysis, and judgments based on historical experience, which have the following significant limitations:

[0003] The limited predictive scope of existing hydrological models fails to meet the needs of micro-level safety management: Current models primarily focus on macroscopic predictions of tidal levels, current velocities, and inundation extent. They lack specialized, quantitative predictive capabilities for the critical microscopic phenomenon of splashing (overtopping) caused by tidal impacts on riverbanks—a direct threat to personal safety and elevated infrastructure. Existing technologies cannot accurately predict the height, length, and coverage of splashes, making it impossible to precisely delineate danger zones beforehand. Safety warnings are often based on macroscopic hydrological conditions and are general, regional alerts lacking specificity.

[0004] The accuracy of hydrological models decays rapidly, and there is a lack of long-term calibration mechanisms: the initial accuracy of hydrological models is based on topographic data at a certain point in time. However, the river environment is dynamic; scouring and silting of the riverbed (topographic changes) and temporary obstacles in the river channel (such as moored ships and floating objects) can significantly alter the flow structure, causing the model's predictive accuracy to decline rapidly over time. Currently, there is a lack of a closed-loop system that can automatically and periodically diagnose the causes of model deviations and complete high-precision calibration using real-time, easily accessible monitoring data.

[0005] Insufficient visualization and insight capabilities limit the depth of decision support: Existing 3D visualization systems mostly focus on static scene display or simple flood inundation simulation, making it difficult to achieve realistic simulation of dynamic tidal evolution, especially the splashing process. More importantly, the system cannot use the discrepancy between prediction and reality to gain insight into and display the internal changes in the river channel (such as underwater topography and obstacles) that cause the deviation. This makes it difficult for managers to quickly locate the root cause of the problem, limiting its application in in-depth hydrological operations such as precision dredging and waterway maintenance.

[0006] The current early warning mechanism is passive and delayed: most early warning systems are triggered after parameters such as water level and flow velocity exceed the standard, which is a reactive or real-time alarm. For disasters such as tidal bores that occur suddenly, this early warning method is passive and delayed. There is an urgent need for a proactive safety assurance system that can delineate safe areas in advance based on high-precision prediction models and actively issue accurate warnings before a disaster occurs.

[0007] Digital twin technology, as a novel technological paradigm connecting the physical world and virtual space, offers the possibility of constructing high-fidelity, real-time interactive hydrological environment models. However, existing digital twin applications in the hydrological field are mostly limited to data integration and static display. How to build an intelligent digital twin system that can deeply integrate multi-source data, accurately predict micro-hydrological phenomena, possess self-calibration and inversion insight capabilities, and provide proactive safety early warnings remains a pressing technical challenge to be solved in this field.

[0008] To address the aforementioned issues, a dynamic 3D visualization system and method for multi-source surveying and mapping data based on digital twins are provided. Summary of the Invention

[0009] In order to overcome the above-mentioned defects of the prior art, the present invention provides a dynamic three-dimensional visualization system and method for multi-source mapping data based on digital twins to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] A dynamic 3D visualization system for multi-source surveying and mapping data based on digital twins includes:

[0012] The multi-source data acquisition and fusion module is used to acquire and fuse multi-source mapping data to generate a three-dimensional topographic model integrating land and water features.

[0013] The hydrological dynamic prediction model module is used to perform numerical simulation and prediction based on hydrological and meteorological data, and output the prediction results.

[0014] The hydrological dynamic prediction model module includes a splash prediction sub-model. This sub-model uses the flow velocity U and water depth H at the bank's leading edge, calculated by the hydrological model, as its main input parameters, and estimates the splash height H_splash and length L_splash based on the following set of empirical formulas:

[0015] H_splash=k1*U² / (2g)*sin²(θ)

[0016] L_splash=k2*(U²*sin(2θ)) / g

[0017] Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness;

[0018] A digital twin engine module is used to construct a 3D scene and dynamically map the prediction results to the 3D scene;

[0019] The dynamic visualization and interactive simulation module is used to display dynamic 3D scenes and receive user simulation parameters to trigger recalculation and visualization updates;

[0020] The data storage and management module is used to store and manage system data;

[0021] The early warning module is used to trigger an alarm when the predicted tidal splash parameters exceed a preset threshold.

[0022] The model self-calibration and inversion module is used to periodically acquire actual tidal splash data, compare it with pre-simulation data, and update the parameters of the 3D model and the prediction model through inversion analysis.

