Engineering construction management and control method and system based on digital twinning

Abstract

The invention relates to the technical field of engineering construction management and control, in particular to an engineering construction management and control method and system based on digital twinning. The method comprises the following steps: acquiring real-time construction engineering data and regional environment data, identifying a real-time structured construction state and generating a three-dimensional simulation model, then acquiring a target construction engineering plan and performing process node positioning to calculate a real-time engineering progress deviation, performing compensation simulation based on the deviation, and determining an engineering construction development trend. And then environment constraint mapping and resource conflict analysis are performed to generate corresponding data, and an optimized construction path conforming to environment constraint and resource conditions is screened out through construction optimization path mining of the simulation model, so that the construction efficiency and the management level are improved. According to the invention, the competitiveness and sustainable development capability of the building industry are enhanced, and an efficient and transparent engineering management system is finally formed.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering construction management and control, and in particular to an engineering construction management and control method and system based on digital twins. Background Art

[0002] Traditional engineering construction management and control methods often rely on manual monitoring and static data analysis, resulting in delayed information updates and untimely decision-making. There is a lack of effective means for real-time monitoring and adjustment of construction progress, causing many engineering projects to face progress delays and resource waste during implementation. Especially in complex projects, how to coordinate the conflicts between different processes and resources has become a major challenge. Existing technologies are not adaptable enough in dynamic environments and cannot respond quickly to on-site changes. At the same time, many existing systems lack comprehensive consideration of environmental factors, resulting in the failure to effectively identify and respond to environmental constraints during construction, such as the impact of climate change and geological conditions. Traditional resource management methods often cannot be updated in real time, resulting in irrational resource allocation, reduced construction efficiency and increased costs. Especially in large-scale projects, analysis methods that rely on historical data often cannot meet rapidly changing needs, affecting the overall progress of the project. Summary of the Invention

[0003] Based on this, it is necessary to provide an engineering construction management and control method and system based on digital twins to solve at least one of the above technical problems.

[0004] To achieve the above objectives, the engineering construction management and control method based on digital twins includes the following steps:

[0005] Step S1: Acquire real-time construction project data and project location environmental data; identify real-time structural construction status based on the real-time construction project data; perform three-dimensional simulation based on the real-time structural construction status to generate a simulated construction project structural model;

[0006] Step S2: Obtain a target construction project plan; locate process nodes of the simulated construction project structure model according to the target construction project plan, thereby obtaining real-time process nodes; calculate the real-time project progress deviation using the real-time process nodes and the target construction project plan;

[0007] Step S3: performing deviation compensation simulation on the simulated construction engineering structure model based on the real-time engineering progress deviation, thereby generating simulated compensation engineering information; and determining the engineering construction development trend based on the simulated compensation engineering information;

[0008] Step S4: Perform environmental constraint mapping based on the project's regional environmental data and the project construction development trend to obtain compensation project environmental constraints; perform resource conflict analysis based on the target construction project plan and the project construction development trend to generate compensation project resource conflict data;

[0009] Step S5: Based on the development trend of engineering construction, the construction optimization path of the simulated construction engineering structure model is mined to obtain a collection of construction optimization paths; the construction optimization path collection is subjected to constraint avoidance path screening based on the compensation engineering environmental constraints and compensation engineering resource conflict data, thereby generating an optimized construction path.

[0010] The present invention obtains construction project data and regional environmental data in real time, and the real-time structured construction status formed provides an accurate basis for subsequent three-dimensional simulation. The generated simulated construction project structural model can clearly show the status of each building element and its mutual relationship. The introduction of the target construction project plan makes the process node positioning more accurate. The acquisition of real-time process nodes and the calculation of progress deviations can timely feedback the progress of the project and effectively identify potential construction delays. The deviation compensation simulation provides a scientific basis for the dynamic adjustment of the construction plan. The generated simulated compensation project information lays the foundation for determining the development trend of engineering construction. The implementation of environmental constraint mapping effectively considers the impact of the construction environment on the project progress, ensuring the feasibility and rationality of the construction plan. The resource conflict analysis can identify and resolve contradictions in resource allocation in advance. The generated compensation project resource conflict The sudden data provides an important basis for the scientific management of resources. The excavation of construction optimization paths ensures the efficiency and rationality of the construction plan by comprehensively considering multiple restrictive factors. The screening of constraint avoidance paths further optimizes the construction path and improves construction efficiency and resource utilization. The overall system realizes all-round monitoring and management of the construction process through digital twin technology, improves the scientificity and timeliness of decision-making, reduces resource waste and cost overruns caused by information lag, promotes the development of the construction industry towards intelligence and digitalization, promotes the coordination and efficiency improvement of project management, provides strong support for the smooth progress of complex projects, enhances the competitiveness and sustainable development capabilities of the construction industry, and ultimately forms an efficient and transparent engineering management system, improves the safety and stability of the construction process, and provides more innovation space for future engineering construction.

[0011] The present invention also provides a digital twin-based engineering construction management and control system for executing the digital twin-based engineering construction management and control method described above. The digital twin-based engineering construction management and control system includes:

[0012] The real-time construction modeling module is used to obtain real-time construction project data and project location environmental data; identify the real-time structural construction status based on the real-time construction project data; and perform 3D simulation based on the real-time structural construction status to generate a simulated construction project structural model.

[0013] The progress deviation analysis module is used to obtain the target construction project plan; locate the process nodes of the simulated construction project structure model according to the target construction project plan to obtain the real-time process nodes; and calculate the real-time project progress deviation based on the real-time process nodes and the target construction project plan;

[0014] The compensation construction simulation module is used to simulate deviation compensation of the simulated construction project structure model based on real-time project progress deviations, thereby generating simulated compensation project information; and determine the project construction development trend based on the simulated compensation project information;

[0015] The environmental resource constraint module is used to map environmental constraints based on the project's regional environmental data and project construction development trends to obtain compensation project environmental constraints; it also conducts resource conflict analysis based on the target construction project plan and project construction development trends to generate compensation project resource conflict data;

[0016] The construction path optimization module is used to mine construction optimization paths for the simulated construction engineering structure model based on the development trend of engineering construction to obtain a collection of construction optimization paths; the construction optimization path collection is used to screen constraint avoidance paths based on the compensation engineering environmental constraints and compensation engineering resource conflict data to generate an optimized construction path.

[0017] Through the application of the real-time construction modeling module, the present invention enables the system to dynamically obtain real-time construction project data and regional environmental data to form an accurate structured construction status. The generated three-dimensional simulation model clearly shows the status of building elements and their relative positions. The introduction of the progress deviation analysis module makes the comparison between the target construction project plan and the simulation model more effective. The acquisition of real-time process nodes and the calculation of progress deviations can timely feedback the project progress and identify potential construction delays. The compensation construction deduction module provides a compensation plan based on real-time deviations to ensure the rationality and scientificity of the construction direction. The generated simulated compensation project information provides clear guidance for subsequent construction. The environmental resource constraint module effectively considers the impact of the construction environment on the project progress to ensure the feasibility of the construction plan. The resource conflict analysis can identify and resolve contradictions in resource allocation in advance. The generated compensation project resource conflict data provides an important basis for the scientific management of resources. The construction path optimization module significantly improves the construction efficiency by mining and screening the construction path, ensuring the rational use of resources. The overall system realizes the optimization of engineering construction through digital twin technology. Precise monitoring of the entire construction process improves management transparency and decision-making accuracy, reduces construction delays and cost overruns, drives the construction industry toward intelligence and digitalization, enhances the flexibility and adaptability of project management, and lays the foundation for the efficient implementation of complex projects. This creates an innovative and responsive construction management system to meet ever-changing market demands and technological advances. The combination of real-time modeling and progress analysis enables project managers to obtain a comprehensive view of project status immediately, enabling them to make more timely and effective decisions. The compensation deduction module develops adaptable construction adjustment strategies for different progress deviations. Environmental constraint mapping ensures the feasibility of construction plans and avoids construction delays caused by environmental factors. The construction path optimization module comprehensively considers various constraints and provides multiple optimization path options to maximize construction efficiency within limited resources. The efficient operation of the overall system not only shortens construction periods, but also reduces construction costs and improves resource utilization, building an efficient and intelligent construction management platform that provides strong support for the sustainable development of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of the steps of a digital twin-based engineering construction management and control method;

[0019] Figure 2 Detailed implementation flow chart of step S2;

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0023] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 2 , a digital twin-based engineering construction management and control method, comprising the following steps:

[0025] Step S1: Acquire real-time construction project data and project location environmental data; identify real-time structural construction status based on the real-time construction project data; perform three-dimensional simulation based on the real-time structural construction status to generate a simulated construction project structural model;

[0026] Step S2: Obtain a target construction project plan; locate process nodes of the simulated construction project structure model according to the target construction project plan, thereby obtaining real-time process nodes; calculate the real-time project progress deviation using the real-time process nodes and the target construction project plan;

[0027] Step S3: performing deviation compensation simulation on the simulated construction engineering structure model based on the real-time engineering progress deviation, thereby generating simulated compensation engineering information; and determining the engineering construction development trend based on the simulated compensation engineering information;

[0028] Step S4: Perform environmental constraint mapping based on the project's regional environmental data and the project construction development trend to obtain compensation project environmental constraints; perform resource conflict analysis based on the target construction project plan and the project construction development trend to generate compensation project resource conflict data;

[0029] Step S5: Based on the development trend of engineering construction, the construction optimization path of the simulated construction engineering structure model is mined to obtain a collection of construction optimization paths; the construction optimization path collection is subjected to constraint avoidance path screening based on the compensation engineering environmental constraints and compensation engineering resource conflict data, thereby generating an optimized construction path.

