A water conservancy project construction management system and method based on digital twinning

Abstract

This application provides a water conservancy project construction management system and method based on digital twins, relating to the field of construction management technology. It involves collecting design data of the target water conservancy project, constructing a digital twin of the project, and simulating the construction path based on the digital twin. The construction path is then optimized based on construction environment data to obtain the actual construction path. Risk prediction is performed on the actual construction path based on the digital twin, generating a construction risk index map. Each risk tendency point is determined according to the risk index map, and a smoothing construction strategy is generated based on these risk tendency points. This application enables intelligent construction management with seamless data throughout the entire lifecycle, reducing the probability of risks during construction and improving the accuracy and safety of water conservancy project construction.

Description

Technical Field

[0001] This application relates to the field of construction management technology, and more specifically, to a construction management system and method for water conservancy projects based on digital twins. Background Technology

[0002] With the continuous expansion of my country's water conservancy infrastructure construction, the complexity and safety requirements of water conservancy project construction are increasing. Water conservancy projects typically involve various types of structures such as dams, dikes, canals, tunnels, and pumping stations. During construction, they face numerous challenges, including complex topography, variable hydrological and meteorological conditions, and geological uncertainties. In recent years, Building Information Modeling (BIM) technology and digital construction management methods have been gradually introduced into the field of water conservancy engineering. By comprehensively managing information such as design data, construction progress, and equipment status, they have enabled the visualization and decision-making support of the construction process, thereby improving construction efficiency and management level to a certain extent.

[0003] However, existing water conservancy project construction management technologies still have shortcomings. There is a data gap between design models and the construction environment, and the ability to predict risks during construction is limited. Water conservancy project construction risks come from diverse sources, including physical collision risks from terrain obstacles, as well as unique safety risks specific to water conservancy projects such as deep water areas, high slopes, and seepage zones. It is difficult to conduct comprehensive risk simulations and quantitative analyses of the entire construction path before construction. Risk identification mainly relies on human experience, which is highly subjective and has limited coverage. Adjustments to the construction path often require manual replanning, resulting in slow response times and unreliable optimization effects, making it impossible to achieve real-time, smooth handling of construction risks. Therefore, how to achieve intelligent construction management with seamless data throughout the entire lifecycle, reduce the probability of risks during construction, and improve the accuracy and safety of water conservancy project construction has become a challenge for the industry. Summary of the Invention

[0004] This application provides a water conservancy project construction management system and method based on digital twins, which can realize intelligent construction management with data connectivity throughout the entire life cycle, reduce the probability of risks during construction, and improve the accuracy and safety of water conservancy project construction.

[0005] In a first aspect, this application provides a water conservancy project construction management method based on digital twins, the management method comprising the following steps:

[0006] Collect design data of the target water conservancy project, construct a digital twin of the water conservancy project, and simulate the water conservancy construction path based on the digital twin of the water conservancy project;

[0007] The actual construction path is obtained by optimizing the water conservancy construction path based on the construction environment data, and the risk is predicted based on the digital twin of the water conservancy project to generate a construction risk index map.

[0008] Each risk tendency point is determined based on the construction risk index map, and a smooth construction strategy is generated based on each risk tendency point.

[0009] In this embodiment, the design data information of the target water conservancy project is collected by extracting the design data information of the target water conservancy project from the BIM model data source.

[0010] In this embodiment, constructing a digital twin of a water conservancy project specifically includes:

[0011] Based on the design data of the target water conservancy project, an ontology for the construction domain of water conservancy project is constructed based on ontology. The ontology for the construction domain of water conservancy project includes an entity layer, a relation layer, and an attribute layer.

[0012] The ontology of the water conservancy project construction field is embedded into a multiphysics simulation platform through an API interface to obtain a digital twin of the water conservancy project.

[0013] In this embodiment, optimizing the water conservancy construction path based on construction environment data to obtain the actual construction path specifically includes:

[0014] Obtain real-time environmental data from the construction site;

[0015] The real-time environmental data is preprocessed to generate standardized construction environment data;

[0016] The standardized construction environment data and the water conservancy construction path are spatially overlaid and analyzed to identify conflict path segments on the water conservancy construction path that conflict with the construction environment.

[0017] Based on the conflicting path segments and the construction environment data, local replanning is performed to generate alternative path segments that avoid conflicts;

[0018] The alternative path segment is spliced ​​and smoothed with the non-conflicting path segment in the water conservancy construction path to obtain the actual construction path.

[0019] In this embodiment, the process of predicting the risks of the actual construction path based on the digital twin of the water conservancy project and generating a construction risk index map specifically includes:

[0020] Obtain path obstacle data;

[0021] The actual construction path is divided into various path points at equal intervals according to a fixed spatial step length.

[0022] Based on the path points and the path obstacle data, the repulsive risk value of each obstacle is determined using the repulsive potential energy function in the artificial potential field method.

[0023] Initialize the potential energy values ​​for each water conservancy risk;

[0024] The corresponding water hazard value is determined by using a distance attenuation model based on the potential energy value of each water conservancy risk.

[0025] The risk potential energy index set for the construction of the target water conservancy project is determined based on all obstacle repulsion risk values ​​and all water conservancy hazard values;

[0026] Using the risk potential energy index set with the cumulative arc length of the path as the horizontal axis and the risk potential energy value as the vertical axis, the construction path risk curve is determined by spline interpolation, and then the construction risk index map is generated.

[0027] In this embodiment, the risk potential energy index set for the construction of the target water conservancy project, determined based on all obstacle repulsion risk values ​​and all water conservancy hazard values, specifically includes:

[0028] A dynamic risk field coupling model is constructed, and the risk coupling coefficient between obstacle risk and water conservancy risk is determined according to the construction environment type. Based on the risk coupling coefficient, the coupling risk increment value of each path point is determined.