[0023] The model self-calibration and inversion module includes an inversion analysis unit, which is configured to perform the following operations:

[0024] a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω;

[0025] b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F;

[0026] c. Use the obtained solution as the inverse result of changes in the riverbed and river surface obstacles.

[0027] Preferably, the dynamic visualization and interactive simulation module can automatically delineate and display safe and dangerous areas in a three-dimensional scene based on the predicted sputtering height and length, wherein the dangerous areas are highlighted with a first preset color and the safe areas are distinguished by a second preset color.

[0028] Preferably, the model self-calibration and inversion module further includes:

[0029] The real-time monitoring unit is used to periodically acquire actual tidal splash data measured by laser rangefinders or image recognition cameras set up along the coast.

[0030] The data comparison unit is used to calculate the deviation between the actual measurement data and the pre-simulation data at the same time point, and to trigger the inversion analysis unit when the deviation value exceeds the allowable range.

[0031] The model update unit is used to feed back ΔZ and Ω obtained from the inversion analysis unit to the digital twin engine module and the hydrological dynamic prediction model module, and dynamically update the riverbed topography model and river surface obstruction model in the 3D scene as well as optimize the model parameters.

[0032] Preferably, the digital twin engine module can display the changes in river surface obstacles and riverbed topography, which have been inverted by the model self-calibration and inversion module, in a real-time and visualized manner in a three-dimensional view interface; wherein, the riverbed elevation change ΔZ is displayed by overlaying a color mapping map, and the distribution of river surface obstacles Ω is displayed by loading a semi-transparent three-dimensional warning model.

[0033] Preferably, the alarm triggering method of the early warning module includes at least one of the following: triggering an on-site audible and visual alarm, sending an early warning text message to a preset list, issuing a voice reminder through a public broadcast system, or pushing a pop-up warning in the application interface.

[0034] Preferably, the empirical coefficients k1 and k2 are determined by regression analysis fitting of the actual splash data measured during historical tides with the corresponding flow velocity U, water depth H and bank slope angle θ.

[0035] Preferably, in the inversion analysis unit, the riverbed elevation change ΔZ is encoded using a real number encoding method, and the presence or absence of obstacles Ω on the river surface is encoded using a binary encoding method.

[0036] A dynamic 3D visualization method for multi-source mapping data based on digital twins includes the following steps:

[0037] S1: Acquire and process multi-source mapping data to generate a high-precision integrated three-dimensional topographic model of land and water.

[0038] S2: Based on the input hydrological and meteorological data, perform numerical simulation and prediction calculations of tidal dynamics and coastal splash;

[0039] The prediction of sputtering uses the frontal velocity U and water depth H as the main input parameters, and estimates the sputtering height H_splash and length L_splash based on the following set of empirical formulas:

[0040] H_splash=k1* U² / (2g)*sin²(θ)

[0041] L_splash=k2*(U²*sin(2θ)) / g

[0042] Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness;

[0043] Generate a dataset of prediction results;

[0044] S3: Load the three-dimensional terrain model and construct the basic three-dimensional scene;

[0045] S4: The predicted result dataset is mapped to the three-dimensional scene in real time, driving the dynamic changes of the visualization elements to form a dynamic three-dimensional visualization of hydrological tides, and automatically delineating and displaying safe and dangerous areas.

[0046] S5: Display the dynamic 3D visualization to the user;

[0047] S6: Monitor prediction results and issue an alert when sputtering exceeds limits;

[0048] S7: Model self-calibration steps: periodically acquire actual sputtering data and compare it with the pre-simulation data; if the deviation is too large, perform inversion analysis;

[0049] The inversion analysis includes:

[0050] a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω;

[0051] b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F;

[0052] c. Based on the obtained solution, update the parameters of the 3D model and the prediction model to complete the calibration;

[0053] S8: Receive the deduction parameters input by the user, and re-execute S2 to S5 based on the new parameters to realize interactive dynamic deduction.

[0054] Preferably, in step S4, the elevation of the water surface mesh is dynamically adjusted based on the water level data using vertex shader technology, and the splashing effect is simulated using a particle system.

[0055] Preferably, in step S7, after calibration is completed, a diagnostic report containing the inverted changes in riverbed and surface obstacles is generated to support hydrological management decisions.

