Digital twin automated construction method for water conservancy projects

Abstract

The present application relates to the technical field of digital twinning of hydraulic engineering, and more particularly to a digital twinning automatic construction method for hydraulic engineering. The method comprises the following steps: partition monitoring and multi-level sampling of the hydraulic engineering construction area to obtain a multi-level hydrodynamic data set; constructing a hydrodynamic vector field using the multi-level hydrodynamic data set; constructing a triangular monitoring area through the hydrodynamic vector field; calculating the area average vorticity of the triangular monitoring area and analyzing the flow velocity vector at the three corner vertices of the triangular monitoring area to obtain a local vorticity distribution map; determining the shear stress distribution map around the construction equipment according to the local vorticity distribution map; determining the influence range on the construction equipment according to the shear stress distribution map to form a multi-parameter influence radius table. The present application realizes real-time two-way coupling analysis of the hydrodynamic environment and construction activities through digital twinning technology of hydraulic engineering, providing efficient and accurate technical support for automatic construction of hydraulic engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning of water conservancy projects, and particularly relates to a digital twinning automatic construction method for water conservancy projects. BACKGROUND

[0002] The existing digital twinning technology of water conservancy projects has a serious computational efficiency bottleneck in the expression of hydrodynamic environment; traditional digital twinning systems rely on complex CFD models for hydrodynamic analysis, which are usually based on finite element or finite volume methods, require the construction of high-precision three-dimensional grids and the solution of complete Navier-Stokes equations, and the single analysis calculation time is as long as several hours to several days; in addition, these models require high-performance computing clusters for support, which are difficult to deploy in the construction site environment; more importantly, traditional CFD calculations have high requirements for the gridding of input data, while actual engineering monitoring networks are often unevenly distributed and sparse, making it difficult to balance model accuracy and computational efficiency, and unable to meet the real-time decision-making needs of the construction site.

[0003] The existing digital twinning system of water conservancy projects generally has a "virtual-real separation" problem, and there is a lack of effective two-way interaction mechanism between the virtual model and the physical entity; in the construction process, traditional models only consider the one-way influence of the water environment on construction activities, while ignoring the reverse effect of the continuous change of the water dynamic environment caused by construction activities; the existing system cannot capture and simulate the changes in water flow, vortex generation and shear stress distribution caused by construction in real time; there is a lack of quantitative construction disturbance propagation model, which cannot predict the chain effect of a construction activity on the water dynamic conditions of the adjacent area; this one-way consideration makes the digital twinning system unable to truly reflect the complex interaction of the physical world, making the construction risk assessment severely insufficient, especially under complex hydrological conditions with multiple devices working simultaneously.

[0004] In summary, the existing digital twinning technology of water conservancy projects has the core problems of low computational efficiency of hydrodynamic environment, lack of virtual-real interaction mechanism, and inaccurate construction-water environment two-way coupling prediction, which need to be solved urgently. SUMMARY

[0005] Therefore, it is necessary to provide a digital twinning automatic construction method for water conservancy projects to solve at least one of the above technical problems.

[0006] To achieve the above purpose, a digital twinning automatic construction method for water conservancy projects comprises the following steps:

[0007] Step S1: performing zoned monitoring and multi-level sampling on the water conservancy construction area on the digital twinning platform to obtain a multi-level hydrodynamic data set; and constructing a hydrodynamic vector field using the multi-level hydrodynamic data set;

[0008] Step S2: Construct a triangular monitoring area through a water power vector field; perform regional average vorticity calculation on the triangular monitoring area, and analyze the flow velocity vector at the three corner vertices of the triangular monitoring area to obtain a local vorticity distribution map;

[0009] Step S3: Determine a shear stress distribution map around the construction equipment according to the local vorticity distribution map; determine an influence range on the construction equipment according to the shear stress distribution map, form a multi-parameter influence radius table, and construct a water power influence domain map in a digital twin environment;

[0010] Step S4: Construct a set of along-path water power curves of the construction planning path using a set of water power characteristics of the water power influence domain map; analyze the time variation characteristics of the set of along-path water power curves, and perform construction-water power interaction prediction to obtain a construction interaction influence map; construct a safe operation window map according to the construction interaction influence map;

[0011] Step S5: Determine the execution time and area of each type of construction operation according to the safe operation window map, and generate a set of construction control instructions; perform error prediction and adjustment on the set of construction control instructions to obtain an adjusted construction parameter table.

[0012] The present application realizes optimized and efficient data acquisition. Through zoned monitoring and multi-level sampling, the monitoring resources are reasonably configured, the data accuracy in key areas is ensured, and data redundancy is avoided, providing a high-quality data basis for subsequent rapid analysis. The "three-point method" vorticity rapid calculation technology significantly improves the efficiency of water power analysis. This method replaces complex CFD solving with direct algebraic operation, reducing the calculation amount by about 85%, realizing lightweight real-time calculation based on a sparse monitoring network, and solving the efficiency bottleneck of traditional technology.

[0013] The complex water power analysis results are converted into intuitive and quantifiable risk assessment tools. By introducing the concept of "parameter influence radius" and constructing a water power influence domain map, the multi-dimensional water power risk is visualized, providing clear and accurate decision-making basis for on-site construction. A two-way coupling mechanism between construction and water environment is established, realizing the transition from passive monitoring to active prediction. Through the "point-line-surface" analysis method and the construction disturbance propagation function, this method can quantitatively predict the reverse influence of construction activities on the water environment, and generate a safe operation window map, effectively solving the "virtual-real separation" problem and realizing active risk avoidance.

[0014] An adaptive closed-loop control system of "monitoring-prediction-decision-correction" is constructed, realizing the automation and intelligentization of construction. This system dynamically adjusts the construction parameters through error analysis, ensuring that the construction process can adapt to environmental changes in real time, thereby significantly improving the safety, accuracy and efficiency of construction, and realizing true virtual-real linkage intelligent control.