[0030] The present invention obtains construction project data and regional environmental data in real time, and the real-time structured construction status formed provides an accurate basis for subsequent three-dimensional simulation. The generated simulated construction project structural model can clearly show the status of each building element and its mutual relationship. The introduction of the target construction project plan makes the process node positioning more accurate. The acquisition of real-time process nodes and the calculation of progress deviations can timely feedback the progress of the project and effectively identify potential construction delays. The deviation compensation simulation provides a scientific basis for the dynamic adjustment of the construction plan. The generated simulated compensation project information lays the foundation for determining the development trend of engineering construction. The implementation of environmental constraint mapping effectively considers the impact of the construction environment on the project progress, ensuring the feasibility and rationality of the construction plan. The resource conflict analysis can identify and resolve contradictions in resource allocation in advance. The generated compensation project resource conflict The sudden data provides an important basis for the scientific management of resources. The excavation of construction optimization paths ensures the efficiency and rationality of the construction plan by comprehensively considering multiple restrictive factors. The screening of constraint avoidance paths further optimizes the construction path and improves construction efficiency and resource utilization. The overall system realizes all-round monitoring and management of the construction process through digital twin technology, improves the scientificity and timeliness of decision-making, reduces resource waste and cost overruns caused by information lag, promotes the development of the construction industry towards intelligence and digitalization, promotes the coordination and efficiency improvement of project management, provides strong support for the smooth progress of complex projects, enhances the competitiveness and sustainable development capabilities of the construction industry, and ultimately forms an efficient and transparent engineering management system, improves the safety and stability of the construction process, and provides more innovation space for future engineering construction.

[0031] In an embodiment of the present invention, the engineering construction management and control method based on digital twins includes the following steps:

[0032] Step S1: Acquire real-time construction project data and project location environmental data; identify real-time structural construction status based on the real-time construction project data; perform three-dimensional simulation based on the real-time structural construction status to generate a simulated construction project structural model;

[0033] In this embodiment, when acquiring real-time construction engineering data, a multi-channel, multi-band LiDAR, a high-precision GNSS positioning system, and a building information acquisition camera deployed at the construction site are used. The component structure, displacement, orientation, and elevation information are synchronously recorded through an embedded acquisition terminal. The LiDAR data sampling frequency is 10Hz, and the GNSS data acquisition accuracy is limited to within 5mm horizontally and 10mm vertically. The building information acquisition camera is fixedly installed with an image resolution of 4K. The deep convolutional neural network (CNN) model integrated in the edge computing device is used to identify the component position, connection status, and hoisting progress, and the identified image features are used. Coordinate alignment with the spatial projection of the lidar point cloud is performed to generate a three-dimensional structural component coordinate point set. Then, combined with the BIM (Building Information Model) data in the known building structure design drawings, the real-time structured construction status is automatically identified through the spatial point set matching algorithm. After the identification is completed, the structured status data is input into the three-dimensional simulation module. This module constructs a simulation model based on the structural mechanics visualization module in the Unity engine. In the simulation model, each component type is marked as an independent mesh body, the node position is encoded in the XYZ three-axis, the component attributes are indexed in the form of a hash table and bound to the dynamic component status, and the physical engine module is called in the simulation running environment to support subsequent construction sequence simulation.

[0034] Step S2: Obtain a target construction project plan; locate process nodes of the simulated construction project structure model according to the target construction project plan, thereby obtaining real-time process nodes; calculate the real-time project progress deviation using the real-time process nodes and the target construction project plan;

[0035] In this embodiment, when obtaining the target construction project plan, the construction master control plan data file is imported through the structural construction management platform. The file is encoded in Gantt format and contains the task number, task content, start time, duration, resource allocation and pre-task conditions of each task. After importing, the project plan file is converted into a data structure in JSON format. After parsing, a standard project plan table structure is formed. The planned process timeline is generated with the task as the index unit. The start and end time points of each task in the process timeline are synchronously mapped with the expected completion time bound to each component in the simulated construction project structure model. The process node of each component is represented by a "node ID + component ID" encoding, where the component ID comes from the simulation model node attribute. The node ID generates a flow code according to the construction sequence. The real-time process node is confirmed by the current status of each component in the simulated construction project structure model. If the status is marked as "installed" or "hoisting", it is marked as having reached the process node. Combined with the expected completion time of the node in the target construction project plan, the node-level progress deviation ΔT = T_real-T_plan is calculated, where T_real is the actual time of the current process and T_plan is the planned time. All component node deviations are aggregated and analyzed to obtain the overall real-time project progress deviation model.

[0036] Step S3: performing deviation compensation simulation on the simulated construction engineering structure model based on the real-time engineering progress deviation, thereby generating simulated compensation engineering information; and determining the engineering construction development trend based on the simulated compensation engineering information;

[0037] In this embodiment, after the progress deviation data is accessed to the simulated construction engineering structural model, a deviation compensation simulation operation is performed on the model based on the time offset of each component. The simulation platform loads the simulation status data of the previous stage as the current input model, and recalibrates the difference between the actual completion time and the scheduled completion time of each component. Components with deviations exceeding 6 hours are included in the structural compensation block. The structural compensation block is used as an independent scheduling unit in the simulation model, and a construction adjustment plan is set for it. These units are compensated and scheduled by a rule-based simulation scheduler to generate simulated compensation engineering information. The compensation simulation scheduler considers multi-dimensional input variables such as changes in construction machine shifts, changes in worker work shifts, and weather changes, and ultimately outputs the construction sequence and resource reconfiguration path after the structural adjustment. The construction direction is automatically generated based on the structural connection sequence of the adjusted path, providing construction logic input for subsequent path optimization and constraint mapping.

[0038] Step S4: Perform environmental constraint mapping based on the project's regional environmental data and the project construction development trend to obtain compensation project environmental constraints; perform resource conflict analysis based on the target construction project plan and the project construction development trend to generate compensation project resource conflict data;

[0039] In this embodiment, when establishing an environmental constraint mapping model based on the environmental data of the project area and the development trend of project construction, the terrain slope, river distribution, and meteorological channel of the current construction site's surrounding environment are first obtained through remote sensing image analysis. The terrain slope is calculated using a DEM (digital elevation model) with a resolution of 1 meter. Areas with a slope angle greater than 15 degrees are set as structural lifting risk areas, and heavy lifting operations are prohibited. Meteorological information is updated at a 30-minute granularity through the access to the China Meteorological Administration API interface to update the wind speed, wind direction, and precipitation data for the next 48 hours at the construction site. Areas with wind speeds greater than level 8 are marked as high wind interference areas, and areas with an angle between the wind direction and the lifting direction is less than 30 degrees are marked. For wind pressure resonance areas, a construction risk area layer is generated, and then spatial projection analysis is performed through the GIS system and the structural connection path of the engineering construction development trend. The sections with overlapping areas in the path are set as path shielding segments and an environmental constraint rule table is formed; resource conflict analysis generates a resource occupation time axis based on the construction task resource scheduling table. The task resource types are limited to lifting equipment, concrete pumps, steel structure transport vehicles and installation personnel. The task time periods are sorted by resource number. If a resource has two or more resource requests within a certain time period, it is marked as a conflict segment. The start and end times of the conflict segment are bound to the construction path, and the compensation project resource conflict data table is output.

[0040] Step S5: Based on the development trend of engineering construction, the construction optimization path of the simulated construction engineering structure model is mined to obtain a collection of construction optimization paths; the construction optimization path collection is subjected to constraint avoidance path screening based on the compensation engineering environmental constraints and compensation engineering resource conflict data, thereby generating an optimized construction path.

[0041] In this embodiment, construction optimization path mining takes the development trend of engineering construction as the starting point. By performing depth-first search (DFS) path topology analysis on the simulated construction engineering structure model, all component connection paths between the current construction node and the target phase end node are extracted. The path set is represented as an ordered list of component node IDs. During mining, the number of components, node-level span, and component type diversity index of each path are recorded. A comprehensive path score is generated by weighting the component type conversion cost and the level span cost. Paths with the top 20% scores are retained to form a set of construction optimization paths. This set of paths is then screened for environmental constraints and resource conflict data matching. Path segments that intersect with the environmental constraint rule table are masked. If the length of the masked segment exceeds 15% of the path length, the path is directly eliminated. Regions in the path with resource conflict segments are determined to be avoidable based on whether the conflict time period overlaps with the path's estimated construction time period. If the overlap rate exceeds 25%, the path is marked as a high-risk resource conflict path and eliminated. Ultimately, the paths that have been avoided are retained to form the optimized construction path result set, providing the input path sequence and resource allocation plan for the next stage of construction scheduling.

[0042] Preferably, identifying the real-time structural construction status according to the real-time construction engineering data in step S1 includes:

[0043] Label the real-time construction engineering data with engineering attributes to generate labeled construction engineering data;

[0044] Decompose the labeled construction engineering data into structural units to obtain decomposed construction unit data;

[0045] Identify the real-time construction status of each unit in the decomposed construction unit data;

[0046] The comprehensive construction status is inferred from the real-time construction engineering data based on the real-time construction status to obtain the real-time structured construction status.