[0056] The technical effects and advantages of this invention, a dynamic 3D visualization system and method for multi-source surveying and mapping data based on digital twins, are as follows:

[0057] This invention introduces a dedicated splash prediction sub-model, based on hydrodynamic parameters (flow velocity, water depth) and shoreline geometry (slope), to achieve quantitative prediction of the height and length of tidal splashes. The system not only visually displays the splashing process through dynamic animation, but also automatically and accurately delineates danger and safety zones in a 3D scene based on the prediction results. Combined with an early warning module, it issues alerts to personnel in specific areas through multiple channels before an emergency occurs, achieving a leap from "macro-level early warning" to "micro-level precise prevention and control," significantly improving the level of public safety management along the coast.

[0058] This invention proposes a closed-loop approach of "sputter monitoring-driven model inversion calibration." The system periodically compares predicted and actual measured sputtering data, automatically initiating inversion analysis upon detecting discrepancies. This analysis, based on optimization methods such as genetic algorithms, intelligently diagnoses the main environmental factors causing the deviations, namely riverbed topography changes (ΔZ) and the distribution of obstacles on the river surface (Ω), and dynamically updates the digital twin model and prediction parameters accordingly. This mechanism enables the system to adapt to dynamic changes in the river environment, continuously maintaining high-precision prediction capabilities and addressing the industry pain point of traditional models experiencing accuracy degradation due to environmental changes.

[0059] The system of this invention is not only a predictive tool but also a powerful diagnostic tool. Through unique inversion analysis, it can "see through the phenomenon to the essence," clearly revealing underwater topographic siltation / erosion and potential obstacles on the river surface in a three-dimensional scene in a visual manner (such as color mapping and warning models). This provides hydrological and waterway departments with unprecedented data insights and scientific decision-making basis for river health assessment, precise dredging, and navigation safety management, upgrading business management from "experience-driven" to "data-driven."

[0060] This invention highly integrates multiple functional modules, including multi-source data fusion, hydrodynamic prediction, dynamic visualization, safety early warning, and model self-calibration, into a unified digital twin framework, forming a complete technical closed loop of data-driven, model simulation, visual feedback, and intelligent decision-making. Simultaneously, the system supports interactive simulations by users modifying boundary conditions, meeting the analysis and pre-simulation needs under different scenarios and significantly enhancing its practical value in complex scenarios such as emergency plan development and water conservancy project planning. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the dynamic three-dimensional visualization method for multi-source surveying and mapping data based on digital twins according to the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0063] Example

[0064] This invention relates to a dynamic 3D visualization system for multi-source surveying and mapping data based on digital twins, comprising:

[0065] The multi-source data acquisition and fusion module is used to acquire and fuse multi-source mapping data to generate a three-dimensional topographic model integrating land and water features.

[0066] The hydrological dynamic prediction model module is used to perform numerical simulation and prediction based on hydrological and meteorological data, and output the prediction results.

[0067] The hydrological dynamic prediction model module includes a splash prediction sub-model. This sub-model uses the flow velocity U and water depth H at the bank's leading edge, calculated by the hydrological model, as its main input parameters, and estimates the splash height H_splash and length L_splash based on the following set of empirical formulas:

[0068] H_splash=k1*U² / (2g)*sin²(θ)

[0069] L_splash=k2*(U²*sin(2θ)) / g

[0070] Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness;

[0071] A digital twin engine module is used to construct a 3D scene and dynamically map the prediction results to the 3D scene;

[0072] The dynamic visualization and interactive simulation module is used to display dynamic 3D scenes and receive user simulation parameters to trigger recalculation and visualization updates;

[0073] The data storage and management module is used to store and manage system data;

[0074] The early warning module is used to trigger an alarm when the predicted tidal splash parameters exceed a preset threshold.

[0075] The model self-calibration and inversion module is used to periodically acquire actual tidal splash data, compare it with pre-simulation data, and update the parameters of the 3D model and the prediction model through inversion analysis.

[0076] The model self-calibration and inversion module includes an inversion analysis unit, which is configured to perform the following operations:

[0077] a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω;

[0078] b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F;

[0079] c. Use the obtained solution as the inverse result of changes in the riverbed and river surface obstacles.