[0015] In summary, the present application solves the efficiency bottleneck of hydrodynamic calculation and the problem of "virtual-real separation" of construction-water environment by the two core innovations of "three-point method" vorticity rapid calculation and "point-line-surface" analysis method and construction disturbance propagation function. These technologies together form a closed-loop optimization system of "monitoring-calculation-prediction-decision-correction", realizing real-time two-way coupling analysis of hydrodynamic environment and construction activities, and providing efficient and accurate technical support for automatic construction of water conservancy projects. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a step flowchart of a digital twin automatic construction method for water conservancy projects.

[0017] Figure 2 It is a detailed implementation step flowchart of step S2 in the present application. DETAILED DESCRIPTION

[0018] The technical method of the present application will be described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0020] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] To achieve the above-mentioned purpose, please refer to Figures 1-2 The present application provides a digital twin automatic construction method for water conservancy projects, comprising the following steps:

[0022] Step S1: partition monitoring and multi-level sampling of the water conservancy construction area on the digital twin platform to obtain a multi-level hydrodynamic data set; and constructing a hydrodynamic vector field using the multi-level hydrodynamic data set;

[0023] In the embodiment of the present application, the construction area is divided into core area, peripheral area and rapid flow area and a differentiated monitoring network is laid out, different ADCP sampling frequencies are set according to the importance of the area, for example, 2Hz during the day and 1Hz at night in the core area. Three-dimensional flow velocity data is collected by the equipment in 10 measurement layers in the vertical direction, forming a multi-level hydrodynamic data set. The data set is preprocessed by applying 3-sigma criterion and Kalman filter to eliminate outliers and noise, and finally the processed discrete data is converted into a continuous three-dimensional hydrodynamic vector field covering the whole area by using the inverse distance weighted interpolation method.

[0024] Step S2: constructing a triangular monitoring area through the hydrodynamic vector field; calculating the area-averaged vorticity of the triangular monitoring area, and analyzing the flow velocity vector at the three corner vertices of the triangular monitoring area to obtain a local vorticity distribution map;

[0025] In the embodiment of the present application, based on the monitoring points in the hydrodynamic vector field, the optimal triangular monitoring area is constructed by applying the Delaunay triangulation algorithm, and the "three-point method" is used for area-averaged vorticity calculation. This method directly calculates the discrete ring flow by using the coordinates of the three vertices of the triangle and the flow velocity vector, and then divides the vorticity value by twice the area of the triangle to obtain the vorticity value, and assigns the vorticity value to the center monitoring point of the triangle. After completing the calculation of all triangular areas, the discrete vorticity values are spatially smoothed by Kriging interpolation method, and finally a continuous and visual local vorticity distribution map is generated.

[0026] Step S3: determining the shear stress distribution map around the construction equipment according to the local vorticity distribution map; determining the influence range on the construction equipment according to the shear stress distribution map, forming a multi-parameter influence radius table, and constructing a hydrodynamic influence domain map in the digital twin environment;

[0027] In the embodiment of the present application, a local encryption calculation grid is established around the construction equipment, and the first-order central difference method is used to calculate the velocity gradient based on the hydrodynamic vector field, so as to obtain the shear stress distribution map around the equipment. Then, the concept of "parameter influence radius" is introduced to quantify the risk range, which specifically includes: calculating the flow velocity influence radius ; determining the vortex core diameter D by "ray detection method" and calculating the vortex influence radius ; and calibrating the continuous area with shear stress value exceeding 0.15 N / m² These three influencing parameters are integrated to form a multi-parameter influence radius table, which is then mapped onto a digital twin environment. Through parameter overlay and risk level classification, a comprehensive hydrodynamic influence domain map that intuitively displays the three-level risk zones is finally constructed.

[0028] Step S4: Construct a set of hydrodynamic curves along the construction planning path using the hydrodynamic feature set of the hydrodynamic influence domain map; analyze the time variation characteristics of the set of hydrodynamic curves along the path, and predict the interaction between construction and hydrodynamics to obtain a construction interaction influence map; construct a safe operation window map based on the construction interaction influence map.

[0029] In this embodiment of the invention, hydrodynamic features are extracted every 10m along the planned construction path, and a set of continuously changing hydrodynamic curves along the path is constructed using cubic spline interpolation. The temporal variation characteristics of the curve set are analyzed in conjunction with historical data to identify tidal cycle patterns and parameter abrupt events. Subsequently, a "construction disturbance propagation function" is constructed to quantify the impact of activities such as dredging and pile driving on the water environment, and the propagation patterns and intensity of these disturbances in a two-dimensional plane are predicted on a digital twin platform, generating a construction interaction impact map. Finally, combining temporal characteristics and interaction impact prediction, a safe operation window map is constructed, using red, yellow, and green colors to visually display the construction risk levels for each time period and area within the next 48 hours.

[0030] Step S5: Determine the execution time and area of ​​various construction operations based on the safety operation window diagram, and generate a construction control instruction set; perform error prediction and adjustment on the construction control instruction set to obtain the adjusted construction parameter table;

[0031] In this embodiment of the invention, based on the green safety area in the safety operation window diagram, the specific execution time and spatial layout of various construction operations are determined, a construction spatiotemporal planning table is generated, and it is converted into a structured construction control instruction set for execution. During construction, the on-site monitoring data and the predicted values ​​of the digital twin platform are compared in real time for error analysis. When the error exceeds 15%, a correction mechanism is triggered. Based on the error analysis report, the system automatically adjusts key construction parameters, such as reducing the operation rate, and issues the updated parameters, forming a virtual-real closed-loop adaptive control from virtual prediction to physical execution and then to feedback correction, ultimately obtaining the adjusted construction parameter table.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Divide the construction monitoring area into regions and deploy a differentiated monitoring network to obtain a partitioned monitoring point configuration table;

[0034] Step S12: Set the sampling frequency configuration table according to the zone monitoring point configuration table;

[0035] Step S13: According to the sampling frequency configuration table, the horizontal and vertical flow velocity components are measured simultaneously, and according to the water depth, the vertical direction is equally divided into levels for collection, forming a multi-level hydrodynamic data set;

[0036] Step S14: The multi-level hydrodynamic data set is subjected to hydrodynamic noise identification and filtering to obtain a filtered hydrodynamic data set;

[0037] Step S15: The filtered hydrodynamic data set is used to construct a hydrodynamic vector field.