[0047] In this embodiment, the acquired real-time construction engineering data including construction log data, component status data exported from the BIM (Building Information Modeling) platform, on-site temperature, humidity and concrete hardening degree sensor data, structural facade image data collected by drones, and surveillance video image frame data are uniformly collected and input into the engineering attribute labeling processing module. The attribute label recognition is performed on each component information through a component-level multi-label classification network. The model used is a multi-label image and attribute joint recognition network based on ResNet-101. The recognition targets include component ID, component category, process stage, size information, construction area number, installation time, and responsible construction group number. The classification label dimension is set to 128 dimensions, where a 128-dimensional vector is added to each component after processing. The labeled construction engineering data is input into a structural unit disassembly module based on a graph neural network (GNN) to construct a topological relationship diagram between components. The component nodes are associated through connection relationship edges. The attributes of the edges include physical connection type, construction sequence, process dependency, etc. The graph segmentation algorithm (GNN) is used. Partitioning is used to partition the overall component diagram into layers. The partitioning granularity is a combination of floor, functional area, and construction axis dimensions. The partitioning threshold is set to retain the split boundary for units with ≥10 components and a total construction physical continuity edge weight ≥80. The final output is a set of component IDs and corresponding construction process status labels contained in each structural unit. The resulting set of structural unit components is input into the construction status recognition module, which combines construction site image recognition data with IoT sensor data to identify process status. Image recognition uses the YOLOv8 object detection model to determine whether construction actions such as lifting, welding, rebar tying, formwork assembly, and concrete pumping are present in the image frame. The recognition confidence threshold for each construction action is set to 0.65, and data below the threshold is ignored. For sensor data, whether the concrete temperature gradient change value within a continuous time window is greater than 3°C / hour is used to determine whether it is in the active construction state. Light intensity and humidity indicators assist in determining whether it is in the formwork removal or curing stage. The image recognition label and the sensor judgment label are cross-verified. If they are consistent, the state is directly assigned. If they are inconsistent, the sensor state is used as the primary state and the image recognition state is added as a secondary label. A time series inference model based on spatial dependency modeling is used to comprehensively infer the construction state of the identified structural units. The inference model consists of a two-channel Transformer network. One channel inputs the spatial position vector and state code sequence of the structural unit, and the other channel inputs the construction state transition record of similar components in the same environment within the past three days. The position code length is 64 dimensions, and the state transition vector is the state code evolution sequence of the past 72 hours. The position code is used to model the spatial dependency relationship, and the state transition channel is used to model the time series construction pattern. The feature vectors of the two channels are fused by attention, and the current state code, state change probability distribution, and state prediction sequence for each structural unit within the next 72 hours are output. The final output is the real-time structured construction state, which is used to drive the simulation construction engineering structural model generation module. .

[0048] Preferably, the three-dimensional simulation using the real-time structural construction state in step S1 includes:

[0049] Based on real-time construction engineering data, the node coordinates of the real-time structural construction status are reorganized to obtain three-dimensional spatial positioning data. The node position coordinates must meet the total station observation accuracy error of no more than ±5mm, and the coordinate update frequency is set to 1Hz;

[0050] Reconstruct the component edge mesh based on the 3D spatial positioning data, where the mesh unit side length is set to no more than 10 mm and the edge recognition error is no more than 3 mm, in order to build the component spatial outline;

[0051] Perform topological connection analysis on the component spatial outline to obtain the spatial connection relationship;

[0052] Simulate engineering components based on real-time construction engineering data to generate simulated independent engineering components;

[0053] The three-dimensional simulation of the simulated independent engineering components is carried out through the spatial connection relationship to generate a simulated construction engineering structure model.

[0054] In this embodiment, based on the acquired real-time construction engineering data and real-time structured construction status data, the key points in the BIM components are reconstructed in three-dimensional space through the node coordinate reorganization module. First, the node number, initial coordinate position and construction stage identifier contained in the labeled data of each component are extracted. Then, the coordinate observation data collected in real time at a frequency of 1Hz by the total station equipment are combined to replace the BIM initial coordinates in real time. The total station point layout refers to the nine-point layout strategy of the construction site to ensure that each node is cross-observed by at least three independent measuring stations to complete the coordinate redundancy check. The positioning error of each observation point is fused and calculated using the least squares method. , set the maximum threshold of coordinate residual to no more than ±5mm, and directly remove the node data that exceeds the threshold, and finally output the reorganized three-dimensional coordinates of each component key point at the current moment, input the component key point data after the node coordinate reorganization into the component mesh reconstruction module, and use the Delaunay triangulation algorithm to reconstruct the edge mesh of each component. The boundary nodes of each component are sorted in counterclockwise order and then input into the meshing module. During the meshing process, the side length of each mesh unit is set to no more than 10mm. If there is a unit with a side length exceeding 10mm in the triangulation result, the local encrypted interpolation method is used for node insertion, and the interpolation node position is used. The edge centerline is generated by perturbation, and the perturbation distance is randomly set to a uniformly distributed value in the interval of [0.5mm, 1.5mm]. After mesh reconstruction, the edge error is checked by surface reprojection. The maximum offset distance between the original BIM edge curve and the reconstructed mesh edge is compared. When the offset error does not exceed 3mm, it is considered a valid mesh. The components exceeding the threshold will resample the boundary points until the error standard is met. The obtained component mesh data is input into the spatial connection relationship calculation module. The topological connection is determined by analyzing the node sharing relationship and spatial distance between components. If there are more than or equal to 3 common nodes between components and the common boundary length is not less than A direct connection component relationship is defined when the component boundary length is 20%. The component adjacency matrix is ​​used to represent the connection graph structure between components. The adjacency matrix is ​​a sparse symmetric matrix. A matrix element of 1 indicates a connection and a 0 indicates no connection. The component spatial connection relationship is further generated by the connection graph structure to generate a topological association table. The table records the component ID, connection object ID, connection type (face connection or edge connection), and connection node index value. This relationship structure is input into the subsequent simulation connection calculation module as the input boundary condition. Physical simulation modeling is performed on each engineering component in the real-time construction engineering data based on the identified construction status information. ANSYS Workbench is used to build the component simulation model. The simulation material parameters are retrieved from the material parameter table according to the concrete strength grade and steel bar diameter specification. For example, when the concrete strength grade is C30, its elastic modulus is set to 3×10^4MPa, Poisson's ratio is 0.2, and density is 2500kg / m 3When the diameter of HRB400 steel bar is 16mm, the elastic modulus is 2.1×10^5MPa, the Poisson's ratio is 0.3, and the density is 7850kg / m 3 , set the grid division rules according to the construction status of the component. If the component is in the template construction state, use shell unit division. If it is in the concrete pouring completion state, use solid unit division. The unit size is 5mm×5mm×5mm cube unit. The simulation solution type is set to static initial analysis. Output the deformation results and stress distribution diagram of the simulation component, and output the simulation component results as an intermediate format file for the next step of the structure assembly module to call. The generated simulation component model is assembled into a three-dimensional structure according to the established spatial connection relationship. In the three-dimensional simulation module, first read the node coordinate data and topological connection information table of all simulation components, and then Surface-connected components are rigidly docked according to shared boundary nodes. The coordinate minimum mean square error matching method is used to perform translation and rotation alignment at inconsistent nodes. The connection error threshold is set at 2 mm. Component pairs that exceed this error will be marked as connection failures and the connection path will be re-evaluated. All components are assembled in sequence according to the construction phase of the components. After each set of components is assembled, the overall structural connection diagram is updated, eventually forming a complete simulated construction engineering structure model. The model structure is driven by the virtual graphics rendering engine Unity3D for three-dimensional visualization, and an interactive component-level dynamic status query interface is provided to synchronize the engineering construction status with the three-dimensional simulation model rendering results in real time.

[0055] Preferably, step S2 includes the following steps:

[0056] Step S21: Obtain a target construction project plan; perform process gridding on the target construction project plan to obtain a gridded project plan;

[0057] Step S22: Mapping the process nodes of the simulated construction engineering structure model through the gridded engineering plan to generate structure process node data; confirming the real-time process node to which the simulated construction engineering structure model belongs based on the structure process node;

[0058] Step S23: performing node matching on the real-time process node and the structural process node data to obtain matching structural process nodes;

[0059] Step S24: Calculate the time data required for matching the structural process nodes based on the target construction project plan; and obtain real-time construction project time consumption data;

[0060] Step S25: performing duration matching according to the required time data and the real-time construction engineering time-consuming data, and mapping the real-time construction engineering time-consuming data based on the duration matching data.

[0061] In this embodiment, when obtaining the target construction project plan, the construction period parsing engine in the project scheduling system reads the XML format project plan file exported by the BIM modeling platform. The file contains the unique identifier of the project work item, start time, end time, resource allocation and associated component ID fields. The parsing engine parses the plan content according to the work level, discretizes the time period of each work in the project plan with a granularity of 10 minutes, and generates continuous gridded time units. Each time unit contains fields such as work ID, start time, end time, component location code and resource ID. The work section of each component is divided into grid numbers, and the work ID and component location code are associated one by one. After all the time grid units are generated, a set of gridded project plans is formed. The plan can represent the construction arrangement and resource demand distribution of the components in each time period of the project. The process node mapping of the simulated construction project structure model is performed through the generated gridded project plan. The simulated construction project structure model is a simulated construction project structure model that is constructed through IFC (Industry Foundation Classification). The three-dimensional structural model is constructed based on the standard of Building Classes. Each component in the model contains a unique component number and spatial location information. By analyzing the global positioning information of the component and performing field-level comparison with the component positioning code in the grid engineering plan, if the building unit, floor, and axis grid coordinates in the component number are completely consistent with the positioning code in the grid, the component is determined to belong to the grid time unit. At the same time, the geometric center coordinates (x, y,z), which is used as the spatial identifier of the process node. At the same time, the associated operation sequence of the component in the BIM model is obtained as the logical predecessor information. Finally, structural process node data is generated for each matching component, including component ID, corresponding operation ID, time grid number, spatial coordinates, operation sequence position and predecessor component ID list. When matching the real-time process node with the structural process node data, the RFID positioning equipment deployed at the construction site and the electronic tags attached to the surface of the component are used to realize real-time component identification. The RFID tag ID set of the currently completed component is read as the real-time process node input. The component tag number is extracted from the data uploaded by the RFID device, and the real-time construction sequence is constructed in chronological order. The sequence is matched with the operation sequence in the structural process node data set. The weighted dynamic time warping algorithm is used for comparison to construct the shortest distance path between the real-time node sequence and the planned node sequence. When calculating the matching path, the component ID is completely consistent and is assigned a value of 0, the operation ID is consistent but the component ID is different and is assigned a value of 1, and the other cases are assigned a value of 2. The path with the minimum matching cost is found through the dynamic programming algorithm. The corresponding planned component node is the matching structural process node. The required time for matching the structural process node is calculated based on the target construction project plan. When the time data is collected, the job IDs corresponding to all matching component nodes are extracted, and the job duration of each component is calculated by calling the job time field in the original plan file. The job duration is calculated by subtracting the start timestamp from the end timestamp, and the unit is unified in minutes. The required time of all components and the real-time time data are recorded by matching the component ID and the job ID. When performing the time matching operation based on the required time data and the real-time construction engineering time data, all matching components are traversed according to the component ID, and the matching degree between the planned time and the real-time time of each component is calculated. The absolute difference and relative proportion dual indicators are used, among which the absolute difference is the best. The value is the absolute value of the planned duration minus the actual duration, set as Δt_abs. The matching rate is defined as σ = 1-(Δt_abs / planned duration). For example, if the component is planned to take 180 minutes and the actual duration is 190 minutes, then Δt_abs = 10 minutes, σ = 1-(10 / 180) = 0.944, and σ ≥ 0.85 is set as the duration matching judgment threshold. The matching degree is represented by color grading, with high-matching components in green, medium-matching components in yellow, and low-matching components in red. This matching information is used to generate a structure-time mapping diagram, and the component ID and time matching degree σ are used to establish an association relationship for subsequent simulation scheduling platform display.