[0080] The dynamic visualization and interactive simulation module can automatically delineate and display safe and dangerous areas in a three-dimensional scene based on the predicted sputtering height and length. The dangerous areas are highlighted with a first preset color, while the safe areas are distinguished by a second preset color.

[0081] The model self-calibration and inversion module also includes:

[0082] The real-time monitoring unit is used to periodically acquire actual tidal splash data measured by laser rangefinders or image recognition cameras set up along the coast.

[0083] The data comparison unit is used to calculate the deviation between the actual measurement data and the pre-simulation data at the same time point, and to trigger the inversion analysis unit when the deviation value exceeds the allowable range.

[0084] The model update unit is used to feed back ΔZ and Ω obtained from the inversion analysis unit to the digital twin engine module and the hydrological dynamic prediction model module, and dynamically update the riverbed topography model and river surface obstruction model in the 3D scene as well as optimize the model parameters.

[0085] The digital twin engine module can display the changes in river surface obstacles and riverbed topography, which are inverted by the model self-calibration and inversion module, in a real-time and visual way in a three-dimensional view interface; the riverbed elevation change ΔZ is displayed by overlaying a color mapping map, and the distribution of river surface obstacles Ω is displayed by loading a semi-transparent three-dimensional warning model.

[0086] The early warning module can trigger alarms in at least one of the following ways: triggering on-site audible and visual alarms, sending early warning text messages to a preset list, issuing voice reminders through a public address system, or pushing pop-up warnings within the application interface.

[0087] The empirical coefficients k1 and k2 were determined by regression analysis and fitting of actual splash data measured during historical tides with the corresponding flow velocity U, water depth H, and bank slope angle θ.

[0088] In the inversion analysis unit, the riverbed elevation change ΔZ is encoded using real number encoding, and the presence or absence of obstacles Ω on the river surface is encoded using binary encoding.

[0089] Specifically: Multi-source data acquisition and fusion module: This module can utilize open-source libraries (such as GDAL, PDAL) or commercial software (such as ArcGIS, ENVI) for data processing. Specifically, laser point cloud data and UAV oblique photogrammetry models are finely registered using the Iterative Closest Point (ICP) algorithm to eliminate coordinate deviations between data; satellite remote sensing imagery is registered using SIFT or ORB feature point matching algorithms, and after orthorectification to eliminate terrain and viewpoint distortions, it is fused with the 3D model as a texture map; underwater terrain data is smoothly stitched with the land elevation model at the horizontal plane using a triangular mesh-based algorithm to ensure a natural transition between land and water terrain. Finally, a high-precision 3D terrain model integrating land and water is generated in the WGS84 coordinate system, with an accuracy better than 0.1 meters.

[0090] Hydrological dynamic prediction model module: This module can integrate the computing kernel of commercial software (such as MIKE 21 FM, Delft3D) or run a self-developed numerical model based on the finite volume method to solve two-dimensional shallow water equations. The spatial step size is set to 5 meters, and the time step size adopts an adaptive strategy.

[0091] Splash prediction sub-model: A simplified physical model based on energy conservation and projectile motion is established. This model uses the flow velocity U at the bankfront and water depth H, calculated from the hydrological model, as the main input parameters. The splash height H_splash and length L_splash can be initially estimated using the following set of empirical formulas:

[0092] H_splash=k1* U^2 / (2g)*sin²(θ)

[0093] L_splash=k2*(U²*sin(2θ)) / g

[0094] Where g is the acceleration due to gravity, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to bank roughness and water flow turbulence intensity, determined by fitting historical observation data. In this embodiment, the values ​​of k1 and k2 are determined by regression analysis of historical data from 10 tidal cycles in the Laoyancang section of the Qiantang River. During 3D visualization, the digital twin engine instantiates a particle emitter at the corresponding bank location based on the values ​​of H_splash and L_splash. Particles are ejected with estimated initial velocities and angles, thus visually simulating the splashing effect.

[0095] Model self-calibration and inversion module: find the most likely riverbed change ΔZ and river surface obstacle distribution Ω, so that the difference between the model-predicted splash value M_pred(ΔZ,Ω) and the actual measured value M_meas is minimized.

[0096] One specific implementation method is as follows: Define the objective function: Using the least squares method, define the objective function as F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where i represents different monitoring points and time steps.

[0097] Optimization algorithm selection: A genetic algorithm was used as the optimizer. The elevation variation ΔZ of the riverbed grid points to be inverted and the preset potential obstacle location and shape parameters were encoded into chromosomes.