[0038] In the embodiment of the application, the digital twin model of the water conservancy construction area is partitioned. The core area where the construction equipment is concentrated is divided into a range of 500m*500m, and a monitoring grid of 10m*10m is established, and a monitoring point is arranged at each grid intersection. For the surrounding area outside the core area, a monitoring grid of 50m*50m is established, and a monitoring point is arranged at the grid intersection. In the rapid flow area where the flow velocity exceeds 1.5m / s, three additional monitoring points are added. Record the three-dimensional coordinates of all monitoring points to form a partitioned monitoring point configuration table, as shown in Table 1.

[0039] Table 1

[0040] Monitoring Point ID Zone Type X Coordinate (m) Y Coordinate (m) Z Coordinate (m) Notes C001 Core Zone 100.0 100.0 -5.0 Grid Intersection (10x10) C002 Core Zone 110.0 100.0 -5.0 Grid Intersection (10x10) C003 Core Zone 120.0 100.0 -5.0 Grid Intersection (10x10) P001 Peripheral Zone 550.0 100.0 -6.0 Grid Intersection (50x50) P002 Peripheral Zone 600.0 100.0 -6.2 Grid Intersection (50x50) R001 Rip Zone 300.0 450.0 -4.0 Additional Monitoring Points R002 Rip Zone 320.0 470.0 -4.2 Additional Monitoring Points R003 Rip Zone 340.0 490.0 -4.5 Additional Monitoring Points

[0041] According to the partitioned monitoring point configuration table, the ADCP acoustic Doppler current meter in different areas is set to have different sampling frequencies. The sampling frequency of the monitoring points in the core area is set to 2Hz during the daytime construction period (7:00-19:00) and 1Hz during the night period (19:00-7:00 the next day). The sampling frequency of the monitoring points in the surrounding area is set to 0.5Hz. The sampling frequency of the additional monitoring points in the rapid flow area is set to 4Hz. These settings together constitute a sampling frequency configuration table, as shown in Table 2.

[0042] Table 2

[0043] Zone Type Time Period Sampling Frequency (Hz) Notes Core Zone Daytime (7:00-19:00) 2.0 Period of High Construction Activity Core Zone Nighttime (19:00-Next Day 7:00) 1.0 Period of Reduced Construction Activity Peripheral Zone All Day 0.5 Constant Sampling Frequency Rip Zone All Day 4.0 High-Frequency Monitoring Zone

[0044] Start the ADCP equipment of all monitoring points and collect data according to the sampling frequency configuration table. The equipment simultaneously measures the horizontal flow velocity component , and the vertical flow velocity component . For water depth , divide the vertical direction into 10 measurement layers from the bottom to the surface, each layer with a thickness of 0.1x . Collect three-dimensional flow velocity vector data at the center point of each measurement layer to form a multi-level hydrodynamic data set containing spatial coordinates, time stamps and multi-level flow velocity components.

[0045] Standardized preprocessing is performed on the multi-level hydrodynamic data set. First, the 3-sigma criterion is applied to identify and remove data points that are outside the range of the average value plus or minus three times the standard deviation. Subsequently, the Kalman filter algorithm is applied to the time series data after removing outliers to smooth random noise introduced by the device itself or high-frequency environmental disturbances. After this process, the filtered hydrodynamic data set is obtained.

[0046] For each measurement layer, a continuous two-dimensional hydrodynamic vector field is generated using the discrete monitoring point data in the filtered hydrodynamic data set through inverse distance weighted interpolation. A regular virtual calculation grid is laid out across the entire construction area with a grid resolution of 2m x 2m. For each grid node, the weighted average flow velocity is calculated based on the distance from the surrounding monitoring points to obtain the flow velocity vector of the node . The two-dimensional vector fields of all measurement layers are integrated into three dimensions to ultimately construct a hydrodynamic vector field that can describe the three-dimensional flow pattern of the entire construction water area.

[0047] As an example of the present application, referring to FIG. 1, in this example, the step S2 includes: Figure 2

[0048] Step S21: Extracting key monitoring points from the hydrodynamic vector field; constructing a three-point combination table by triangulating the key monitoring points;

[0049] Step S22: Iterating through the three-point combination table to extract a triangular element calculation data set from the hydrodynamic vector field;

[0050] Step S23: Calculating the geometric properties of the triangular element calculation data set to obtain a geometric property-attached triangular element set;

[0051] Step S24: For each triangular element in the geometric property-attached triangular element set, using its vertex coordinates and flow velocity vector, performing discrete ring flow calculation to obtain a ring flow list for the element;

[0052] Step S25: For each triangular element in the ring flow list, extracting its calculated ring flow and area, calculating the average vorticity and assigning the center point vorticity to obtain a discrete point vorticity assignment set;

[0053] Step S26: Spatially visualizing the vorticity field of the discrete point vorticity assignment set to obtain a local vorticity distribution map.

[0054] In the embodiment of the present application, each record in the three-point combination table is iterated. According to the three vertex indices in the record, the two-dimensional coordinates of the three points , , and their corresponding horizontal flow velocity vectors ​, , These coordinates and flow velocity information are integrated into a structured dataset containing all the basic computational units, i.e. the triangular element computation dataset.

[0055] Each triangular element in the triangular element computation dataset is processed. With the coordinates of its three vertices , , The exact area of the triangle is calculated by the determinant method, and the formula is: Area = ½ | (x1y2-x2y1) + (x2y3-x3y2) + (x3y1-x1y3) |. The calculated area value is attached to the data entry of the corresponding triangular element, and the triangular element set with geometric attributes is generated.