[0029] Identify the current construction stage, and obtain the obstacle risk weight coefficient and water conservancy risk weight coefficient from the dynamic weight configuration table based on the current construction stage;

[0030] The water flow direction attenuation factor for the water hazard value is determined based on the water flow direction vector, and the terrain attenuation factor for the obstacle repulsion risk value is determined based on terrain attenuation analysis.

[0031] The combined risk potential value of each path point is determined by fusing the coupled risk increment value, the water hazard value corrected by the water flow direction attenuation factor, and the obstacle repulsion risk value corrected by the terrain shielding attenuation factor.

[0032] The comprehensive risk potential values ​​of all path points are organized in the order of path index to generate a risk potential index set.

[0033] In this embodiment, determining each risk tendency point based on the construction risk index map specifically includes:

[0034] Initialize construction risk thresholds, which include an upper limit threshold and a lower limit threshold;

[0035] By traversing each path point in the construction risk index map, the risk potential value of each path point is compared with the construction risk threshold to obtain each risk tendency point.

[0036] Secondly, this application provides a digital twin-based water conservancy project construction management system for executing a digital twin-based water conservancy project construction management method, the water conservancy project construction management system comprising:

[0037] The digital twin module is used to collect design data information of the target water conservancy project, construct a digital twin of the water conservancy project, and simulate the water conservancy construction path based on the digital twin of the water conservancy project.

[0038] The risk index module is used to optimize the water conservancy construction path based on construction environment data to obtain the actual construction path, and to predict the risk of the actual construction path based on the digital twin of the water conservancy project, generating a construction risk index map.

[0039] The strategy implementation module is used to determine each risk tendency point according to the construction risk index map, and generate a smooth construction strategy based on each risk tendency point.

[0040] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described method for water conservancy project construction management based on digital twins.

[0041] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned method for water conservancy project construction management based on digital twins.

[0042] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0043] The process involves collecting design data of the target water conservancy project, constructing a digital twin of the project, and simulating the construction path based on this digital twin. The construction path is then optimized using construction environment data to obtain the actual construction path. Risk prediction is performed on the actual construction path based on the digital twin, generating a construction risk index map. Risk tendency points are determined according to the construction risk index map, and a smoothing construction strategy is generated based on these risk tendency points. Finally, each risk tendency point is determined according to the construction risk index map, and a smoothing construction strategy is generated based on each risk tendency point.

[0044] Therefore, this application firstly, by collecting design data of the target water conservancy project and constructing a corresponding digital twin of the water conservancy project, it is possible to faithfully reproduce the structural features, spatial layout, and physical properties of the water conservancy project in a virtual environment. Using this digital twin, the construction path can be simulated and analyzed, allowing for visual verification and comparison of different path schemes before construction. This enables the early identification of spatial conflicts, equipment interference, or geological incompatibilities in the design drawings, thereby optimizing the construction sequence layout and equipment travel routes, effectively reducing trial-and-error costs and rework risks during on-site construction. Secondly, by acquiring real environmental data from the construction site and optimizing the simulated water conservancy construction path, it is possible to… This approach ensures that construction paths are fully adapted to dynamic factors such as site topography, hydrological conditions, and obstacle distribution, improving the consistency between path planning and the actual construction environment. It quantifies the collision risks of obstacles at various locations along the path, as well as the unique risks of water conservancy projects, into a unified risk potential value. Furthermore, it generates a visualized construction risk index map, enabling the spatial distribution and tiered management of construction risks, thereby enhancing risk pre-control capabilities and safety management levels during construction. Finally, by calculating the overturning index of the construction path based on risk tendency points, it quantifies the degree of risk fluctuation in different path segments, providing a precise basis for path optimization, mitigating the impact of high-risk areas on overall construction, and improving the safety and intelligent decision-making level of the construction process.

[0045] In summary, the technical solution adopted in this application can realize intelligent construction management with data connectivity throughout the entire life cycle, reduce the probability of risks during construction, and improve the accuracy and safety of water conservancy project construction. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is an exemplary flowchart of a water conservancy project construction management method based on digital twins provided in this application;

[0048] Figure 2 This is an exemplary flowchart of obtaining the actual construction path provided in this application;

[0049] Figure 3 This is a data flow diagram of a water conservancy project construction management method based on digital twins provided in this application;

[0050] Figure 4This is a module structure diagram of the water conservancy project construction management system based on digital twins provided in this application;

[0051] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a digital twin-based water conservancy project construction management method, as provided in this application. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0053] This application provides a water conservancy project construction management system and method based on digital twins. The core of the system involves collecting design data of the target water conservancy project, constructing a digital twin of the project, and simulating the construction path based on this digital twin. The system then optimizes the construction path based on construction environment data to obtain the actual construction path, performs risk prediction on the actual construction path based on the digital twin, and generates a construction risk index map. Based on the construction risk index map, risk tendency points are determined, and a smoothing construction strategy is generated based on these risk tendency points. Finally, the system identifies each risk tendency point based on the construction risk index map and generates a smoothing construction strategy based on each risk tendency point. This approach enables intelligent construction management with seamless data throughout the entire lifecycle, reducing the probability of risks during construction and improving the accuracy and safety of water conservancy project construction.

[0054] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a water conservancy project construction management method based on digital twin according to this embodiment of the application. The management method includes the following steps:

[0055] In step S1, design data of the target water conservancy project is collected, a digital twin of the water conservancy project is constructed, and the water conservancy construction path is simulated based on the digital twin of the water conservancy project.

[0056] In this embodiment, the design data information of the target water conservancy project is collected by extracting the design data information of the target water conservancy project from the BIM model data source.