[0062] Preferably, the deviation compensation simulation of the simulated construction engineering structure model based on the real-time engineering progress deviation in step S3 includes:

[0063] Locate actual deviation units based on real-time project progress deviations and target construction project plans;

[0064] The deviation engineering structure is analyzed on the simulated construction engineering structure model through the actual deviation unit to obtain the deviation engineering structure, where the node resolution of the simulation structure model is not less than 50 nodes / m 2 ;

[0065] The current construction speed is calculated based on real-time construction project time data, with the time sampling period set to every 2 hours and the construction speed value accuracy limited to 0.1 process / h;

[0066] Compensation simulation is performed based on the current construction speed and deviation engineering structure to generate simulated compensation engineering information. The compensation simulation duration is limited to 72 hours.

[0067] In this embodiment, in the process of locating the actual deviation unit based on the real-time project progress deviation and the target construction project plan, the RFID identification data uploaded by the construction site is first compared with the BIM project component plan data one by one to construct the actual construction completion sequence on the timeline, and the dynamic time warping is called. The sequence is matched with the standard operation sequence in the target construction project plan using the Warping) comparison algorithm, and the time deviation value of each component is output using the minimum cost path. The deviation threshold is set to 15 minutes. When the difference between the actual construction time and the planned construction time of a component exceeds the threshold, the component is marked as a deviation unit. By reading the positioning code and model coordinate information of the component, the deviation unit is located to the specific component unit in the IFC format structure model. By parsing the attribute field of the IFC model, the spatial area number, the operation ID and the floor number of the component are obtained. At the same time, all deviation components are divided into multiple deviation component areas according to their spatial clustering relationship. Each area consists of at least 5 components, and the maximum geometric distance between components shall not exceed 8 meters. Finally, a deviation unit set is generated, which includes the component number, spatial position, deviation duration and the operation ID field. In the process of performing deviation engineering structure analysis on the simulated construction engineering structure model through the actual deviation unit, the above-generated deviation unit set is first imported into the 3D simulation structure model environment built based on the OpenCascade kernel, and the model analysis module is called to perform spatial mapping on each deviation component. The simulation node resolution is set to be no less than 50 nodes per square meter, and the 3D corresponding to each component is constructed through the model reconstruction interface. Grid unit, each component is refined into a high-density node grid composed of triangular facets, and the node distribution is calculated according to the surface area distribution of the component. Each face is divided into at least 10 triangular units. After extracting the component face node set, the difference is compared with the component node set in the original planned structural model, the surface units with deviations are marked, and their spatial overlapping areas are calculated. The deviation propagation diagram is established through the connection relationship and time dependency relationship between the components in the structural model. In the deviation propagation diagram, if the deviation component is a predecessor node and its operation deviation exceeds 30 minutes, its connected successor node is marked as a potential deviation node, and the influence weight is established according to the structural connection direction, thereby forming a complete The entire deviation engineering structure data set includes five items: deviation component nodes, potential impact nodes, deviation direction, deviation source, and inter-node impact weights. When calculating the current construction speed based on real-time construction project time data, the RFID check-in data and worker location data within the last 24 hours before the current time are first extracted. The number of components completed within each two-hour time period is filtered by timestamp as the construction completion amount. This process is based on the progress data API provided by the construction management platform. Each construction completion record must contain the component ID, completion time, and job ID fields. The number of components completed within each two-hour time period is calculated using the time period summary function.The calculation unit of construction speed is defined as "process per hour". The specific value is obtained by dividing the number of work items completed in the time period by the length of time. For example, if 6 component operations are completed between 8:00 and 10:00, the construction speed is 3 processes per hour (3 processes / h). All calculation results are rounded to one decimal place. If there is no component completion record in the time period, the construction speed of the time period is set to 0. Finally, the current construction speed sequence is obtained. The sequence length is equal to the actual construction time of the day divided by 2 hours. The minimum value granularity is 0.1 process per hour. In the process of generating a simulated compensation project through compensation simulation based on the current construction speed and deviation engineering structure, the deviation engineering structure dataset is first imported into the simulation scheduling platform. The platform establishes a time-driven construction simulation flow chart based on the AnyLogic or FlexSim simulation framework, sets the compensation simulation start time to the current system time, and limits the total simulation time to 72 hours, i.e. 21 There are 6 simulation periods, each corresponding to a simulation construction progress unit of 0.5 hours. The simulation logic reads the current construction speed sequence as an input parameter, predicts the number of components that can be completed in the next time period based on the construction speed sequence, and allocates this number to the component nodes marked in the deviation engineering structure. The priority queue scheduling algorithm is used to schedule component operations. The priority is calculated based on the three indicators of impact weight, operation duration, and predecessor completion. For example, if the impact weight of a component is 0.7, the operation duration is 120 minutes, and the predecessor completion is 80%, then its priority calculation value is a weighted sum of 2.3. The higher the value, the higher the priority scheduling order. The operation status is updated in each simulation cycle, and the corresponding operation end time of the completed operation node is updated. Finally, the compensation simulation engineering data file is output, which contains the predicted operation start and end time, predicted construction path, scheduling order, and expected completion progress ratio fields for each component in the next 72 hours.

[0068] Preferably, determining the construction development trend of the project based on the simulated compensation project information in step S3 includes:

[0069] Extracting structural evolution paths from simulation compensation engineering information;

[0070] Identify critical path nodes in the structural evolution path;

[0071] Based on the critical path nodes, the process of the simulated construction engineering structure model is simulated to generate specific simulation compensation processes;

[0072] Determine the subsequent construction time requirements based on simulated compensation data;

[0073] Rearrange the construction sequence of the simulated compensation specific processes according to the subsequent construction time limit requirements to generate rearranged construction specific processes;

[0074] Determine the development trend of project construction by rearranging the specific construction processes.

[0075] In this embodiment, in the specific implementation process of extracting the structural evolution path from the simulated compensation engineering information, first, the generated three-dimensional structural data of the simulated compensation engineering is accessed through the simulation platform. The three-dimensional data is stored in the form of a three-dimensional coordinate matrix with a matrix size of X×Y×Z, where X, Y, and Z respectively represent the horizontal, longitudinal, and vertical spatial grid distribution of the construction area at the meter scale. Each matrix unit records the type number, construction status identifier, timestamp information, and structural connection identifier of the current structural component. By traversing each component unit in order from small to large according to the timestamp sequence, a directed graph structural evolution map is constructed based on the structural connection identifier and the time sequence mark. The starting point of each edge in the graph is the completion node of the component in the previous time period, and the end point is the connected component node in the next time period. The weight of the edge is defined as the connection complexity value between the components, and the value range is an integer between 1 and 10, where 1 represents a point-to-point connection and 10 represents a highly coupled complex connection. Finally, a set of structural evolution paths is formed. In the specific implementation process of identifying the key path nodes in the structural evolution path, the set of structural evolution paths is firstly searched based on the breadth-first search algorithm. The algorithm is used to carry out the path depth identification process. For each path, the longest number of consecutive nodes and the highest cumulative value of connection complexity are calculated. All paths are sorted to extract the intersection path of the longest path and the high-complexity path as the candidate key path. Then, the PageRank graph algorithm is applied to calculate the centrality of each node in the candidate path. The nodes with a centrality value more than twice the average value are extracted as key path nodes. Each key path node needs to be marked with a structural position index value and a corresponding timestamp. The structural position index value is composed of a three-dimensional coordinate and a component type number to form a key path node index table. Based on the key path nodes, the process of the simulated construction engineering structure model is simulated to generate a simulation compensation in the specific implementation process. The standard process metamodel defined in the construction process component library is called. The process metamodel consists of process number, input component type, output structural characteristics, average construction time, resource demand information, etc. For each key path node, it is matched and mapped in the simulated construction engineering structure model. The mapping condition requires that the component type of the node is completely consistent with the component type defined in the model library, and the difference in the structural position index does not exceed 1 meter. After the matching is completed, the corresponding standard process model is bound to the node. Then, the actual construction resource parameter data is introduced into the process simulation environment. The resource parameters include the number of workers, the number of equipment, the total amount of material units, etc. The control range of the number of workers is limited to 3 to 8 people, the control range of the number of equipment is limited to 2 to 5 units, and the total amount of material units is accurate to 0.1 cubic meter unit, perform simulation deduction according to the bound process model and resource parameters, output the process number, estimated time value, and resource utilization forecast value of the specific process of simulation compensation, and determine the specific implementation process of subsequent construction time limit requirements based on the simulation compensation data. First, sort the specific processes of simulation compensation by process number and build a process queue. For each process, read the estimated time value of simulation consumption. The value is floating-point data in hours, with one decimal place precision. Then read the total time limit target of the project and the current construction time used, calculate the difference between the remaining available time, and the remaining time and all the time in the process queue. The total time consumed by the simulation is compared. If the total time consumed is greater than the remaining time, the time limit compression judgment link is entered. The time limit compression judgment is based on the weight coefficient adjustment rule. The process is divided into weight coefficients according to the importance of construction. The coefficient range is between 0.1 and 1.0. The compression priority is that the low-weight process is compressed first, and the compression ratio is limited to within 30% of the original time consumption. Finally, the target time consumption of each process and the corresponding latest completion time are calculated to form the subsequent construction time limit requirement table. The construction sequence of the specific simulation compensation processes is rearranged according to the subsequent construction time limit requirement to generate the specific implementation process of the rearranged construction process. First, a process dependency graph is constructed based on the simulation compensation specific process. Each node in the dependency graph represents a process, and each edge represents a predecessor dependency relationship. At the same time, the process target time and the latest completion time in the subsequent construction time limit requirement table are assigned to the attributes of the corresponding node. Then, a sorting algorithm based on heuristic constraint optimization is called to perform task sorting under the constraints (without breaking the predecessor process constraint relationship and giving priority to satisfying the earliest start time limit). The sorting priority uses the free time margin value formed by subtracting the target time from the latest completion time. The process with the smaller margin value is arranged first. The final output is the rearranged construction specific process queue, in which each process contains the process number, rearranged start time, rearranged completion time, and predecessor process number set. In the specific implementation process of determining the construction development trend by rearranging the specific construction processes, the rearranged construction specific process queue is read and the construction stage segmentation data is constructed according to the time sequence. The stage division cycle is set to 24 hours. If the number of processes in a certain time period is greater than 5, it is set as a high-density operation section. The structural position index set of the process is extracted in this section, and the three-dimensional spatial clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise (Density-Based Spatial Clustering) was used to cluster the structural locations. The cluster radius was set to 2 meters, with a minimum sample size of 3. The cluster center was used as the construction direction point for each stage. Each direction point was connected in time sequence to form a trajectory path for the project's construction development trend. Each direction path point was accompanied by a timestamp, ultimately forming a construction direction vector sequence with spatial and temporal indexes.