[0056] The discrete line integral of the flow velocity along the boundary of the triangle, i.e. the ring flow, is directly calculated by algebraic operation. The triangular element set with geometric attributes is traversed, and for each element, the vertex coordinates and flow velocity vector are used to execute the ring flow calculation formula: Ring flow = ½ | (u1y2-u2y1) + (u2y3-u3y2) + (u3y1-u1y3) |. wherein is the vertex coordinate, is the corresponding flow velocity component. The calculation result forms the element ring flow list.

[0057] The element ring flow list is traversed to complete the final vorticity calculation and assignment. For each triangular element, its calculated ring flow and area are extracted. According to the discrete form of Stokes theorem, the average vorticity of the element is calculated: Vorticity ω = Ring flow / (2 × Area). The calculated vorticity value ω is assigned to the corresponding center point of the triangular element in the three-point combination table. Finally, a discrete point vorticity assignment set containing the positions of all key monitoring points and their corresponding vorticity values is obtained.

[0058] To convert the discrete vorticity data into a continuous distribution map, the Kriging interpolation method is used. On a regular grid with a resolution of 1m × 1m covering the entire construction area, the discrete point vorticity assignment set is used as input to estimate the vorticity values of all grid points. Finally, according to the size and sign of the vorticity value, each grid point is rendered with a color, with blue for positive vorticity (counterclockwise rotation) and red for negative vorticity (clockwise rotation), and the color depth represents the vorticity intensity, thus generating a local vorticity distribution map.

[0059] Preferably, step S24 comprises:

[0060] The ring flow calculation data is obtained by performing a ring flow theory-based transformation on the triangular element set with geometric attributes;

[0061] The boundary flow velocity feature table is obtained by performing triangular boundary vector decomposition on the ring flow calculation data;

[0062] The boundary ring flow contribution set is obtained by performing boundary contribution calculation according to the boundary flow velocity feature table;

[0063] The algebraic formula derivation and simplification are performed on the boundary circulation contribution set to obtain an algebraic calculation scheme of the circulation;

[0064] The circulation calculation optimization is performed according to the algebraic calculation scheme of the circulation to obtain a circulation value set;

[0065] The circulation data standardization is performed on the circulation value set to obtain a unit circulation quantization list.

[0066] In the embodiment of the application, the circulation theoretical basis transformation is performed on the triangular element set with geometric properties, and the vorticity calculation problem is transformed into the solution of the discrete line integral of the flow velocity vector along the closed boundary of the triangle, that is, the circulation. For each triangular element, a counterclockwise closed path is established with three vertices of the triangular element as nodes . All the vertex coordinates and flow velocity vectors required for calculation are organized to form circulation calculation data.

[0067] For each triangular element in the circulation calculation data, vectorization processing is performed on the three boundaries of the triangular element. The displacement vector of each boundary is calculated, for example . At the same time, the average flow velocity vector along the boundary is calculated, for example . The displacement vectors and average flow velocity vectors of all the boundaries are summarized to form a boundary flow velocity feature table.

[0068] According to the boundary flow velocity feature table, the contribution of each boundary to the total circulation is calculated. The contribution is approximately obtained through the dot product of the average flow velocity vector and the displacement vector, for example, the contribution of the boundary is . The contributions of the three boundaries are calculated, and these scalar values are collected into a boundary circulation contribution set.

[0069] The calculation of the circulation is transformed from step-by-step summation into single-step algebraic operation. The total circulation expression is fully expanded, and through algebraic merging and reorganization, the intermediate variables are eliminated, and finally a simplified formula only depending on the three vertex coordinates and the flow velocity components is derived. The final form of the algebraic calculation scheme of the circulation is: circulation , where is the coordinate of the vertex , and is the flow velocity component of the vertex .

[0070] According to the circulation algebraic calculation scheme, calculation is directly performed on each element in the set of triangular elements with geometric attributes. 12 values of the three vertex coordinates and the flow velocity vector of each triangular element are taken as inputs, and the circulation of the element is calculated at one time by substituting the foregoing simplified formula. This method avoids the iterative process of calculating the average flow velocity and the dot product by edge, and realizes calculation optimization. All calculation results constitute a circulation value set.

[0071] The data in the circulation value set is sorted and formatted. Each circulation value is associated with its corresponding triangular element identifier, and is given a physical unit m² / s. These structured information is integrated into a list, where each entry clearly records the circulation result of a triangular element. This list is the element circulation list, which provides direct input for subsequent vorticity assignment.

[0072] Preferably, step S3 comprises the following steps:

[0073] Step S31: obtaining device position information and device characteristic parameters of the construction equipment;

[0074] Step S32: performing shear stress analysis on the periphery of the device according to the device position information and the local vorticity distribution map, to obtain a shear stress distribution map;

[0075] Step S33: quantifying the influence range of the device characteristic parameters on the construction equipment according to the shear stress distribution map, to determine a multi-parameter influence radius table;

[0076] Step S34: mapping each parameter influence radius in the multi-parameter influence radius table to the construction area plan, to construct a water dynamic influence domain map.

[0077] In the embodiment of the application, the center coordinates of the construction caisson are obtained through a global positioning system , and the characteristic parameters thereof are read from a device database, including a characteristic length of 20 m, and a characteristic width of 15 m. Within a range of 10 m x 10 m around the center coordinates of the construction caisson, a local calculation grid with a resolution of 1 m x 1 m is established. The flow velocity vectors of each node on the grid are extracted from the water dynamic vector field. The horizontal velocity gradient and are calculated by using a first-order central difference method. Then, the shear stress value of each grid point is calculated according to the shear stress formula , wherein the water density is taken as , and the water dynamic viscosity is taken as . The calculation results are normalized to generate a shear stress distribution map around the device.

[0078] The flow velocity influence radius is calculated where V is the flow velocity at the location of the device, is the characteristic length of the device. Secondly, the point with the maximum absolute value of vorticity in the local vorticity distribution map is located as the vortex core, and the boundary is detected in eight directions until the vorticity value decays to 20% of the core value, and the average value is calculated to obtain the vortex diameter , and then the vortex influence radius is determined. Thirdly, the shear stress influence area is calibrated, which is defined as the continuous area with all measured values greater than 0.15 N / m² in the shear stress distribution map. Finally, the three influence parameters and their geometric shapes (circular or polygonal) are integrated into a table, and a dynamic mechanism is established to update every 15 minutes, forming a multi-parameter influence radius table.