[0057] In practical implementation, geometric data and construction design information of each structure of the target water conservancy project can be extracted from the BIM model data source, and then the geometric data and construction design information of each structure can be used as the design data information of the target water conservancy project.

[0058] In this embodiment, the construction of a digital twin of a water conservancy project can be achieved through the following steps:

[0059] Based on the design data of the target water conservancy project, an ontology for the construction domain of water conservancy project is constructed based on ontology. The ontology for the construction domain of water conservancy project includes an entity layer, a relation layer, and an attribute layer.

[0060] The ontology of the water conservancy project construction field is embedded into a multiphysics simulation platform through an API interface to obtain a digital twin of the water conservancy project.

[0061] In practical implementation, firstly, based on the design data of the target water conservancy project, an ontology for the construction domain of the water conservancy project can be constructed. This ontology includes an entity layer, a relation layer, and an attribute layer. Specifically, a mesh model of each structure can be constructed based on the geometric data of each structure in the design data of the target water conservancy project. The construction design information in the design data of the target water conservancy project is then set as the basic data information for each mesh model, resulting in the ontology for the construction domain of the water conservancy project. The entity layer contains the geometric mesh models of each structure, the relation layer defines the spatial topology and construction logic constraints between structures, and the attribute layer stores material parameters, boundary conditions, and initial geostress field data. Then, the ontology for the construction domain of the water conservancy project can be embedded into a multiphysics simulation platform via an API interface to obtain a digital twin of the water conservancy project. That is, the ontology for the construction domain of the water conservancy project can be input into dedicated multiphysics simulation software, such as ANSYS or FLUENT, to obtain a digital twin of the water conservancy project.

[0062] In practical implementation, the water conservancy construction path can be obtained based on the simulation of the digital twin of the water conservancy project. That is, the starting point coordinates and ending point coordinates of the target water conservancy project can be obtained from the water conservancy project design and management system. The starting point coordinates and ending point coordinates are input into the digital twin of the water conservancy project. Combined with the topography and the layout of existing structures, the initial water conservancy construction path is generated through simulation using a path search algorithm.

[0063] In step S2, the water conservancy construction path is optimized based on construction environment data to obtain the actual construction path, and the actual construction path is risk-predicted based on the digital twin of the water conservancy project to generate a construction risk index map.

[0064] Preferably, in this embodiment, reference Figure 2As shown, this diagram is an exemplary flowchart of obtaining the actual construction path in an embodiment of this application. In this embodiment, optimizing the water conservancy construction path based on construction environment data to obtain the actual construction path can be achieved through the following steps:

[0065] In step S21, real-time environmental data of the construction site is acquired;

[0066] In step S22, the real-time environmental data is preprocessed to generate standardized construction environment data;

[0067] In step S23, the standardized construction environment data and the water conservancy construction path are spatially overlaid and analyzed to identify conflict path segments on the water conservancy construction path that conflict with the construction environment.

[0068] In step S24, local replanning is performed based on the conflicting path segments and the construction environment data to generate alternative path segments that avoid conflicts;

[0069] In step S25, the alternative path segment is spliced ​​and smoothed with the non-conflicting path segment in the water conservancy construction path to obtain the actual construction path.

[0070] In practical implementation, firstly, real-time environmental data of the construction site can be acquired. This can be done by scanning the construction environment of the target water conservancy project using UAV LiDAR or a mobile 3D laser scanner, obtaining real-time environmental data including topographic data, hydrological data, geological data, and obstacle data. Secondly, the real-time environmental data can be preprocessed to generate standardized construction environment data. This involves using filtering algorithms to remove outliers and noise points from the laser scan data and interpolating missing areas to obtain standardized construction environment data. Thirdly, the standardized construction environment data can be spatially overlaid with the water conservancy construction path to identify conflicting path segments. This involves spatially overlaying and collision detection between the coordinates of each point on the water conservancy construction path and the obstacle boundaries in the standardized construction environment data. When the spatial distance between a path point and an obstacle is less than a preset safety distance threshold (e.g., 1.5 meters for general obstacles and 5 meters for high-risk obstacles such as high-voltage power line towers), a collision can be detected. The path point is identified as a conflict point, and the path segment formed by consecutive conflict points is marked as a conflict path segment, while the remaining part is marked as a non-conflict path segment. Then, local replanning can be performed based on the conflict path segments and construction environment data to generate alternative path segments that avoid conflicts. That is, for each conflict path segment, the set of visible vertices of obstacles in its local area is extracted. Visible vertices are the edge points of obstacles that can be reached from the current path starting point along a straight line without passing through any obstacles. Then, a path search algorithm is used to search for feasible channels between visible vertices. Using the starting and ending points of the conflict path segment as endpoints, an alternative path segment that can completely avoid obstacles is generated. Finally, the alternative path segment can be spliced ​​and smoothed with the non-conflict path segments in the water conservancy construction path to obtain the actual construction path. That is, the alternative path segment and the non-conflict path segment are spliced ​​according to the path sequence. At the splicing point, the curvature continuity is smoothed using a curve fitting method to eliminate abrupt changes and sharp corners in the path, so that the entire path meets the turning radius and motion smoothness requirements of the construction machinery. Finally, an actual construction path that can be used for on-site construction guidance is obtained.

[0071] In this embodiment, the risk prediction of the actual construction path based on the digital twin of the water conservancy project, and the generation of a construction risk index map, can be achieved through the following steps:

[0072] Obtain path obstacle data;

[0073] The actual construction path is divided into various path points at equal intervals according to a fixed spatial step length.

[0074] Based on the path points and the path obstacle data, the repulsive risk value of each obstacle is determined using the repulsive potential energy function in the artificial potential field method.