[0076] It is particularly important to determine the development trend of engineering construction by rearranging the specific construction processes, including:

[0077] Divide the construction phases according to the rearranged construction process to obtain the construction phase segmentation data;

[0078] For the sections with more than 5 processes in the construction stage segmentation data, the structure position index set is extracted and three-dimensional spatial cluster analysis is performed to generate stage-specific construction direction points.

[0079] Connect the phased construction direction points in chronological order to generate the trajectory path of the project construction development trend;

[0080] A construction direction vector sequence is constructed according to the trajectory path of the project construction development trend, thereby determining the project construction development trend.

[0081] In this embodiment, in the specific implementation process of dividing the construction phases according to the specific re-arranged construction process and obtaining the construction phase segmentation data, the specific re-arranged construction process queue is first arranged in ascending order according to the re-arrangement start time field, and the sorted process data is divided into time segments with a 24-hour cycle to form a set of equal-period time windows. The start time of each time window is T0 and the end time is T0+24 hours. In each time window, all the re-arranged process data are traversed, and a subset of processes whose re-arrangement start time field is between the start time and the end time of the window is extracted. The number of processes included in the subset is counted and used as the process density parameter of the time period. The above process is repeated for all time windows in sequence to finally form the construction phase segmentation data, wherein each segment includes the start time, end time, process density and the set of process numbers included in the segment. The segmentation data is represented by a nested structure, and each structural unit records the complete process content and time index information of the corresponding time period. The timestamp data comes from the field "StartTime" in the scheduling output file of the re-arranged process, in hours, with a time accuracy limited to 1 hour. The segmentation processing logic is based on the standard time slicing method (Time Slicing). The time span is fixed at 24 hours. The partitioning operation is implemented through a custom time window sliding function with a sliding step of 24 hours and non-overlapping partitioning. For sections with more than 5 processes in the construction phase segmentation data, a structure location index set is extracted and a three-dimensional spatial clustering analysis is performed to generate phased construction direction points. In the specific implementation process, the construction phase segmentation data is first traversed. The field "number of processes" is extracted from each segmented structural unit. If the number of processes is greater than 5, the time period is marked as a high-density operation section. Then, all process numbers within the section are extracted. Based on the process number, the structure location index value corresponding to each process is located in the rearranged process scheduling data set. The structure location index value is composed of the three-dimensional coordinate values ​​of the fields "Structure_X", "Structure_Y", and "Structure_Z" and the component type number of the field "ComponentTypeID". All structure location index values ​​are combined into a three-dimensional coordinate set data. The density-based spatial clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is called, and the clustering parameter Eps is set to 2.0 (in meters) represents the cluster radius, MinPts=3 represents the minimum sample number threshold, and the standard Euclidean distance formula is used for sample distance calculation. The point set input to the clustering function is the above three-dimensional coordinate set, and the output is a number of clusters. Each cluster corresponds to a dense construction area. Then the geometric center of each cluster is calculated to obtain the three-dimensional centroid coordinate value. The centroid calculation is obtained by weighted average of the point set coordinates. The weight defaults to 1. Finally, each centroid point is accompanied by the segment start time information and the process number set within the cluster, as the staged construction direction point of the time period. The clustering result is output in the direction point list structure, each item contains X, Y, Z Coordinate value, start time, component type number set and time period identifier, sort all phased construction direction points in ascending order according to the timestamp field "TimeStart", traverse the sorted direction point set, extract the three-dimensional coordinate values ​​and time fields of two adjacent direction points in turn, construct a point pair relationship set, each item in the set contains the starting point coordinates (X1, Y1, Z1), the ending point coordinates (X2, Y2, Z2), the time period start and end time pair (T1, T2) and the corresponding component number set pair, by traversing all adjacent direction point pairs, the point pairs are connected end to end in turn to form a directed path node chain, and the path chain is in the form of JSO N format is constructed, and each path segment records the path starting point, end point, start and end time and path span distance. The path span distance is the Euclidean distance in three-dimensional space, in meters, with an accuracy of 0.01 meters. All path segments are sequentially combined to form the construction direction trajectory path. Any point in the path can uniquely locate the corresponding time period, corresponding structural area and associated process set. The path sequence maintains a monotonically increasing time. Each path segment in the engineering construction development trend trajectory path is traversed, and the three-dimensional coordinate values ​​of the path starting point and end point are extracted and recorded as P1 and P2 respectively. The direction vector V=P2-P1 is calculated, and the obtained V vector is the three-dimensional space direction propulsion vector, and the vector unit is meter. Taking the start and end times T1 and T2 of the path segments, we calculate the time span ΔT = T2 - T1. We construct a direction vector data structure: Vx, Vy, Vz, ΔT, where Vx, Vy, and Vz are the component values ​​in the X, Y, and Z directions, with an accuracy of 0.01 meters. ΔT is the time span in hours with an accuracy of 1 hour. The direction vectors of all path segments are combined into a direction vector sequence, and the corresponding process number and component type number are added as attributes. Each direction vector sequence item uniquely identifies the construction advancement direction, spatial flow trend, and structural component distribution characteristics of a specific time period. The final output is a construction direction vector sequence.

[0082] Preferably, the environmental constraint mapping in step S4 based on the project location environmental data and the project construction development trend includes:

[0083] Extract climate and environmental data from the regional environmental data of the project by zoning;

[0084] Conduct environmental field simulation based on climate and environmental data to generate a simulated environmental field;

[0085] Conduct subsequent construction simulations on the development trend of engineering construction through the simulation environment field to obtain simulated subsequent construction data;

[0086] Construction constraint analysis is performed on the simulated subsequent construction data to obtain the compensation engineering environment constraints.

[0087] In this embodiment, the global positioning system (GPS) is used to calibrate the three-dimensional coordinates of the geographical boundaries of the construction area. The surface elevation data is obtained through a laser radar scanning system and a terrain model is established. Then, the data interface of the national meteorological data sharing platform is connected to extract meteorological element data such as temperature, wind speed, wind direction, humidity, precipitation, and sunshine duration for five consecutive years within the corresponding coordinate range. The wind speed and wind direction data are required to be granular per hour, with an accuracy of 0.1 meters per second for wind speed and 1 degree for wind direction. The temperature data accuracy is set to 0.1 degrees Celsius. The project area is divided into grid cells with a side length of 100 meters using a partitioning algorithm based on spatial rasterization technology. The point data is converted into grid field data using the Kriging interpolation algorithm and output as a standardized NetCDF format climate dataset. The time axis is uniformly set to 0:00 every day as the meteorological data recording point to ensure that the data is aligned with the subsequent construction data in the time dimension. When performing environmental field simulation based on climate and environmental data, a regional multi-physics field coupling simulation model needs to be constructed. The commercial-grade meteorological simulation platform WRF (Weather Research and Forecasting) is used. Model, Weather Research and Forecasting Model), set the regional wind speed and direction input distribution through the boundary layer parameter setting module, set the boundary layer type to YSU (Yonsei University Scheme) atmospheric boundary layer scheme, set the time step to 60 seconds, the total simulation period to 72 hours, the number of vertical layers to 40 layers, and use the land cover dataset MODIS to provide ground albedo, soil moisture and surface temperature parameters. The simulation output includes four types of scenes: wind field, temperature field, humidity field and precipitation field, and uniformly exports spatial distribution data in GeoTIFF format. The resolution of each pixel is 30 meters, and the simulation area extension radius is set to 30 kilometers outside the construction area to cover wind source interference. The simulation results are reconstructed in three dimensions using the ParaView simulation visualization tool, and the edge area of ​​the simulation field is reconstructed based on the The boundary smoothing process of the Gaussian smoothing kernel function is used to generate a continuous simulation environment field data set for construction simulation. When the subsequent construction simulation of the construction development trend is carried out through the simulation environment field, the simulation environment field information needs to be loaded into the constructed three-dimensional simulation construction structure model, and the wind field, temperature field and humidity field data in the simulation environment field are mapped to the structural model grid unit. The Fluent fluid mechanics simulation module is used to analyze the structural stability under the action of airflow. The boundary condition of the simulation flow field is set to the maximum value of the wind speed and direction of the simulation environment. The wind pressure action point is set by the normal direction of the outer surface of the structure. The wind pressure per unit area is calculated according to the formula P = 0.613 × V 2(P is wind pressure, V is wind speed) calculation, the simulation time is 1 hour granularity simulation for each stage in the subsequent estimated construction period, and local grid refinement areas are constructed for the external support parts of the structure and the suspended template area. The node density is four times the original grid density. The structure force image, deformation displacement distribution and temperature thermal field image are output in the simulation. The output format is unified as JPEG, and the original numerical matrix is ​​stored in CSV format for subsequent construction process matching. When performing construction constraint analysis on the simulated subsequent construction data, the wind pressure change curve, structural offset curve and construction site mapping relationship extracted during the simulation process need to be input into the construction process constraint analysis engine, and the construction process time-structure response mapping method is used to verify the synchronization of wind field interference and construction operation hour by hour, specifically with a comparison period of every 30 minutes. The wind speed and the template stability value at the construction height are set. The wind speed threshold is set to 8 meters per second. If the wind speed exceeds this value, the construction process in this period is marked as a constraint period. In addition, the degree of interference with concrete curing is analyzed based on the temperature change rate. If the daily temperature difference exceeds 15 degrees Celsius, the period is marked as an insulation constraint period. The final output includes three indicator data of "structural response offset value", "environmental disturbance factor" and "construction operability coefficient" for each process in the corresponding construction time period. If the "construction operability coefficient" is lower than 0.6, it is marked as a compensation engineering constraint section. All output data are sorted by process number and output in a table file format. The header contains fields such as process number, time period, constraint type, environmental field index, operability index, etc., and is multi-dimensionally associated and mapped with the original construction process data to generate a compensation engineering environmental constraint data set.