[0079] The influence radius and influence area in the multi-parameter influence radius table are mapped to the digital twin plan of the construction area. When the influence ranges of different parameters overlap, the maximum influence radius is taken as the comprehensive influence boundary of the area. According to the influence degree, three influence areas are divided: the strong influence area is the area where the influence radii of at least two parameters overlap, the medium influence area is the area covered by the influence radius of a single parameter, and the weak influence area is the edge area of the parameter influence radius. Through the visualization rendering of red, yellow and green three colors to these three areas, the water dynamic influence domain map is finally constructed.

[0080] Preferably, step S33 comprises:

[0081] The current local flow velocity value is extracted from the water dynamic vector field, and the flow velocity influence radius is calibrated by combining the device characteristic parameters to obtain the flow velocity influence radius value;

[0082] The vortex core is located by positioning the vortex core in the local vorticity distribution map;

[0083] The vortex diameter is obtained by performing boundary detection and diameter estimation in eight directions around the vortex core;

[0084] The device characteristic length is extracted from the device characteristic parameters, and the vortex influence radius value is calculated by combining the vortex diameter;

[0085] The high shear stress area boundary is calibrated by using the preset shear stress safety threshold for the shear stress distribution map, and the shear stress influence polygon is obtained;

[0086] The multi-parameter influence information analysis is performed according to the flow velocity influence radius value, the vortex influence radius value and the shear stress influence polygon, and the multi-parameter influence radius table is obtained.

[0087] In the embodiment of the application, the flow velocity value at the center position of the construction device is extracted from the water dynamic vector field The device characteristic length is obtained from the device characteristic parameters The empirical formula is applied wherein is the local flow velocity, in units of m / s; is the device characteristic length, in units of m; and 2.5 is an empirical coefficient. The calculation result is the flow velocity influence radius value of the single, quantified water flow impact effect.

[0088] The local vorticity distribution map is scanned in the whole field to find and lock the point with the maximum absolute value of vorticity |ω|, and the coordinates of the point are defined as the vortex core.

[0089] The "ray detection method" is used to quantify the vortex size. Virtual rays are emitted in eight directions, i.e., east, north, west, south, and four 45-degree angle directions, with the vortex core as the origin. Along each ray, the vorticity value is continuously detected until the point where the vorticity value first decays to 20% of the core vorticity value is found, and the point is recorded as the vortex boundary point. The distance between the opposite boundary point pairs in the eight directions is calculated, and the arithmetic mean value is taken as the equivalent vortex diameter of the vortex .

[0090] The device characteristic length is extracted from the device characteristic parameter table The vortex influence radius value is calculated using the formula wherein is the vortex diameter calculated above. The physical meaning of the formula is that the dangerous range includes the vortex itself and a safety redundancy related to the device size.

[0091] Each grid point in the device peripheral shear stress distribution map is traversed. The shear stress value of each point is compared with the preset shear stress safety threshold value 0.15 N / m². All grid points greater than 0.15 N / m² and spatially connected are collected, the peripheral boundary is outlined, and one or more closed shear stress influence polygons are formed.

[0092] A table containing the fields of "parameter type", "influence geometry type", "influence value", and "update time" is created. The calculated flow velocity influence radius value, vortex influence radius value, and coordinate set of the shear stress influence polygon are sequentially filled into the table to form a structured multi-parameter influence radius table. This table provides a standardized data basis for subsequent construction of a comprehensive influence domain map.

[0093] Preferably, step S4 comprises the following steps:

[0094] Step S41: extracting a set of hydrodynamic characteristics on the path monitoring point of the hydrodynamic influence domain map construction path;

[0095] Step S42: constructing a set of along-path hydrodynamic curves of the construction planning path by using the set of hydrodynamic features;

[0096] Step S43: obtaining and using historical monitoring data to extract the time variation characteristics of the hydrodynamic parameters from the set of along-path hydrodynamic curves to form a hydrodynamic time sequence characteristic table;

[0097] Step S44: performing virtual simulation and construction-hydrodynamic interaction prediction on the digital twin platform according to the hydrodynamic time sequence characteristic table and the set of along-path hydrodynamic curves to obtain a construction interaction influence graph.

[0098] In the embodiment of the application, based on the preset construction planning path, a fixed point is selected as a path monitoring point every 10 m along the path with the starting point of the path as the zero point. The values of three parameters of instantaneous flow rate, vorticity and shear stress are extracted from the bottom layer data of the hydrodynamic influence domain graph at the position of each path monitoring point. A hydrodynamic feature vector is established for each point, which includes the current value, the linear change rate in the past 10 minutes and the average value in the past 24 hours of each parameter. The weight coefficients of the three parameters in the vector are set as follows: the flow rate weight is 0.5, the vorticity weight is 0.3, and the shear stress weight is 0.2. The weighted feature vectors of all path monitoring points together constitute the hydrodynamic feature set.

[0099] The discrete path monitoring point features in the hydrodynamic feature set are connected into continuous parameter variation curves along the construction path through a cubic spline interpolation technique. The interpolation algorithm ensures that the first and second derivatives of the curve at each monitoring point are continuous, thereby smoothly reflecting the spatial gradual variation characteristics of the parameters. Independent along-path variation curves are generated for the three types of parameters of flow rate, vorticity and shear stress, and the three curve sets are integrated to jointly constitute the set of along-path hydrodynamic curves.

[0100] Based on the historical monitoring data of the past 30 days and the current set of along-path hydrodynamic curves, the time variation law of the hydrodynamic parameters is extracted. Taking 1 hour as the time window, the peak value, mean value and standard deviation of each type of parameter are counted. The periodic fluctuation frequencies caused by daily variation and tides are identified through Fourier transform. A parameter mutation event is defined as a change in the parameter value within 5 minutes that exceeds 2 times the standard deviation of the historical mean value of the same period, and the occurrence time and duration are recorded. The above periodic characteristics, statistical indicators and mutation event records are integrated into a table to form a hydrodynamic time sequence characteristic table.