[0075] Initialize the potential energy values ​​for each water conservancy risk;

[0076] The corresponding water hazard value is determined by using a distance attenuation model based on the potential energy value of each water conservancy risk.

[0077] The risk potential energy index set for the construction of the target water conservancy project is determined based on all obstacle repulsion risk values ​​and all water conservancy hazard values;

[0078] Using the risk potential energy index set with the cumulative arc length of the path as the horizontal axis and the risk potential energy value as the vertical axis, the construction path risk curve is determined by spline interpolation, and then the construction risk index map is generated.

[0079] In practical implementation, firstly, path obstacle data can be acquired. Specifically, obstacle information related to the construction path can be extracted from the acquired construction environment data as path obstacle data. This data includes the spatial coordinates of obstacles, their geometric dimensions, and their types, such as fixed buildings, temporary facilities, water bodies, and weak geological zones. It also includes the spatial topological relationship between obstacles and the construction path. Obstacles located within a pre-defined buffer zone around the actual construction path, such as a 5-meter radius on either side of the path, can be filtered out through spatial database queries or spatial overlay analysis to form a path obstacle dataset. Secondly, the actual... The actual construction path is discretized into path points at equal intervals according to a fixed spatial step size. That is, the actual construction path is discretized according to a fixed spatial step size, and the continuous construction path curve is transformed into a series of discrete coordinate points. All coordinate points are used as path points, and each path point contains its three-dimensional spatial coordinates and its index position in the path sequence. Then, the repulsive risk value of each obstacle can be determined by the repulsive potential energy function in the artificial potential field method based on each path point and the path obstacle data. That is, the information of all obstacles is obtained from the path obstacle data, including the spatial position coordinates, geometric contours and obstacle types of each obstacle. For a pathpoint currently being calculated, the system iterates through all obstacles in the path obstacle data, calculating the spatial distance between the pathpoint and each obstacle one by one. The Euclidean distance calculation method is used, which calculates the straight-line distance between the pathpoint's three-dimensional spatial coordinates and the center point coordinates of each obstacle. After calculating the distance from the pathpoint to an obstacle, the repulsive force risk contribution of that obstacle to the pathpoint is quantified based on this distance value. The reciprocal of the square of the distance is used as the risk contribution value of the obstacle to the pathpoint. For each obstacle, the risk contribution to the current pathpoint is calculated in the above manner. The risk contribution value is calculated by summing the risk contribution values ​​of all obstacles corresponding to the path point. This sum represents the risk impact of each obstacle on the path point and is the obstacle repulsion risk value for that path point. This same calculation process is performed for each path point to obtain the obstacle repulsion risk value for each path point. It should be noted that the obstacle repulsion risk value quantifies the degree of repulsion influence of obstacles on the construction path. The closer the obstacle is to the path point, the greater the risk to the construction. The obstacle repulsion risk value can be obtained through the above steps.

[0080] In addition, in specific implementation, firstly, the potential energy values ​​of various water conservancy risks can be initialized. That is, the various risk sources in the construction process of the target water conservancy project can be obtained. Risk sources include: deep water areas, high-voltage electrical equipment, steep slopes, blasting operation areas, underground pipeline areas, geological disaster hazard points, etc. The potential energy values ​​of each risk source are determined by assigning values ​​based on expert experience. It should be noted that each potential energy value is a numerical value used to quantify the inherent hazard level of the risk source, and the value range is usually [0,1]. The larger the value, the higher the degree of danger of the risk source. Secondly, the corresponding water conservancy hazard value can be determined based on the potential energy values ​​of each water conservancy risk using a distance attenuation model. That is, for each path point, the distance from the path point to the center point of each risk source can be calculated using the Euclidean distance formula. Then, the water conservancy hazard value can be obtained by the following formula:

[0081]

[0082] in, This represents the water hazard value corresponding to the i-th path point; This represents the hydraulic risk potential energy value of the k-th energized device; This represents the distance from the i-th path point to the k-th risk source. This represents the total number of risk sources. It should be noted that the water hazard value is used to quantify the overall degree of harm to construction personnel, equipment, and the project itself caused by the presence of each risk source at the path point.

[0083] Then, based on all obstacle repulsion risk values ​​and all water conservancy hazard values, the risk potential energy index set for the construction of the target water conservancy project can be determined, namely:

[0084] In this embodiment, determining the risk potential energy index set for the construction of the target water conservancy project based on all obstacle repulsion risk values ​​and all water conservancy hazard values ​​can be achieved through the following steps:

[0085] A dynamic risk field coupling model is constructed, and the risk coupling coefficient between obstacle risk and water conservancy risk is determined according to the construction environment type. Based on the risk coupling coefficient, the coupling risk increment value of each path point is determined.

[0086] Identify the current construction stage, and obtain the obstacle risk weight coefficient and water conservancy risk weight coefficient from the dynamic weight configuration table based on the current construction stage;

[0087] The water flow direction attenuation factor for the water hazard value is determined based on the water flow direction vector, and the terrain attenuation factor for the obstacle repulsion risk value is determined based on terrain attenuation analysis.

[0088] The combined risk potential value of each path point is determined by fusing the coupled risk increment value, the water hazard value corrected by the water flow direction attenuation factor, and the obstacle repulsion risk value corrected by the terrain shielding attenuation factor.

[0089] The comprehensive risk potential values ​​of all path points are organized in the order of path index to generate a risk potential index set.