[0098] Perform the inversion process:

[0099] Initialization: Randomly generate an initial population to represent different riverbed and obstacle assumptions.

[0100] Forward modeling: For each individual in the population, the hydrological dynamic prediction model module (including the sputtering prediction sub-model) is called to perform a fast simulation to obtain the predicted sputtering data M_pred.

[0101] Fitness assessment: Calculate the objective function value F for each individual. Fitness can be set as 1 / F. The smaller the F value, the higher the fitness.

[0102] Selection, crossover, and mutation: Selection is based on fitness, and a new generation of population is generated through crossover and mutation operations.

[0103] Iteration: Repeat steps 2-4 until the maximum number of iterations is reached or the objective function value converges below the threshold.

[0104] Output results: Decoding the individual with the highest fitness in the final generation yields the inverted riverbed elevation change field ΔZ and the river surface obstacle distribution Ω.

[0105] The digital twin engine module visualizes the inversion results in real time. Specifically, for the inverted riverbed change ΔZ, a color map can be overlaid on the original riverbed in the 3D scene, for example, using blue to represent siltation and red to represent scour; for the inverted river surface obstacle Ω, a semi-transparent warning 3D model (such as a cube or ship hull) can be loaded at the corresponding location. This allows hydrological workers to clearly identify abnormal areas in the river channel, providing precise decision support for dredging, clearance, and navigation safety management.

[0106] Take the digital twin system of the Qiantang River tidal bore as an example.

[0107] Initial Modeling and Prediction / Early Warning: The system first integrates recently acquired airborne LiDAR point cloud data (point density > 16 points / square meter), UAV oblique photogrammetry model (resolution 0.03 meters), and multibeam bathymetry data (point spacing 2 meters) to generate a centimeter-level precision 3D topographic model of the lower reaches of the Qiantang River. 24 hours before the spring tide, the system incorporates meteorological forecast data, predicting a force 6 easterly wind. Based on this condition, the hydrological dynamic prediction model calculates and predicts a powerful impact current with a velocity U = 4.2 m / s and a depth H = 3.1 m in the Laoyancang backflow tidal area. The splash prediction sub-model calculates, based on the aforementioned formula, that a giant wave with a height H_splash = 7.8 meters and a length L_splash = 52.3 meters will be generated at this location.

[0108] The digital twin engine dynamically simulates this process in a 3D scene: the water surface rises dynamically according to the prediction results, and when it hits the bank, white particle effects are generated at the corresponding positions to simulate splashing water. At the same time, based on L_splash=52.3 meters, the system automatically delineates a red semi-transparent area within 52.3 meters of the bank in the 3D scene as a danger zone, and delineates a green area outside as a safe zone.

[0109] The early warning module detected that the predicted splash height of 7.8 meters exceeded the preset safety threshold of 6.0 meters. It immediately sent an early warning text message to the mobile phones of tourists who had entered the signal range of the base station in the danger zone through the integrated SMS gateway: "Emergency warning: It is expected that there will be an 8-meter high giant wave splash at the Laoyancang tide viewing point at 15:20 today. You have entered the danger zone. Please evacuate to the safe zone immediately!"

[0110] Self-calibration and inversion: After the tidal process ended, the actual splash data obtained by the system through the laser rangefinder deployed on the shore showed that the maximum splash length was only 45.1 meters, which was significantly different from the predicted value of 52.3 meters (>13%). The data comparison unit determined that the deviation exceeded the limit and then triggered the inversion analysis unit to start the above-mentioned genetic algorithm inversion process.

[0111] After 150 iterations of calculation, the algorithm converged. The inversion results showed that there was a sandbar (ΔZ) about 55 meters long and with an average siltation height of 0.6 meters, located 80 meters from the bank in the river center. This sandbar altered the water flow structure and weakened the impact energy on the bank. The model update unit then rendered the riverbed in this area as dark blue in the 3D scene based on the inverted ΔZ data and labeled it "Inferred Siltation Area (Inversion Result)". At the same time, the system automatically generated a diagnostic report, clearly stating that "the siltation body in the river center is the main reason for the weakened measured splash, and it is recommended to conduct underwater topographic verification."