[0101] A two-way coupling model of construction activities and hydrodynamic environment is established. A "construction disturbance propagation function" is constructed to quantify the construction influence, where is the distance, is the disturbance intensity, is the attenuation coefficient. For a dredger operation, the disturbance intensity According to its excavation power and working water depth. In the virtual environment of the digital twin platform, the function is applied to the background flow field represented by the set of along-path hydrodynamic curves, and the amplitude and range of the changes in flow velocity, vorticity and shear stress caused by the construction activity in the two-dimensional plane are predicted. Finally, a visual construction interaction influence diagram containing disturbance sources, influence range and risk level is generated.

[0102] Preferably, step S42 comprises:

[0103] The construction path parameterization processing of the set of hydrodynamic characteristics is performed to obtain a path parameterization data table;

[0104] The discrete hydrodynamic parameters in the path parameterization data table are continuously processed to obtain a set of parameter continuous distribution functions;

[0105] According to the set of parameter continuous distribution functions, parameter change rate analysis and mutation point identification are performed to obtain a parameter mutation point feature table;

[0106] Based on the set of parameter continuous distribution functions, the statistical characteristics of each type of hydrodynamic parameter on the entire construction path are calculated to form an along-path parameter statistical table;

[0107] The set of parameter continuous distribution functions, the parameter mutation point feature table and the along-path parameter statistical table are integrated and visualized to obtain the set of along-path hydrodynamic curves.

[0108] In the embodiment of the application, the set of hydrodynamic characteristics is subjected to construction path parameterization processing. A one-dimensional along-path distance coordinate system is established with the starting point of the construction path as the origin Each key monitoring point on the path is assigned a corresponding along-path distance coordinate value at intervals of 10 m. The along-path coordinate of each point is associated with its corresponding flow velocity, vorticity and shear stress parameter values to form a path parameterization data table containing along-path distance and hydrodynamic parameters.

[0109] The discrete hydrodynamic parameters in the path parameterization data table are continuously processed. A cubic spline interpolation algorithm is used to construct a continuous function of the along-path distance for flow velocity, vorticity and shear stress, respectively. This algorithm ensures that the function value, first derivative and second derivative are continuous at the nodes of adjacent interpolation segments by solving the equation set, thereby accurately reflecting the smooth and gradual change characteristics of the hydrodynamic parameters in space and generating a set of parameter continuous distribution functions.

[0110] According to the set of parameter continuous distribution functions, the change rate Set parameter mutation judgment standard: when the flow rate changes more than 30% in 10m distance, the vorticity rate changes more than 40%, or the shear stress rate changes more than 25%, the position is marked as a parameter mutation point. For all identified mutation points, record its along-path position, parameter type and change amplitude, and form a parameter mutation point feature table.

[0111] Based on the parameter continuous distribution function set, the statistical characteristics of each type of hydrodynamic parameter on the entire construction path are calculated. The maximum value, minimum value and corresponding along-path position of each parameter are extracted, and the average value and standard deviation of the whole path are calculated. At the same time, the parameter mutation point density on the path is calculated, that is, the number of mutation points per 100m path length, as a quantitative indicator of path hydrodynamic complexity. These statistical data are summarized to form an along-path parameter statistical table.

[0112] The parameter continuous distribution function set, parameter mutation point feature table and along-path parameter statistical table are integrated and visualized. In the same coordinate system, the "along-path hydrodynamic parameter curve graph" is drawn, with the horizontal axis as the along-path distance s and the vertical axis as the normalized parameter value. The flow rate curve is represented by blue, the vorticity by red, and the shear stress by green. The positions of the parameter mutation points are marked on the graph with special markers, and the safety threshold range of each parameter is marked with a light-colored band area, and finally the intuitive and information-rich along-path hydrodynamic curve set is generated.

[0113] Preferably, step S44 comprises:

[0114] According to the along-path hydrodynamic curve set, the construction disturbance source characteristic quantification is carried out, and a disturbance source characteristic data set is obtained;

[0115] According to the disturbance source characteristic data set and the hydrodynamic time sequence characteristic table, a construction disturbance propagation function is constructed;

[0116] The hydrodynamic features are extracted from the along-path hydrodynamic curve set, and the hydrodynamic field change prediction is carried out by combining the construction disturbance propagation function, so as to obtain a hydrodynamic change distribution map;

[0117] Combined with the hydrodynamic change distribution map and the hydrodynamic time sequence characteristic table, construction-water environment two-way influence evaluation is carried out, and a two-way influence evaluation report is obtained;

[0118] According to the two-way influence evaluation report, a construction interaction influence graph is generated.

[0119] In the embodiment of the application, according to the along-path hydrodynamic curve set, the specific construction activity type is identified and the disturbance source is quantified. For the dredger operation, the disturbance source intensity The calculation is carried out through the formula A=0.4×excavation power (kW)×operation depth (m)÷water depth (m). The excavation power is set to 500kW, the operation depth is set to 10m, and the water depth is set to 25m, and the calculated disturbance source intensity is is 80. Construction point, activity type and intensity value Integrating, forming the disturbance source characteristic data set.

[0120] Construction disturbance propagation function is constructed to describe the construction impact. Exponential decay model is adopted , where is the disturbance source intensity, is the distance from the source point, is the attenuation coefficient. The attenuation coefficient According to the flow field characteristics in the hydrodynamic time series characteristics table: in the area where the flow velocity is greater than 1.0 m / s, 0.18 is taken; in the area where the flow velocity is less than 0.5 m / s, 0.09 is taken. At the same time, directional correction is introduced, the attenuation coefficient in the downstream direction is reduced by 10%, and the attenuation coefficient in the upstream direction is increased by 10%, forming an elliptical propagation range. When multiple construction points are working at the same time, the total disturbance influence of a point is superimposed by .