[0090] In practical implementation, firstly, a dynamic risk field coupling model can be constructed. Based on the construction environment type, the risk coupling coefficient between obstacle risk and water conservancy risk is determined. Then, based on this risk coupling coefficient, the coupling risk increment value for each path point is determined. That is, for each path point, the construction environment type is determined. The construction environment type can be automatically identified through the spatial analysis function in the digital twin, including: high water level areas, low-lying and flood-prone areas, steep slope areas, flat and open areas, narrow passage areas, and areas near edges or water. Based on the identified environment type, the risk coupling coefficient between obstacle risk and water conservancy risk is retrieved from a preset risk coupling coefficient table. The risk coupling coefficient table can be obtained from experimental data analysis. The risk coupling coefficient is typically set between 0 and 2. When the risk coupling coefficient is greater than 1, it indicates a positive coupling enhancement effect between obstacle risk and water conservancy risk, meaning the total risk after the two types of risks are superimposed is higher than the sum of their individual risks. When the risk coupling coefficient is less than 1, it indicates a negative coupling weakening effect, meaning the presence of one type of risk weakens the impact of the other. When the risk coupling coefficient is equal to 1, it indicates that the two types of risks are independent, and they can be superimposed using conventional methods. After determining the risk coupling coefficient, the coupling risk increment value for each path point is calculated. The coupling risk increment value is... To quantify the additional risk or risk reduction resulting from the interaction of two types of risks, the normalized obstacle repulsion risk value is first multiplied by the normalized hydraulic hazard value to obtain the interaction term. This interaction term is then multiplied by the difference between the risk coupling coefficient and 1. The result is taken as the coupled risk increment value. When the risk coupling coefficient is greater than 1, the coupled risk increment value is positive, indicating increased risk; when the risk coupling coefficient is less than 1, the coupled risk increment value is negative, indicating reduced risk; when the risk coupling coefficient is equal to 1, the coupled risk increment value is zero, indicating no coupling effect. Secondly, the current construction stage can be identified, and based on the current construction stage... The segment obtains the obstacle risk weight coefficient and water conservancy risk weight coefficient from the dynamic weight configuration table. That is, the current construction stage can be automatically obtained from the progress plan of the construction management system or specified by manual input. The construction stages include: site leveling stage, foundation excavation stage, structural pouring stage, equipment installation stage, commissioning and operation stage, etc. Based on the identified current construction stage, the segment queries the dynamic weight configuration table to obtain the corresponding obstacle risk weight coefficient and water conservancy risk weight coefficient. The dynamic weight configuration table can be preset according to engineering experience. The table records the weight coefficient combination corresponding to each construction stage, and the sum of the two weight coefficients is 1.

[0091] Furthermore, in practical implementation, the water flow direction attenuation factor of the water hazard value can be determined based on the water flow direction vector, and the terrain shading attenuation factor of the obstacle repulsion risk value can be determined based on terrain shading analysis. That is, for the water hazard value, the water flow direction vector of the construction area is obtained based on the hydrological model in the digital twin. For each combination of risk source and each path point, the orientation of the risk source relative to the path point is determined, and the angle between the line connecting the risk source location and the path point and the water flow direction vector is calculated. The water flow direction attenuation factor is determined based on this angle. That is, when the risk source is located upstream of the path point, the water flow may carry away the risk material or cause it to be washed away. When the impact force propagates to the path point, a smaller first attenuation coefficient is used, indicating a longer risk propagation distance and a larger impact area. When the risk source is downstream of the path point, risk propagation is hindered by the reverse flow of water, and a larger second attenuation coefficient is used, indicating a faster attenuation rate and a smaller impact area. When the risk source is laterally located at the path point, a third attenuation coefficient, between the two, is used. The attenuation coefficients can be obtained by establishing an attenuation coefficient mapping table through historical data analysis and using a lookup method. That is, historical risk monitoring data under different water flow velocity conditions within the engineering area are collected, including measured values ​​of the impact distance of upstream risk sources and the impact of downstream risk sources. Based on measured values, regression analysis was performed on historical data to fit the optimal attenuation coefficients for different water flow velocity ranges. The fitting results were compiled into an attenuation coefficient mapping table. For obstacle repulsion risk values, terrain data and 3D models of structures in the digital twin were used to perform line-of-sight analysis between path points and obstacles. A straight line segment was formed connecting the path point and the center point of the obstacle. It was checked whether there were any terrain points or structures higher than the connecting line segment. If so, an obstruction was determined. The obstruction attenuation coefficient was comprehensively determined based on the height, width, material density of the obstruction, and the relative distance between the obstruction and the path point. The range of values ​​for the obstruction attenuation coefficient is as follows. The value ranges from 0 to 1. The stronger the occlusion effect, the smaller the attenuation coefficient, and the more significant the reduction in obstacle repulsion risk value. If the line-of-sight analysis results show that there are no obstructions, the occlusion attenuation coefficient is set to 1, indicating that no attenuation is performed. The occlusion attenuation coefficient is also determined by looking up a table, in the same way as the attenuation coefficient. Then, the coupled risk increment value, the water hazard value corrected by the water flow direction attenuation factor, and the obstacle repulsion risk value corrected by the terrain occlusion attenuation factor can be fused to determine the comprehensive risk potential value of each path point. That is, the original obstacle repulsion risk value is multiplied by the terrain occlusion attenuation factor to obtain the obstacle repulsion risk value corrected by the terrain occlusion.The original water hazard value is multiplied by the flow direction attenuation factor to obtain the water hazard value corrected for the flow direction. The corrected obstacle repulsion risk value is then multiplied by its corresponding obstacle risk weight coefficient, and the corrected water hazard value is multiplied by its corresponding water hazard risk weight coefficient. These two values ​​are added together to obtain a weighted sum. This result is then added to the coupled risk increment value to obtain the comprehensive risk potential energy value of the path point. It should be noted that the comprehensive risk potential energy value is a numerical indicator used to quantitatively assess the comprehensive risk level at a specific location on the construction path of a water conservancy project, thus obtaining the comprehensive risk potential energy value for each path point. Finally, the comprehensive risk potential energy values ​​of all path points can be organized according to the path index order to generate a risk potential energy index set. That is, the comprehensive risk potential energy value of each path point is associated with its index position on the construction path, and arranged according to the order of the path indexes to form a complete numerical sequence, which is the risk potential energy index set for the target water conservancy project construction. It should be noted that each value in the risk potential energy index set corresponds to a construction path... A specific location on the path is used to quantify the comprehensive risk index generated by the combined effects of multiple risk sources at that location. Finally, the construction path risk curve can be determined through the risk potential energy index set, thereby generating a construction risk index map. That is, the construction path risk curve can be determined based on the risk potential energy index set. With path length as the abscissa and risk potential energy value as the ordinate, each path point is sorted according to its cumulative arc length on the actual construction path. Scattered points are plotted sequentially and connected into a continuous curve using spline interpolation, resulting in the construction path risk curve. The construction risk index map of the target water conservancy project is generated from the construction path risk curve. That is, the construction path risk curve is mapped onto the construction layout plan. Based on the visualization function of the geographic information system or digital twin platform, the risk potential energy value of each path point is rendered on the construction path in a color gradient manner, such as: green for low risk, yellow for medium risk, and red for high risk. At the same time, the spatial distribution of risk can be displayed in the form of contour lines or heat maps, thereby generating a construction risk index map.