[0088] Preferably, performing resource conflict analysis based on the target construction project plan and the construction project development trend in step S4 includes:

[0089] Deconstruct resource requirements for the target construction project plan to obtain resource load time series data;

[0090] Conduct resource demand analysis on engineering construction development trends and generate construction demand resource data;

[0091] Cross-correlate and match resource load time series data and construction demand resource data, and mine construction resource dependencies;

[0092] Conduct subsequent construction simulation based on construction resource dependencies and engineering construction development trends to generate simulated subsequent construction data;

[0093] Determine resource bottleneck nodes based on simulated subsequent construction data;

[0094] Conduct construction development deduction based on resource bottleneck nodes to obtain predicted construction development data;

[0095] Identify predicted development interference data in predicted construction development data;

[0096] Resource conflict mapping is performed based on the predicted development interference data to generate compensation engineering resource conflict data.

[0097] In this embodiment, when deconstructing the resource requirements of the target construction project plan, the construction component process list established in BIM (Building Information Modeling) is first used to expand all nodes through the process logic tree structure, and the labor, equipment, and material resources defined in each node are marked in timeline order. Then, the resource requirements are structured and deconstructed according to the four parameters of component number, process number, construction start time, and construction duration. Each resource requirement is converted into a triple format, which contains resource type, resource quantity, time period, and is stored in the Microsoft The project plan file (.MPP format) extracts the work schedule, and uses Python scripts to match the work ID with the BIM component ID. The output format is a resource load time series data table with hours as the minimum time unit. Each row of the data table represents the consumption of a certain resource in a certain hour. The labor resource unit is person-hour, the material unit is cubic meter or ton, and the equipment unit is unit-hour. When analyzing the resource demand trend of the construction development trend, based on the defined construction direction vector sequence, the construction phase is divided into three categories: structural construction, mechanical and electrical installation, and decoration engineering. Each category is further divided into several typical sub-processes. Using Revit The API tool batch extracts the list of standard resource templates required for each sub-process, and then matches the process with the component list included in the construction development trend, and calculates the total demand for each resource in combination with the process standard quota data. Labor resources are calculated using the man-day quota method. For example, for pouring floor slabs, 3 man-days of reinforcement workers and 2.5 man-days of concrete workers are required for every 100 square meters of concrete. Material requirements are calculated using component volume and material consumption rate. For example, C30 concrete requires 320 kg of cement, 740 kg of sand, and 1240 kg of gravel per cubic meter. Equipment requirements are estimated by the duration of single-process equipment, and finally a construction demand resource data table is generated, including resource name, specification model, Five fields, total demand, demand time period, and construction unit, are used to cross-correlate and match resource load time series data with construction demand resource data. To explore construction resource dependencies, a database association query tool is used to perform a JOIN operation on the two tables based on the resource name field. Matching items are then further identified for time overlap. If a resource is repeatedly called within overlapping time periods between multiple processes, it is marked as a resource conflict point. The NetworkX library is then used to construct a directed resource dependency graph. The graph nodes represent each construction process, edges represent shared resources, and edge weights represent the intensity of resource conflicts. This is weighted by the length of resource overlap and the resource unit demand. If the edge weight exceeds a threshold of 2, the resource conflict is eliminated.If the dependency is 0, it is marked as a critical dependency. A topological sort is performed on the graph and a dependency path table is output. The path table records whether the sequence adjustment of the construction process is allowed and the earliest constructible time window. When performing subsequent construction simulation based on the construction resource dependency relationship and the development trend of engineering construction, a discrete event simulation model is constructed using the Simphony modeling engine. Each process node is modeled as an event processor, and each resource is modeled as a shared resource pool. The simulation is performed according to the sequence and resource constraints generated in the resource dependency graph. The simulation time step is 1 hour, and the overall simulation cycle is set to 180 days. The initial maximum capacity is set for each type of resource in the resource pool. The maximum capacity of labor resources is 200 people, the maximum capacity of equipment resources is 60 units, and the maximum inventory capacity of material resources is 300 tons. The earliest start time and the latest completion time within the resource available time window are simulated for each process. During the simulation, each time a resource preemption failure occurs, the process number, preemption time, and resource preemption failure events are recorded. The failure time, failed resource name, and resource gap are output as construction simulation data in CSV format, containing five columns: time, process ID, status, available resource quantity, and gap quantity. To identify resource bottleneck nodes based on subsequent simulated construction data, the resource gap quantity field in the construction simulation data is first statistically analyzed. The hourly gap quantity for each resource type is aggregated, and the resource with the most gap frequency is selected as the bottleneck resource. The frequency of all process numbers in the failed preemption events is then counted, and the process with the highest frequency is selected as the bottleneck node. If a process has a cumulative resource gap of more than 12 hours for three consecutive days, it is marked as a structural resource bottleneck node. The process number, construction phase, resource type involved, gap peak, and gap duration of the bottleneck node are recorded. Construction development deduction is performed based on the resource bottleneck node. Once the predicted construction development data is obtained, a limited resource adjustment model is constructed. A genetic algorithm is used to reorder and optimize the construction plan for the construction period where the bottleneck node is located. The fitness function is set as a weighted combination of the minimum total construction duration and the resource balance function. The population size is set to 100, the crossover probability is 0.8, and the mutation probability is set to 0.1. The maximum number of iterations is 200 generations. Each chromosome represents a construction sequence. Its executability and total time under the current resource capacity are evaluated. The optimal chromosome finally output is the adjusted predicted construction plan. The predicted construction development data table is exported, which contains the construction start time, end time, occupied resources, corresponding resource peak and delay time of each process after adjustment. When identifying the predicted development interference data in the predicted construction development data, the sliding window anomaly detection algorithm is used to process the time series of the predicted construction plan. The window length is set to 24 hours and the step length is set to 6 hours. The standard deviation of resource consumption in each window is calculated. If the standard deviation exceeds 3 times the mean of the stage, the process corresponding to the window is an abnormal development section. Further curve fitting is performed on the resource type and quantity change rate in the section. If the fitting residual exceeds the set threshold of 0.2, it is marked as a development interference point and the interference point is output. The record table includes fields such as occurrence time, process number, abnormal resource type, fluctuation value, forecast residual, and stage information. Resource conflicts are mapped based on the predicted development interference data to generate compensation engineering resource conflict data. A three-dimensional resource-process-time tensor data structure is constructed. The predicted development interference data is mapped field by field, with the abnormal resource type mapped to the resource dimension identifier, the abnormal occurrence time mapped to the time dimension index, and the abnormal process number mapped to the process dimension label. A conflict tensor fragment is generated for each interference event. A tensor clustering algorithm (such as CP decomposition) is then used to perform pattern recognition on the tensor fragments, identifying interference clusters with the same conflict characteristics. The conflict path is then inverted and tracked for each cluster, outputting a compensation engineering resource conflict data table with fields including the resource number, conflict starting process, conflict ending process, conflict period, conflict resource peak, and the recommended resource adjustment time window.

[0098] Preferably, step S5 includes the following steps:

[0099] Step S51: mapping the process nodes of the construction development trend to obtain subsequent construction process nodes; performing path expansion simulation on the simulated construction engineering structure model based on the subsequent construction process nodes to obtain an expansion channel path;

[0100] Step S52: Calculate the resource load data of each path in the expansion channel path, and select the load-balanced path based on the resource load data; integrate all the load-balanced paths to obtain a set of construction-optimized paths;

[0101] Step S53: mapping the constraint degree of each path in the construction optimization path set according to the compensation project environmental constraints and the compensation project resource conflict data, thereby generating constraint strength data for each path;

[0102] Step S54: sorting the set of optimized construction paths in reverse order of constraint strength based on the constraint strength data of each path to obtain construction paths sorted in reverse order;

[0103] Step S55: Screening the construction paths in reverse order for the minimum constraint path, thereby generating an optimized construction path.