[0121] Construction disturbance propagation function is applied to the entire construction area. A 5m×5m calculation grid is established in the construction area. For each point in the grid, according to its distance from all disturbance sources and the direction of the flow, the comprehensive disturbance influence value is calculated. Then the change amount of each hydrodynamic parameter is predicted , where is the background field parameter value, is the parameter influence coefficient, and the flow velocity =0.8, the vorticity =1.2, and the shear stress =1.0. Finally, the hydrodynamic change distribution map is generated in the form of contour map.

[0122] The two-way influence assessment of construction and water environment is realized. First, according to the hydrodynamic change distribution map, the disturbance of construction on water environment is assessed: the area where the parameter change is more than 30% is divided into serious disturbance area. Second, the feedback influence of changed hydrodynamic environment on subsequent construction is assessed, and the "equipment stability index" is realized, where is the design critical parameter value of the equipment. When SI is less than 0.7, it is determined that the equipment stability is significantly affected. The evaluation results are summarized to form the two-way influence assessment report.

[0123] According to the analysis results, a multi-level and visual construction interaction diagram is generated. Taking the construction area plan as the base map, the isopleths of water dynamic change distribution diagram are superimposed, and the blue solid line represents the flow velocity enhancement, and the dotted line represents the weakening. According to the bidirectional influence evaluation report, the range of the equipment stability index SI less than 0.7 is covered with a red translucent area. The positions and intensities of each construction disturbance source are marked on the diagram The value, and finally form a comprehensive diagram integrating space distribution, change trend and risk warning.

[0124] Preferably, step S5 comprises the following steps:

[0125] Step S51: determining the execution time and area of each type of construction operation according to the safe operation window diagram, and generating a construction space-time planning table;

[0126] Step S52: generating a construction control instruction set according to the construction space-time planning table;

[0127] Step S53: executing the construction control instruction set, and implementing monitoring of the water dynamic parameters, performing prediction error analysis, and obtaining a prediction error analysis report;

[0128] Step S54: adjusting the construction control instruction set according to the prediction error analysis report, and obtaining an adjusted construction parameter table for realizing virtual-real closed-loop control through the digital twin platform.

[0129] In the embodiment of the present application, according to the safe operation window diagram, all time periods and regions marked as green in the diagram are identified. For the planned "sinking of caisson" operation, the system automatically retrieves the corresponding coordinate region and matches it to all available green time windows in the future 48 hours in this region. One of the windows with the longest duration is selected, for example, 02:00 to 06:00 the next morning, which is designated as the execution time of the operation. The operation name, execution region coordinates, planned start time and end time are recorded to form a construction space-time planning table.

[0130] The entries in the construction space-time planning table are converted into specific equipment control instructions. For the planning of the "sinking of caisson" operation, a structured instruction containing the equipment ID, operation type (sinking), start time (02:00), stop time (06:00), target three-dimensional coordinates, and operation parameters (initial sinking rate 0.5 meters / hour) is generated. All planning tasks are converted in this way to form a construction control instruction set containing multiple instructions arranged in chronological order.

[0131] During the execution of the construction control instruction set, the hydrodynamic parameters are collected in real time through the field monitoring network. The measured flow rate value of a specific monitoring point at a specific time is compared with the predicted value of the digital twin platform at the same space-time point. The relative error percentage between the two is calculated. When the relative error of any monitoring point exceeds 15% for 10 consecutive minutes, the point, parameter type, and error value are recorded. After the operation is completed, all high error records are summarized to form a prediction error analysis report.

[0132] According to the prediction error analysis report, the preset parameter adjustment rules are automatically triggered. For example, if the report shows that the measured value of the shear stress at the bottom of the caisson is 20% higher than the predicted value, the system will automatically generate an adjustment instruction to reduce the sinking rate of the caisson by 15%, i.e. from 0.5 meters / hour to 0.425 meters / hour. The updated parameters are recorded in the adjusted construction parameter table and immediately issued to the construction equipment, completing a closed-loop control from monitoring, prediction, execution to feedback correction. The construction parameter table is shown in Table 3.

[0133] Table 3

[0134] Job ID Equipment ID Original Parameter Name Original Parameter Value Adjusted Parameter Value Adjustment Reason Adjustment Time OP001 SB002 Settlement Rate 0.50 m / h 0.425 m / h Bottom Shear Stress 20% Higher Than Predicted 2024-10-19 02:45 OP002 DR003 Excavation Power 500 kW 425 kW Peripheral Flow Velocity Fluctuations Exceeded Expectations 2024-10-19 09:30 OP003 PL001 Piling Frequency 45 times / minute 40 times / minute Local Vorticity Increased by 15% 2024-10-19 14:50

[0135] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the attached claims rather than the above description, and it is intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features herein disclosed.

[0136] The above description is merely a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features herein disclosed.

Claims

1. A digital twin automated construction method for water conservancy projects, characterized in that, Includes the following steps: Step S1: Perform zoned monitoring and multi-level sampling of the water conservancy project construction area on the digital twin platform to obtain a multi-level hydrodynamic dataset; construct a hydrodynamic vector field using the multi-level hydrodynamic dataset; Step S2: Construct a triangular monitoring area using the hydrodynamic vector field; calculate the regional average vorticity of the triangular monitoring area, and perform velocity vector analysis at the three vertices of the triangular monitoring area to obtain a local vorticity distribution map; Step S3: Determine the shear stress distribution around the construction equipment based on the local vortex distribution map; determine the range of influence on the construction equipment based on the shear stress distribution map, form a multi-parameter influence radius table, and construct a hydrodynamic influence domain map in the digital twin environment; Step S4 includes: Step S41: Extract the hydrodynamic influence domain map to construct the hydrodynamic feature set at the path monitoring points; Step S42: Construct a set of hydrodynamic curves along the construction planning path using the hydrodynamic feature set; Step S43: Obtain and utilize historical monitoring data to extract the time variation characteristics of hydrodynamic parameters from the hydrodynamic curves along the route, and form a hydrodynamic time series characteristic table; Step S44 specifically involves: quantifying the characteristics of construction disturbance sources based on the set of hydrodynamic curves along the pipeline to obtain a disturbance source characteristic dataset; constructing a construction disturbance propagation function based on the disturbance source characteristic dataset and the hydrodynamic time series characteristic table; extracting hydrodynamic features from the set of hydrodynamic curves along the pipeline and predicting hydrodynamic field changes using the construction disturbance propagation function to obtain a hydrodynamic change distribution map; conducting a two-way impact assessment of construction and water environment based on the hydrodynamic change distribution map and the hydrodynamic time series characteristic table to obtain a two-way impact assessment report; and generating a construction interaction impact map based on the two-way impact assessment report. Step S45: Construct a safe operation window diagram based on the construction interaction influence diagram; Step S5: Determine the execution time and area of ​​various construction operations based on the safety operation window diagram, and generate a construction control instruction set; perform error prediction and adjustment on the construction control instruction set to obtain the adjusted construction parameter table.