[0092] It should be noted that by acquiring real environmental data from the construction site and optimizing the simulated hydraulic construction path, the construction path can be fully adapted to dynamic factors such as the terrain, hydrological conditions, and obstacle distribution on site. This improves the consistency between the path planning and the actual construction environment. It can also quantify the collision risk of obstacles at various locations along the path and the unique risks of hydraulic engineering into a unified risk potential value, and further generate a visualized construction risk index map. This enables the spatial distribution presentation and graded management of construction risks, thereby improving the risk pre-control capability and safety management level during the construction process.

[0093] In step S3, risk tendency points are determined based on the construction risk index map, and a smooth construction strategy is generated based on the risk tendency points.

[0094] In this embodiment, determining the risk tendency point based on the construction risk index map can be achieved through the following steps:

[0095] Initialize construction risk thresholds, which include an upper limit threshold and a lower limit threshold;

[0096] By traversing each path point in the construction risk index map, the risk potential value of each path point is compared with the construction risk threshold to obtain each risk tendency point.

[0097] In practical implementation, firstly, construction risk thresholds can be initialized, i.e., construction risk thresholds can be preset based on historical data analysis. These thresholds include an upper limit and a lower limit. Then, each path point in the construction risk index graph can be traversed, and the risk potential value of each path point can be compared with the construction risk thresholds to obtain each risk tendency point. Specifically, for each path point, its risk potential value is first compared with the upper limit threshold. If the risk potential value of the path point is greater than the upper limit threshold, it is marked as a high-risk tendency point. If the risk potential value is not greater than the upper limit threshold, it is compared with the lower limit threshold. If the risk potential value is less than the lower limit threshold, it is marked as a low-risk tendency point. If the risk potential value is between the lower and upper limits, it is marked as a general risk point. This process is repeated for all path points, comparing and marking each one individually. Finally, all path points marked as high-risk or low-risk tendency points are summarized to form each risk tendency point.

[0098] In practical implementation, a smooth construction strategy is generated based on risk tendency points; that is, for each risk tendency point, one of the risk tendency points is selected and denoted as... The previous risk propensity point is denoted as The next risk propensity point after this risk propensity point is denoted as... Then we can calculate arrive The distance is denoted as ,calculate arrive The distance is denoted as The risk potential gradient at that risk propensity point can be obtained by the following formula:

[0099]

[0100] in, This represents the risk potential gradient at that risk propensity point; express The risk potential value; express The risk potential value; express arrive The distance; express arrive The distance, where it should be noted, is used to represent the steepness of the risk change along the construction path, thus obtaining each risk potential gradient; secondly, each local overturning index can be determined through each risk potential gradient, that is, by clustering all risk tendency points according to their positions on the construction path, and grouping multiple risk tendency points whose adjacent distance does not exceed a preset clustering threshold into the same risk path segment, the construction path can be divided into several continuous risk path segments, each risk path segment consisting of multiple spatially adjacent risk tendency points. For each risky path segment, the risk potential gradients of all risk-prone points within that segment are summed. The summation is then divided by the total length of the risky path segment, yielding its local overturning index. This local overturning index is used to quantitatively assess the stability level of a path segment when traversing a high-risk area. Based on all the local overturning indices, the construction path overturning index can be determined. This involves obtaining the total length of the entire actual construction path and, for each risky path segment, calculating the ratio of that segment's length to the total path length. This ratio is used as the weighting coefficient of that segment in the entire path. The weighting coefficient of each risky path segment is multiplied by its corresponding local overturning index to obtain the segment's contribution to the overall overturning risk. The contribution values ​​of all risky path segments are then summed to obtain the construction path overturning index for the entire construction path. It should be noted that the construction path overturning index comprehensively reflects the overall risk level of overturning accidents during construction along the entire path. Finally, risk smoothing can be performed on the actual construction path based on the construction path overturning index to generate a smoothed construction strategy. That is, it can be achieved through experimental data... The analysis established a grading standard for the path overturning threshold, as follows: when the construction path overturning index is less than 0.3, it is in the low-risk range; when the construction path overturning index is less than 0.6 but greater than 0.3, it is in the medium-risk range; when the construction path overturning index is greater than 0.6, it is in the high-risk range. When the construction path overturning index is less than 0.3, it indicates that the overall risk level of the entire construction path is low, the existing actual construction path can meet the construction safety requirements, and no path adjustment is needed. In this case, the generated smooth construction strategy is to maintain the original path strategy; when the construction path overturning index is greater than or equal to 0.3 but less than 0...At 6 o'clock, it indicates that the overall risk level of the entire construction path is moderate, with some local high-risk areas that need to be addressed. The smoothing construction strategy generated at this time is a local risk smoothing strategy: First, extract the risk path segment with the largest local overturning index among all risk path segments. This segment represents the highest risk and least stable section in the entire path. Then, input the start and end position information of this risk path segment into the digital twin of the water conservancy project. Use the simulation capability of the digital twin of the water conservancy project to perform path replanning for this section, and search for an alternative path that can avoid the high-risk points in this area. After completing the path replanning for this section, the newly generated alternative path segment is spliced ​​with other parts of the original path to form the updated actual construction path. Then, the construction path overturning index of the updated path is recalculated and compared with the threshold again. If the updated construction path overturning index is still greater than or equal to 0... 3. Repeat the above steps to continue replanning the risk path segments corresponding to the new maximum local overturning index until the construction path overturning index drops below 0.3. When the construction path overturning index is greater than 0.6, it indicates that the overall risk level of the entire construction path is high, requiring a complete replanning of the entire construction path. The resulting smoothing construction strategy is an overall replanning strategy: ignoring the original actual construction path, re-acquiring the starting and ending coordinates of the target water conservancy project, inputting the starting and ending coordinates into the water conservancy project's digital twin, and utilizing the global path search capability of the water conservancy project's digital twin, combined with all obstacle and risk source information in the construction environment data, to replan a completely new water conservancy construction path. Thus, through the above steps, a smoothing construction strategy can be generated, which includes: maintaining the original path strategy, a local risk smoothing strategy, and an overall replanning strategy.