[0112] Three days later, based on the visualization results, the hydrological department dispatched a survey vessel to verify the marked area using a single-beam echo sounder. This confirmed the existence of a sandbar with an average siltation height of 0.55 meters, highly consistent with the inversion results, thus providing a precise target area for subsequent dredging operations. The system then used this verified data to automatically update the 3D topographic model and fine-tune the empirical coefficient k2 (from 0.72 to 0.68), thereby completing this self-calibration loop and improving the accuracy of subsequent predictions.

[0113] like Figure 1 As shown: A dynamic 3D visualization method for multi-source mapping data based on digital twins includes the following steps:

[0114] S1: Acquire and process multi-source mapping data to generate a high-precision integrated three-dimensional topographic model of land and water.

[0115] S2: Based on the input hydrological and meteorological data, perform numerical simulation and prediction calculations of tidal dynamics and coastal splash;

[0116] The prediction of sputtering uses the frontal velocity U and water depth H as the main input parameters, and estimates the sputtering height H_splash and length L_splash based on the following set of empirical formulas:

[0117] H_splash=k1* U² / (2g)*sin²(θ)

[0118] L_splash=k2*(U²*sin(2θ)) / g

[0119] Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness;

[0120] Generate a dataset of prediction results;

[0121] S3: Load the three-dimensional terrain model and construct the basic three-dimensional scene;

[0122] S4: The predicted result dataset is mapped to the three-dimensional scene in real time, driving the dynamic changes of the visualization elements to form a dynamic three-dimensional visualization of hydrological tides, and automatically delineating and displaying safe and dangerous areas.

[0123] S5: Display the dynamic 3D visualization to the user;

[0124] S6: Monitor prediction results and issue an alert when sputtering exceeds limits;

[0125] S7: Model self-calibration steps: periodically acquire actual sputtering data and compare it with the pre-simulation data; if the deviation is too large, perform inversion analysis;

[0126] The inversion analysis includes:

[0127] a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω;

[0128] b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F;

[0129] c. Based on the obtained solution, update the parameters of the 3D model and the prediction model to complete the calibration;

[0130] S8: Receive the deduction parameters input by the user, and re-execute S2 to S5 based on the new parameters to realize interactive dynamic deduction.

[0131] In step S4, the elevation of the water surface mesh is dynamically adjusted based on the water level data using vertex shader technology, and the splashing effect is simulated using a particle system.

[0132] In step S7, after calibration is completed, a diagnostic report containing the inverted changes in riverbed and surface obstacles is generated to support hydrological management decisions.

[0133] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0134] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0135] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0138] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0140] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0142] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic 3D visualization system for multi-source surveying and mapping data based on digital twins, characterized in that, include: The multi-source data acquisition and fusion module is used to acquire and fuse multi-source mapping data to generate a three-dimensional topographic model integrating land and water features. The hydrological dynamic prediction model module is used to perform numerical simulation and prediction based on hydrological and meteorological data, and output the prediction results. The hydrological dynamic prediction model module includes a splash prediction sub-model. This sub-model uses the flow velocity U and water depth H at the bank's leading edge, calculated by the hydrological model, as its main input parameters, and estimates the splash height H_splash and length L_splash based on the following set of empirical formulas: H_splash=k1*U² / (2g)*sin²(θ) L_splash=k2*(U²*sin(2θ)) / g Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness; A digital twin engine module is used to construct a 3D scene and dynamically map the prediction results to the 3D scene; The dynamic visualization and interactive simulation module is used to display dynamic 3D scenes and receive user simulation parameters to trigger recalculation and visualization updates; The data storage and management module is used to store and manage system data; The early warning module is used to trigger an alarm when the predicted tidal splash parameters exceed a preset threshold. The model self-calibration and inversion module is used to periodically acquire actual tidal splash data, compare it with pre-simulation data, and update the parameters of the 3D model and the prediction model through inversion analysis. The model self-calibration and inversion module includes an inversion analysis unit, which is configured to perform the following operations: a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω; b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F; c. Use the obtained solution as the inverse result of changes in the riverbed and river surface obstacles.

2. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, The dynamic visualization and interactive simulation module can automatically delineate and display safe and dangerous areas in a three-dimensional scene based on the predicted sputtering height and length. The dangerous areas are highlighted with a first preset color, while the safe areas are distinguished by a second preset color.

3. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, The model self-calibration and inversion module also includes: The real-time monitoring unit is used to periodically acquire actual tidal splash data measured by laser rangefinders or image recognition cameras set up along the coast. The data comparison unit is used to calculate the deviation between the actual measurement data and the pre-simulation data at the same time point, and to trigger the inversion analysis unit when the deviation value exceeds the allowable range. The model update unit is used to feed back ΔZ and Ω obtained from the inversion analysis unit to the digital twin engine module and the hydrological dynamic prediction model module, and dynamically update the riverbed topography model and river surface obstruction model in the 3D scene as well as optimize the model parameters.

4. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, The digital twin engine module can display the changes in river surface obstacles and riverbed topography, which are inverted by the model self-calibration and inversion module, in a real-time and visual way in a three-dimensional view interface; the riverbed elevation change ΔZ is displayed by overlaying a color mapping map, and the distribution of river surface obstacles Ω is displayed by loading a semi-transparent three-dimensional warning model.

5. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, The early warning module can trigger alarms in at least one of the following ways: triggering on-site audible and visual alarms, sending early warning text messages to a preset list, issuing voice reminders through a public address system, or pushing pop-up warnings within the application interface.

6. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, The empirical coefficients k1 and k2 were determined by regression analysis and fitting of actual splash data measured during historical tides with the corresponding flow velocity U, water depth H, and bank slope angle θ.

7. The dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins according to claim 1, characterized in that, In the inversion analysis unit, the riverbed elevation change ΔZ is encoded using real number encoding, and the presence or absence of obstacles Ω on the river surface is encoded using binary encoding.

8. A method for dynamic three-dimensional visualization of multi-source surveying and mapping data based on digital twins, applied to the dynamic three-dimensional visualization system for multi-source surveying and mapping data based on digital twins as described in any one of claims 1-7, characterized in that, Includes the following steps: S1: Acquire and process multi-source mapping data to generate a high-precision integrated three-dimensional topographic model of land and water. S2: Based on the input hydrological and meteorological data, perform numerical simulation and prediction calculations of tidal dynamics and coastal splash; The prediction of sputtering uses the frontal velocity U and water depth H as the main input parameters, and estimates the sputtering height H_splash and length L_splash based on the following set of empirical formulas: H_splash=k1* U² / (2g)*sin²(θ) L_splash=k2*(U²*sin(2θ)) / g Where g is the gravitational acceleration, θ is the bank slope angle, and k1 and k2 are empirical coefficients related to the bank roughness; Generate a dataset of prediction results; S3: Load the three-dimensional terrain model and construct the basic three-dimensional scene; S4: The predicted result dataset is mapped to the three-dimensional scene in real time, driving the dynamic changes of the visualization elements to form a dynamic three-dimensional visualization of hydrological tides, and automatically delineating and displaying safe and dangerous areas. S5: Display the dynamic 3D visualization to the user; S6: Monitor prediction results and issue an alert when sputtering exceeds limits; S7: Model self-calibration steps: periodically acquire actual sputtering data and compare it with the pre-simulation data; if the deviation is too large, perform inversion analysis; The inversion analysis includes: a. Taking the actual measured sputtering data M_meas as the target, define the objective function F(ΔZ,Ω)=Σ[M_pred_i(ΔZ,Ω)-M_meas_i]², where M_pred_i(ΔZ,Ω) is the predicted sputtering data based on the riverbed elevation change ΔZ and the distribution of obstacles on the river surface Ω; b. A genetic algorithm is used as the optimizer to encode ΔZ and Ω to generate a population, and selection, crossover and mutation operations are performed iteratively to find the solution of ΔZ and Ω that minimizes the objective function F; c. Based on the obtained solution, update the parameters of the 3D model and the prediction model to complete the calibration; S8: Receive the deduction parameters input by the user, and re-execute S2 to S5 based on the new parameters to realize interactive dynamic deduction.

9. The method for dynamic three-dimensional visualization of multi-source mapping data based on digital twins according to claim 8, characterized in that, In step S4, the elevation of the water surface mesh is dynamically adjusted based on the water level data using vertex shader technology, and the splashing effect is simulated using a particle system.

10. The method for dynamic three-dimensional visualization of multi-source mapping data based on digital twins according to claim 8, characterized in that, In step S7, after calibration is completed, a diagnostic report containing the inverted changes in riverbed and surface obstacles is generated to support hydrological management decisions.

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