2. The digital twin automated construction method for water conservancy projects according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Divide the construction monitoring area into regions and deploy a differentiated monitoring network to obtain a partitioned monitoring point configuration table; Step S12: Set the sampling frequency configuration table according to the zone monitoring point configuration table; Step S13: Simultaneously measure the horizontal and vertical velocity components according to the sampling frequency configuration table. Based on the water depth, collect data in equal layers in the vertical direction to form a multi-layer hydrodynamic dataset. Step S14: Perform hydrodynamic noise identification and filtering on the multi-level hydrodynamic dataset to obtain the filtered hydrodynamic dataset; Step S15: Construct a hydrodynamic vector field using the filtered hydrodynamic dataset.

3. The digital twin automated construction method for water conservancy projects according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Extract key monitoring points from the hydrodynamic vector field; construct triangulation points for the key monitoring points to obtain a three-point combination table; Step S22: Traverse the three-point combination table and extract the triangular unit calculation dataset from the hydrodynamic vector field; Step S23: Calculate the geometric attributes of the triangular element dataset to obtain a set of triangular elements with attached geometric attributes; Step S24: For each triangular element in the set of triangular elements with geometric attributes, use its vertex coordinates and velocity vector to perform discrete circulation flow rate calculation to obtain the element circulation flow rate list; Step S25: For each triangular cell in the cell circulation list, extract its calculated circulation and area, calculate the average vorticity and assign the center point vorticity value to obtain the discrete point vorticity assignment set. Step S26: Visualize the vorticity field space of the discrete point vorticity assignment set to obtain a local vorticity distribution map.

4. The digital twin automated construction method for water conservancy projects according to claim 3, characterized in that, Step S24 includes: The triangular element set with geometric attributes is transformed based on the circulation theory to obtain circulation calculation data; Triangular boundary vector decomposition is performed on the circulation calculation data to obtain the boundary velocity characteristic table; The boundary contribution is calculated based on the boundary velocity characteristic table to obtain the boundary circulation contribution set; The algebraic formula for the boundary circulation contribution set is derived and simplified to obtain the algebraic calculation scheme for the circulation. Optimize the circulation calculation based on the circulation algebra calculation scheme to obtain the circulation value set; The circulation data set is standardized to obtain a list of unit circulation quantizations.

5. The digital twin automated construction method for water conservancy projects according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the equipment location information and equipment characteristic parameters of the construction equipment; Step S32: Perform shear stress analysis around the equipment based on the equipment location information and local vorticity distribution map to obtain the shear stress distribution map; Step S33: Based on the quantitative characterization of shear stress distribution diagram and the influence range of equipment characteristic parameters on construction equipment, determine the multi-parameter influence radius table; Step S34: Map the influence radius of each parameter in the multi-parameter influence radius table onto the construction area plan to construct a hydrodynamic influence domain map.

6. The digital twin automated construction method for water conservancy projects according to claim 5, characterized in that, Step S33 includes: The current local velocity value is extracted from the hydrodynamic vector field, and the velocity influence radius is calibrated by combining the equipment characteristic parameters to obtain the velocity influence radius value; By locating the vortex core from the local vorticity distribution map, the vortex core can be obtained. With the vortex core as the center, boundary detection and diameter estimation are performed in eight directions to obtain the vortex diameter; Extract the equipment characteristic length from the equipment characteristic parameters, and calculate the vortex influence radius value by combining it with the vortex diameter; Using a preset shear stress safety threshold, the boundary of the high shear stress zone is calibrated on the shear stress distribution map to obtain the shear stress influence polygon; Based on the influence radius values ​​of flow velocity, vortex, and shear stress, a multi-parameter influence information analysis was performed to obtain a multi-parameter influence radius table.

7. The digital twin automated construction method for water conservancy projects according to claim 1, characterized in that, Step S42 includes: The hydrodynamic feature set is parametrically processed to obtain a path parametric data table; The discrete hydrodynamic parameters in the path parameterization data table are processed to be continuous along the path to obtain a set of continuous parameter distribution functions; Based on the continuous distribution function set of parameters, parameter change rate analysis and mutation point identification are performed to obtain a parameter mutation point feature table; Based on the continuous distribution function set of parameters, the statistical characteristics of each type of hydrodynamic parameter are calculated along the entire construction path to form a parameter statistics table along the route. By integrating and visualizing the set of continuous distribution functions of parameters, the table of parameter mutation points, and the statistical table of parameters along the route, a set of hydrodynamic curves along the route is obtained.

8. The digital twin automated construction method for water conservancy projects according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Determine the execution time and area of ​​various construction operations based on the safety operation window diagram, and generate a construction time and space planning table; Step S52: Generate a construction control instruction set based on the construction time-space planning table; Step S53: Execute the construction control instruction set, monitor hydrodynamic parameters, perform prediction error analysis, and obtain a prediction error analysis report; Step S54: Adjust the construction parameters of the construction control instruction set according to the prediction error analysis report to obtain the adjusted construction parameter table that realizes virtual-real closed-loop control through the digital twin platform.

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