[0101] It should be noted that by calculating the overturning index of the construction path based on risk tendency points, the degree of risk fluctuation of different path segments can be quantified, thereby providing a precise basis for path optimization, reducing the impact of high-risk areas on the overall construction, and improving the safety and intelligent decision-making level of the construction process.

[0102] refer to Figure 3As shown in the diagram, the data flow diagram of the water conservancy project construction management method based on digital twin provided in this application is divided into three layers from bottom to top: data acquisition layer, digital twin layer, and output layer. In the data acquisition layer, the system obtains design data information from the BIM model data source and construction environment data from the construction site environment. These two types of data are input into the water conservancy project digital twin layer. Based on the received design data information and construction environment data, the digital twin layer performs four core processes: simulation analysis, environmental optimization, risk prediction, and strategy generation. Finally, the output layer sequentially obtains the water conservancy construction path, the actual construction path, the construction risk index map, and the smoothing construction strategy, thereby realizing intelligent management and decision support for the entire process of water conservancy project construction.

[0103] Therefore, this application firstly, by collecting design data of the target water conservancy project and constructing a corresponding digital twin of the water conservancy project, it is possible to faithfully reproduce the structural features, spatial layout, and physical properties of the water conservancy project in a virtual environment. Using this digital twin, the construction path can be simulated and analyzed, allowing for visual verification and comparison of different path schemes before construction. This enables the early identification of spatial conflicts, equipment interference, or geological incompatibilities in the design drawings, thereby optimizing the construction sequence layout and equipment travel routes, effectively reducing trial-and-error costs and rework risks during on-site construction. Secondly, by acquiring real environmental data from the construction site and optimizing the simulated water conservancy construction path, it is possible to… This approach ensures that construction paths are fully adapted to dynamic factors such as site topography, hydrological conditions, and obstacle distribution, improving the consistency between path planning and the actual construction environment. It quantifies the collision risks of obstacles at various locations along the path, as well as the unique risks of water conservancy projects, into a unified risk potential value. Furthermore, it generates a visualized construction risk index map, enabling the spatial distribution and tiered management of construction risks, thereby enhancing risk pre-control capabilities and safety management levels during construction. Finally, by calculating the overturning index of the construction path based on risk tendency points, it quantifies the degree of risk fluctuation in different path segments, providing a precise basis for path optimization, mitigating the impact of high-risk areas on overall construction, and improving the safety and intelligent decision-making level of the construction process.

[0104] In summary, the technical solution adopted in this application can realize intelligent construction management with data connectivity throughout the entire life cycle, reduce the probability of risks during construction, and improve the accuracy and safety of water conservancy project construction.

[0105] Example 2: This application provides a water conservancy project construction management system based on digital twins, referencing... Figure 4 As shown in the figure, this is a module structure diagram of a water conservancy project construction management system based on digital twins according to this embodiment of the present application. The water conservancy project construction management system based on digital twins includes:

[0106] The digital twin module 100 is used to collect design data information of the target water conservancy project, construct a digital twin of the water conservancy project, and simulate the water conservancy construction path based on the digital twin of the water conservancy project.

[0107] The risk index module 200 is used to optimize the water conservancy construction path based on construction environment data to obtain the actual construction path, and to predict the risk of the actual construction path based on the digital twin of the water conservancy project, and generate a construction risk index map.

[0108] The construction strategy module 300 is used to determine the risk tendency points according to the construction risk index map, and generate a smooth construction strategy based on the risk tendency points.

[0109] The foregoing has detailed an example of a water conservancy engineering construction management system and method based on digital twins provided in this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware 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.

[0110] In embodiment three, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for calling and running the computer programs from the memory, so that the computer device executes the above-described method for water conservancy engineering construction management based on digital twins.

[0111] In this embodiment, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for a water conservancy project construction management system based on digital twins, according to an embodiment of this application. The water conservancy project construction management method based on digital twins in the above embodiment can... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device may be a terminal device, a server or a chip.