[0104] In this embodiment, when mapping the process nodes of the construction development trend, the latest project schedule data is loaded into the digital twin construction platform, and the directed graph modeling method is used to express the construction task units in the construction direction in the form of nodes. Each node is marked with a unique number, job category, construction period, required resource category and quantity. At the same time, by constructing dependency edge relationships to identify the preceding or subsequent sequence constraints between different nodes, all process nodes are organized into a process network graph structure. On the basis of the process network graph structure, by expanding the edge path of the completed nodes in the simulated construction engineering structure model, the path expansion is performed in a layer-by-layer iterative manner. The path expansion adopts a hierarchical breadth-first search algorithm, and introduces resource occupancy constraints and construction in each layer. The spatial constraint judges the legality of the boundary extension, thereby generating an expansion channel path set. Each expansion channel path contains two dynamic parameters: the path starting point, the path end point, the node list, the cumulative amount of resource demand, and the expected continuation period. When calculating the resource load data of each path in the expansion channel path, first take each path in the expansion channel path set as a unit, call the resource allocation matrix stored in the resource distribution module, calculate the resource dimension data occupied by each construction node in the expected execution time period, and accumulate resource usage according to the execution order of the nodes in the path to obtain the resource load data of the entire path. The resource dimensions include but are not limited to manpower and mechanical equipment, power energy consumption, and spatial location number, among which the manpower and equipment usage hours are calculated according to the unit resource consumption per hour. The power consumption is calculated, and the power resources are accumulated in kilowatt-hours (kWh). The spatial resources are mapped to the site layout map with the construction unit number corresponding to each path node, and the spatial coverage is calculated using pixel rasterization. Then, based on the set resource load upper limit threshold, such as manpower hours not exceeding 240 hours, power not exceeding 800 kWh, and spatial overlap not exceeding 20%, the qualified paths are screened as load balancing paths. Finally, all paths that meet the above resource constraints are integrated into a construction optimization path collection. The collection data structure includes a path unique identifier, a node index sequence, a resource usage detail array, an estimated construction time period, and a load balancing score value. According to the compensation project environmental constraints and compensation project resources, the construction optimization path is selected. When conflict data is used to map the constraint degree of each path in the construction optimization path collection, a compensation engineering environmental constraint data matrix is ​​first constructed. Each row in the matrix corresponds to a specific node in the path, and the columns represent the environmental constraints, including noise threshold limits, nighttime operation prohibition periods, climate humidity impact coefficients, and transportation road closure periods. The resource conflict data is constructed as a resource conflict weight vector. The conflict degree of each resource type, such as tower cranes, welding robots, and pouring pumps, is represented by a normalized conflict coefficient from 0 to 1. Then, the nodes in each path are mapped one-to-one with the compensation engineering environmental constraint data. The rule nested loop algorithm is used to determine whether a node in the path is unexecutable due to a certain environmental constraint.If it exists, the environmental constraint score of the path is accumulated, and the weighted sum of various resource conflicts in the path is calculated as the resource conflict score using the resource conflict weight vector. The two scores are added to form the total constraint strength data of the path. This data is added to the "constraint strength" field in the construction optimization path collection data structure in numerical form. When the construction optimization path collection is sorted in reverse order of constraint strength based on the constraint strength data of each path, the reverse comparison function defined in the sorting module is called, and the "constraint strength" field in the construction optimization path collection is used as the sorting keyword. All paths are sorted in reverse order through the quick sorting algorithm to generate a reverse-order construction path list. In the sorted path list, the one with the highest constraint strength is arranged in front and the one with the lowest constraint strength is arranged in the back. During the sorting operation, if there are multiple paths with exactly the same constraint strength values, the minimum resource load value in the path is used as the secondary sorting condition, and a sorting index field "path_index" is added to the data structure to maintain To maintain the stability of the sorting results, a reverse list of construction paths containing complete sorting identifiers is finally formed. When screening the minimum constraint path for the reverse sorted construction paths, the sorted construction path list is first read, and the data of each path is read from the end. The node sequence, resource usage record, environmental constraint matching record and conflict score data in the path are read one by one. A predefined minimum constraint screening function is used. The function sets the screening criteria as "constraint strength value less than 0.3, resource usage intensity less than 50% of the total system resource load", and further restricts the path length to no more than 100 nodes, the time interval between each node must not be less than 1 hour, and the path must not contain 4 or more consecutive nodes in the nighttime restricted time period. The path that meets the above conditions is considered the minimum constraint path, and its path identifier, node array, resource load parameter, environmental constraint label and original number of the expansion channel are recorded. All paths that meet the conditions are output to form the final optimized construction path.

[0105] It is particularly important that step S53 includes:

[0106] Based on the preset environmental matching data, the construction optimization path collection data is calibrated for path environmental adaptability to obtain path environmental adaptability data;

[0107] Performing fitness constraint mapping on the path environment fitness data and the compensation engineering environment constraint data, thereby obtaining environment constraint mapping data;

[0108] Performing path resource conflict detection on the construction optimization path collection data to generate path resource conflict data;

[0109] Performing resource constraint mapping on the path resource conflict data and the compensation project resource conflict data, thereby obtaining resource constraint mapping data;

[0110] The environmental constraint mapping data and the resource constraint mapping data are integrated into multi-dimensional constraints to generate the constraint strength data of each path.

[0111] In this embodiment, based on the preset environmental matching data, each path in the construction optimization path collection is matched node by node. In combination with the spatial position coordinates, time period and environmental condition parameters corresponding to each construction node in the path, a multi-dimensional feature vector is used to calculate the matching degree between the path node and the environment. The matching degree is calculated using the cosine similarity algorithm (cosinesimilarity), and the similarity values ​​of the four key environmental parameters of ambient temperature, humidity, wind speed and dust concentration are fused by weighted average to obtain the path environment adaptability data of each path. The preset environmental matching data is based on the historical environmental monitoring data of the construction area, and the weight distribution is fixed to 0.3 for temperature, 0.25 for humidity, 0.25 for wind speed, and 0.2 for dust concentration. The range of the path environment adaptability data is limited to between 0 and 1, and the closer the value is to 1, the better the match. The higher the degree of path-environmental compatibility, the more the path environmental suitability data is mapped to the compensation project environmental constraint data. First, the environmental threshold parameters in the compensation project environmental constraint data are normalized, including temperature thresholds (minimum 15 degrees Celsius, maximum 35 degrees Celsius), humidity thresholds (minimum 30%, maximum 70%), wind speed thresholds (maximum 5 meters per second), and dust concentration thresholds (maximum 0.15 milligrams per cubic meter). The path environmental suitability data is mapped to these thresholds according to a rule. A linear interpolation mapping function is used to assign higher constraint weights to path nodes that do not meet the threshold requirements. The resulting mapping results form the environmental constraint mapping data, which is then integrated into the path level using a weighted summation method. Weights are assigned based on the construction process risk level, with the highest-risk nodes receiving a weight of 0.5 and the lowest-risk nodes receiving a weight of 0.1. The mapping data reflects the degree to which the path satisfies the environmental constraints. The larger the value, the stronger the constraint. Path resource conflict detection is performed on the collection data of construction optimization paths. Time axis overlap detection is performed on the node construction time window and resource usage of each path. The resource occupation conflict index is calculated using the time series analysis method. The conflict index is expressed as a value between 0 and 1, where 0 indicates no conflict and 1 indicates complete conflict. The detection process is based on resource type classification, covering mechanical equipment, labor resources and material resources. The sliding time window length is set to 1 hour and the resolution is set to 5 minutes. The conflict degree of all resource categories is counted and fused to obtain path resource conflict data. The data records the resource overlap conflict degree in each time window in detail, supporting fine-grained analysis. Resource constraint mapping is performed on path resource conflict data and compensation project resource conflict data. First, the resource restriction thresholds in the compensation project resource conflict data are classified according to resource type. The restriction thresholds include the maximum number of equipment occupied at the same time, the maximum labor density and the maximum material stacking amount. For example, the threshold value is limited to 5 equipment and the labor density is limited to 1 per square meter. For example, a 20-person village with a material stacking limit of 50 cubic meters was used. The resource usage value for each time period in the path resource conflict data was compared to the corresponding threshold. If the ratio was greater than 1, the overlimit weight was calculated. The overlimit weight was converted into resource constraint strength using an exponential decay function. The resource constraint mapping data was accumulated to the path level based on time weighting. The total resource constraint strength of the path was obtained by summing the resource constraint strengths of each time period, reflecting the magnitude of the path resource usage constraint strength. The environmental constraint mapping data and the resource constraint mapping data were subjected to multi-dimensional constraint integration. A weighted linear fusion algorithm was used to multiply the environmental constraint mapping data and the resource constraint mapping data by weight factors, which were set to 0.6 for the environmental constraint weight and 0.4 for the resource constraint weight, respectively. After fusion, a comprehensive constraint strength value was generated. The comprehensive constraint strength data was calculated for each path. The fusion process normalized the constraint data of all nodes within the path. The final output of the constraint strength data for each path was stored as a floating-point number, limited to a range of 0 to 1. Values ​​closer to 1 indicate greater constraint strength. The constraint strength data was used in subsequent path sorting and screening.

[0112] The present invention also provides a digital twin-based engineering construction management and control system for executing the digital twin-based engineering construction management and control method described above. The digital twin-based engineering construction management and control system includes:

[0113] The real-time construction modeling module is used to obtain real-time construction project data and project location environmental data; identify the real-time structural construction status based on the real-time construction project data; and perform 3D simulation based on the real-time structural construction status to generate a simulated construction project structural model.

[0114] The progress deviation analysis module is used to obtain the target construction project plan; locate the process nodes of the simulated construction project structure model according to the target construction project plan to obtain the real-time process nodes; and calculate the real-time project progress deviation based on the real-time process nodes and the target construction project plan;

[0115] The compensation construction simulation module is used to simulate deviation compensation of the simulated construction project structure model based on real-time project progress deviations, thereby generating simulated compensation project information; and determine the project construction development trend based on the simulated compensation project information;

[0116] The environmental resource constraint module is used to map environmental constraints based on the project's regional environmental data and project construction development trends to obtain compensation project environmental constraints; it also conducts resource conflict analysis based on the target construction project plan and project construction development trends to generate compensation project resource conflict data;

[0117] The construction path optimization module is used to mine construction optimization paths for the simulated construction engineering structure model based on the development trend of engineering construction to obtain a collection of construction optimization paths; the construction optimization path collection is used to screen constraint avoidance paths based on the compensation engineering environmental constraints and compensation engineering resource conflict data to generate an optimized construction path.