[0112] Processor 501 can be a general-purpose processor or a special-purpose processor. For example, processor 501 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 505 for receiving and transmitting signals.

[0113] For example, the computer device may be a chip, the communication unit 505 may be the input and / or output circuit of the chip, or the communication unit 505 may be the communication interface of the chip, and the chip may be a component of a terminal device, network device or other device.

[0114] For example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0115] The computer device may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0116] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0117] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in processor 501. Processor 501 can be a central processing unit, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device, such as discrete gate, transistor logic device, or discrete hardware component.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] In embodiment four, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for water conservancy project construction management based on digital twins.

[0120] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0121] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A construction management method for water conservancy projects based on digital twins, characterized in that, The management method includes: Collect design data of the target water conservancy project, construct a digital twin of the water conservancy project, and simulate the water conservancy construction path based on the digital twin of the water conservancy project; The actual construction path is obtained by optimizing the water conservancy construction path based on the construction environment data, and the risk is predicted based on the digital twin of the water conservancy project to generate a construction risk index map. Each risk tendency point is determined based on the construction risk index map, and a smooth construction strategy is generated based on each risk tendency point.

2. The water conservancy project construction management method based on digital twin as described in claim 1, characterized in that, The design data of the target water conservancy project is collected by extracting the design data of the target water conservancy project from the BIM model data source.

3. The water conservancy project construction management method based on digital twin as described in claim 1, characterized in that, The construction of digital twins for water conservancy projects specifically includes: Based on the design data of the target water conservancy project, an ontology for the construction domain of water conservancy project is constructed based on ontology. The ontology for the construction domain of water conservancy project includes an entity layer, a relation layer, and an attribute layer. The ontology of the water conservancy project construction field is embedded into a multiphysics simulation platform through an API interface to obtain a digital twin of the water conservancy project.

4. The water conservancy project construction management method based on digital twin as described in claim 1, characterized in that, The actual construction path is obtained by optimizing the water conservancy construction path based on construction environment data, specifically including: Obtain real-time environmental data from the construction site; The real-time environmental data is preprocessed to generate standardized construction environment data; The standardized construction environment data and the water conservancy construction path are spatially overlaid and analyzed to identify conflict path segments on the water conservancy construction path that conflict with the construction environment. Based on the conflicting path segments and the construction environment data, local replanning is performed to generate alternative path segments that avoid conflicts; The alternative path segment is spliced ​​and smoothed with the non-conflicting path segment in the water conservancy construction path to obtain the actual construction path.

5. The water conservancy project construction management method based on digital twin as described in claim 1, characterized in that, Based on the digital twin of the water conservancy project, risk prediction is performed on the actual construction path, and a construction risk index map is generated, specifically including: Obtain path obstacle data; The actual construction path is divided into various path points at equal intervals according to a fixed spatial step length. Based on the path points and the path obstacle data, the repulsive risk value of each obstacle is determined using the repulsive potential energy function in the artificial potential field method. Initialize the potential energy values ​​for each water conservancy risk; The corresponding water hazard value is determined by using a distance attenuation model based on the potential energy value of each water conservancy risk. The risk potential energy index set for the construction of the target water conservancy project is determined based on all obstacle repulsion risk values ​​and all water conservancy hazard values; The construction path risk curve is determined by using the cumulative arc length of the path as the horizontal axis and the risk potential value as the vertical axis through the risk potential energy index set, thereby generating a construction risk index map.

6. The water conservancy project construction management method based on digital twin as described in claim 5, characterized in that, Based on all obstacle repulsion risk values ​​and all water conservancy hazard values, the risk potential energy index set for the construction of the target water conservancy project is determined, specifically including: A dynamic risk field coupling model is constructed, and the risk coupling coefficient between obstacle risk and water conservancy risk is determined according to the construction environment type. Based on the risk coupling coefficient, the coupling risk increment value of each path point is determined. Identify the current construction stage, and obtain the obstacle risk weight coefficient and water conservancy risk weight coefficient from the dynamic weight configuration table based on the current construction stage; The water flow direction attenuation factor for the water hazard value is determined based on the water flow direction vector, and the terrain attenuation factor for the obstacle repulsion risk value is determined based on terrain attenuation analysis. The combined risk potential value of each path point is determined by fusing the coupled risk increment value, the water hazard value corrected by the water flow direction attenuation factor, and the obstacle repulsion risk value corrected by the terrain shielding attenuation factor. The comprehensive risk potential values ​​of all path points are organized in the order of path index to generate a risk potential index set.

7. The water conservancy project construction management method based on digital twin as described in claim 1, characterized in that, The specific risk tendency points determined based on the aforementioned construction risk index map include: Initialize construction risk thresholds, which include an upper limit threshold and a lower limit threshold; By traversing each path point in the construction risk index map, the risk potential value of each path point is compared with the construction risk threshold to obtain each risk tendency point.

8. A water conservancy project construction management system based on digital twins, used to execute a water conservancy project construction management method based on digital twins as described in any one of claims 1 to 7, characterized in that, The water conservancy project construction management system includes: The digital twin module is used to collect design data information of the target water conservancy project, construct a digital twin of the water conservancy project, and simulate the water conservancy construction path based on the digital twin of the water conservancy project. The risk index module is used to optimize the water conservancy construction path based on construction environment data to obtain the actual construction path, and to predict the risk of the actual construction path based on the digital twin of the water conservancy project, generating a construction risk index map. The strategy implementation module is used to determine each risk tendency point according to the construction risk index map, and generate a smooth construction strategy based on each risk tendency point.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs a water conservancy project construction management method based on digital twins as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement a water conservancy project construction management method based on digital twins as described in any one of claims 1 to 7.

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