[0118] Through the application of the real-time construction modeling module, the present invention enables the system to dynamically obtain real-time construction project data and regional environmental data to form an accurate structured construction status. The generated three-dimensional simulation model clearly shows the status of building elements and their relative positions. The introduction of the progress deviation analysis module makes the comparison between the target construction project plan and the simulation model more effective. The acquisition of real-time process nodes and the calculation of progress deviations can timely feedback the project progress and identify potential construction delays. The compensation construction deduction module provides a compensation plan based on real-time deviations to ensure the rationality and scientificity of the construction direction. The generated simulated compensation project information provides clear guidance for subsequent construction. The environmental resource constraint module effectively considers the impact of the construction environment on the project progress to ensure the feasibility of the construction plan. The resource conflict analysis can identify and resolve contradictions in resource allocation in advance. The generated compensation project resource conflict data provides an important basis for the scientific management of resources. The construction path optimization module significantly improves the construction efficiency by mining and screening the construction path, ensuring the rational use of resources. The overall system realizes the optimization of engineering construction through digital twin technology. Precise monitoring of the entire construction process improves management transparency and decision-making accuracy, reduces construction delays and cost overruns, drives the construction industry toward intelligence and digitalization, enhances the flexibility and adaptability of project management, and lays the foundation for the efficient implementation of complex projects. This creates an innovative and responsive construction management system to meet ever-changing market demands and technological advances. The combination of real-time modeling and progress analysis enables project managers to obtain a comprehensive view of project status immediately, enabling them to make more timely and effective decisions. The compensation deduction module develops adaptable construction adjustment strategies for different progress deviations. Environmental constraint mapping ensures the feasibility of construction plans and avoids construction delays caused by environmental factors. The construction path optimization module comprehensively considers various constraints and provides multiple optimization path options to maximize construction efficiency within limited resources. The efficient operation of the overall system not only shortens construction periods, but also reduces construction costs and improves resource utilization, building an efficient and intelligent construction management platform that provides strong support for the sustainable development of the construction industry.

[0119] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0120] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A digital twin-based engineering construction management and control method, characterized in that: The following steps are involved: Step S1: Acquire real-time construction project data and project location environmental data; Identify real-time structural construction status based on real-time construction engineering data; Through real-time structural construction status, 3D simulation is performed to generate a simulated construction engineering structural model; Step S2: Obtaining a target construction project plan; Locate process nodes of the simulated construction engineering structure model according to the target construction engineering plan to obtain real-time process nodes; calculate the real-time project progress deviation based on the real-time process nodes and the target construction engineering plan; Step S3: performing deviation compensation simulation on the simulated construction engineering structure model based on the real-time engineering progress deviation, thereby generating simulated compensation engineering information; and determining the engineering construction development trend based on the simulated compensation engineering information; Step S4: Perform environmental constraint mapping based on the project location environmental data and project construction development trends to obtain compensation project environmental constraints; Conduct resource conflict analysis based on target construction project plans and project construction development trends to generate compensation project resource conflict data; Step S5: Based on the development trend of engineering construction, the construction optimization path of the simulated construction engineering structure model is mined to obtain a collection of construction optimization paths; the construction optimization path collection is subjected to constraint avoidance path screening based on the compensation engineering environmental constraints and compensation engineering resource conflict data, thereby generating an optimized construction path.

2. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: Identifying the real-time structural construction status based on the real-time construction engineering data in step S1 includes: Label the real-time construction engineering data with engineering attributes to generate labeled construction engineering data; Decompose the labeled construction engineering data into structural units to obtain decomposed construction unit data; Identify the real-time construction status of each unit in the decomposed construction unit data; The comprehensive construction status is inferred from the real-time construction engineering data based on the real-time construction status to obtain the real-time structured construction status.

3. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: The three-dimensional simulation based on the real-time structural construction state in step S1 includes: Based on real-time construction engineering data, the node coordinates of the real-time structural construction status are reorganized to obtain three-dimensional spatial positioning data. The node position coordinates must meet the total station observation accuracy error of no more than ±5mm, and the coordinate update frequency is set to 1Hz; Reconstruct the component edge mesh based on the 3D spatial positioning data, where the mesh unit side length is set to no more than 10 mm and the edge recognition error is no more than 3 mm, in order to build the component spatial outline; Perform topological connection analysis on the component spatial outline to obtain the spatial connection relationship; Simulate engineering components based on real-time construction engineering data to generate simulated independent engineering components; The three-dimensional simulation of the simulated independent engineering components is carried out through the spatial connection relationship to generate a simulated construction engineering structure model.

4. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtain a target construction project plan; perform process gridding on the target construction project plan to obtain a gridded project plan; Step S22: Mapping the process nodes of the simulated construction engineering structure model through the gridded engineering plan to generate structure process node data; confirming the real-time process node to which the simulated construction engineering structure model belongs based on the structure process node; Step S23: performing node matching on the real-time process node and the structural process node data to obtain matching structural process nodes; Step S24: Calculate the time data required for matching the structural process nodes based on the target construction project plan; and obtain real-time construction project time consumption data; Step S25: performing duration matching according to the required time data and the real-time construction engineering time-consuming data, and mapping the real-time construction engineering time-consuming data based on the duration matching data.

5. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: The deviation compensation simulation of the simulated construction engineering structure model based on the real-time engineering progress deviation in step S3 includes: Locate actual deviation units based on real-time project progress deviations and target construction project plans; The deviation engineering structure is analyzed on the simulated construction engineering structure model through the actual deviation unit to obtain the deviation engineering structure, where the node resolution of the simulation structure model is not less than 50 nodes / m 2 ; The current construction speed is calculated based on real-time construction project time data, with the time sampling period set to every 2 hours and the construction speed value accuracy limited to 0.1 process / h; Compensation simulation is performed based on the current construction speed and deviation engineering structure to generate simulated compensation engineering information. The compensation simulation duration is limited to 72 hours.

6. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: Determining the construction development trend of the project based on the simulated compensation project information in step S3 includes: Extracting structural evolution paths from simulation compensation engineering information; Identify critical path nodes in the structural evolution path; Based on the critical path nodes, the process of the simulated construction engineering structure model is simulated to generate the specific process of simulation compensation; Determine the subsequent construction time requirements based on simulated compensation data; Rearrange the construction sequence of the simulated compensation specific processes according to the subsequent construction time limit requirements to generate rearranged construction specific processes; Determine the development trend of project construction by rearranging the specific construction processes.

7. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: The environmental constraint mapping in step S4 based on the project's regional environmental data and the project construction development trend includes: Extract climate and environmental data from the regional environmental data of the project by zoning; Conduct environmental field simulation based on climate and environmental data to generate a simulated environmental field; Conduct subsequent construction simulations on the development trend of engineering construction through the simulation environment field to obtain simulated subsequent construction data; Construction constraint analysis is performed on the simulated subsequent construction data to obtain the compensation engineering environment constraints.

8. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: The resource conflict analysis in step S4 based on the target construction project plan and the construction project development trend includes: Deconstruct resource requirements for the target construction project plan to obtain resource load time series data; Conduct resource demand analysis on engineering construction development trends and generate construction demand resource data; Cross-correlate and match resource load time series data and construction demand resource data, and mine construction resource dependencies; Conduct subsequent construction simulation based on construction resource dependencies and engineering construction development trends to generate simulated subsequent construction data; Determine resource bottleneck nodes based on simulated subsequent construction data; Conduct construction development deduction based on resource bottleneck nodes to obtain predicted construction development data; Identify predicted development interference data in predicted construction development data; Resource conflict mapping is performed based on the predicted development interference data to generate compensation engineering resource conflict data.

9. The engineering construction management and control method based on digital twin according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: mapping the process nodes of the construction development trend to obtain subsequent construction process nodes; performing path expansion simulation on the simulated construction engineering structure model based on the subsequent construction process nodes to obtain an expansion channel path; Step S52: Calculate the resource load data of each path in the expansion channel path, and select the load-balanced path based on the resource load data; integrate all the load-balanced paths to obtain a set of construction-optimized paths; Step S53: mapping the constraint degree of each path in the construction optimization path set according to the compensation project environmental constraints and the compensation project resource conflict data, thereby generating constraint strength data for each path; Step S54: sorting the set of optimized construction paths in reverse order of constraint strength based on the constraint strength data of each path to obtain construction paths sorted in reverse order; Step S55: Screening the construction paths in reverse order for the minimum constraint path, thereby generating an optimized construction path.

10. An engineering construction management and control system based on digital twins, characterized by: For executing the engineering construction management and control method based on digital twins as claimed in claim 1, the engineering construction management and control system based on digital twins comprises: The real-time construction modeling module is used to obtain real-time construction project data and project location environmental data; identify the real-time structural construction status based on the real-time construction project data; and perform 3D simulation based on the real-time structural construction status to generate a simulated construction project structural model. The progress deviation analysis module is used to obtain the target construction project plan; locate the process nodes of the simulated construction project structure model according to the target construction project plan to obtain the real-time process nodes; and calculate the real-time project progress deviation based on the real-time process nodes and the target construction project plan; The compensation construction simulation module is used to simulate deviation compensation of the simulated construction project structure model based on real-time project progress deviations, thereby generating simulated compensation project information; and determine the project construction development trend based on the simulated compensation project information; The environmental resource constraint module is used to map environmental constraints based on the project's regional environmental data and project construction development trends to obtain compensation project environmental constraints; it also conducts resource conflict analysis based on the target construction project plan and project construction development trends to generate compensation project resource conflict data; The construction path optimization module is used to mine construction optimization paths for the simulated construction engineering structure model based on the development trend of engineering construction to obtain a collection of construction optimization paths; the construction optimization path collection is used to screen constraint avoidance paths based on the compensation engineering environmental constraints and compensation engineering resource conflict data to generate an optimized construction path.

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