Intelligent construction simulation system based on BIM and virtual reality fusion

Abstract

The invention discloses an intelligent construction simulation system based on BIM and virtual reality fusion, and the processing flow of the intelligent construction simulation system comprises the steps: constructing a three-dimensional digital model according to a design drawing, geological exploration data and a planned construction progress; a three-dimensional digital model is used as a core, a digital twinning model of a bridge is built in combination with the Internet of Things, a sensor and a big data analysis technology, the three-dimensional digital model and the digital twinning model are integrated to a VR development platform, and virtual simulation is performed on a bridge construction site; inputting real-time sensing data acquired by various sensors into the digital twinborn model, and constructing a dynamic risk field; and carrying out risk propagation path analysis and dynamic early warning threshold adjustment according to the dynamic risk field to obtain a risk thermodynamic diagram, an early warning level and a recommended coping strategy. According to the invention, the limitation of a traditional construction management mode is broken through, regulation and control can be carried out in advance according to the predicted risk, and a more comprehensive, accurate and real-time simulation and management means is provided for the bridge construction process.

Description

Technical Field

[0001] The present invention relates to the technical field of building information model application, and in particular to an intelligent construction simulation system based on the fusion of BIM and virtual reality. Background Art

[0002] The simple application carriers of BIM technology are fixed equipment and mobile devices, which are mainly displayed on two-dimensional display screens or VR headsets, which are separated from the actual construction site. This causes the separation of BIM information and the actual construction site information, making it easy for errors to occur in information transmission, which is not conducive to the refined management and standardized construction of the project.

[0003] Domestic BIM applications are still in the stage of popularization and promotion, and a series of applications are still in the practice or initial application exploration stage. VR technology is also a technology that has only been paid attention to this year, and the technology is becoming more mature.

[0004] The information disclosed in this background section is only intended to enhance understanding of the overall background of the invention and should not be considered as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention The purpose of this invention is to provide an intelligent construction simulation system based on the integration of BIM and virtual reality, which innovatively integrates BIM technology, digital twin technology, virtual reality technology and big data analysis technology to form an organic whole, breaking through the limitations of traditional construction management models. It can also be regulated in advance based on predicted risks, providing a more comprehensive, accurate and real-time simulation and management means for the bridge construction process.

[0005] To achieve the above objectives, the present invention provides an intelligent construction simulation system based on the integration of BIM and virtual reality. The intelligent construction simulation system is applied to the field of bridge construction. Various sensors are arranged at the bridge construction site, and data from the various sensors is collected and processed by a processor. The system comprises the following steps: S1, constructing a three-dimensional digital model of the bridge based on the bridge design drawings, geological survey data, and planned construction schedule, wherein the three-dimensional digital model includes the three-dimensional geometric shape and spatial position information of the bridge components, the layout and parameters of the equipment system, and the specifications and arrangement of the construction materials; S2, using the three-dimensional digital model as the core, combining the Internet of Things, sensors, and big data analysis technologies to build a digital twin model of the bridge, integrating the three-dimensional digital model and the digital twin model into a VR development platform, and performing a virtual simulation of the bridge construction site; S3, inputting real-time sensor data collected by various sensors into the digital twin model to obtain the current construction progress, and constructing a dynamic risk field in the three-dimensional digital model based on the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data. The dynamic risk field includes a structural risk factor, a progress risk factor, and an environmental risk factor; S4. Based on the dynamic risk field, risk propagation path analysis and dynamic warning threshold adjustment are performed to obtain a risk heat map, warning level, and recommended response strategies.

[0006] Furthermore, in step S3, a dynamic risk field is constructed in the three-dimensional digital model according to the current construction progress, the planned construction progress, the real-time sensor data and the environmental monitoring data, and the steps include: Mapping the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data to a unified spatiotemporal coordinate system, and calculating an initial structural risk factor, an initial progress risk factor, and an initial environmental risk factor based on the converted data; Normalizing the initial structural risk factor, the initial progress risk factor, and the initial environmental risk factor to form an initial risk field; Based on the initial risk field, a risk diffusion equation is defined and solved by finite difference to obtain a dynamic risk field. The dynamic risk field is used to predict the risk distribution in a preset time period in the future.

[0007] Furthermore, in step S4, the risk propagation path analysis and dynamic warning threshold adjustment are performed based on the dynamic risk field to obtain a risk heat map, warning level and recommended response strategy, including the following steps: Inputting the dynamic risk field into a spatiotemporal convolutional neural network model to obtain a modified risk field; The modified risk field is subjected to risk propagation path analysis and dynamic warning threshold adjustment to obtain the risk heat map, the warning level and the recommended response strategy.

[0008] Furthermore, the calculation formula of the risk diffusion equation is as follows: ; in, represents anisotropic diffusion data, represents the risk attenuation coefficient, represents external risk sources, represents the dynamic risk field.

[0009] Furthermore, after step S3, the method further includes: The current construction progress is compared and analyzed with the planned construction progress, and the progress deviation rate is calculated to determine whether to issue an early warning based on the progress deviation rate, wherein the current construction progress includes the actual workload completed by each construction process at the current time node.

[0010] Furthermore, after step S4, the method further includes: S5. In the digital twin model, the structural deformation data and stress change data in the real-time sensing data are compared and analyzed with the structural deformation range and stress change range in the three-dimensional digital model. Once data anomalies are monitored, a quality and safety warning is issued.

[0011] Furthermore, the various sensors include displacement sensors, stress sensors, temperature sensors and humidity sensors. The displacement sensors are used to monitor the structural deformation data of the bridge under construction, the stress sensors are used to monitor the stress change data of the bridge under construction, and the environmental sensors are used to monitor the construction environment parameter data of the construction site. The steps of comparing and analyzing the current construction progress with the planned construction progress, calculating a progress deviation rate, and determining whether to issue an early warning based on the progress deviation rate include: Inputting the real-time sensor data into the digital twin model to obtain the current construction progress; Calculating a progress deviation rate between the current construction progress and the planned construction progress; If the progress deviation rate is greater than 0, it indicates that the current construction progress is ahead of schedule; if the progress deviation rate is less than 0, it indicates that the current construction progress is behind schedule.

[0012] Furthermore, step S5 includes: Comparing the structural deformation data with the structural deformation range in the three-dimensional digital model to calculate the structural deformation difference; Comparing the stress change data with the stress change range to calculate the stress-deformation difference; Statistical analysis methods are used to analyze data change trends, and structural deformation thresholds and stress deformation thresholds are pre-set. When the structural deformation difference exceeds the structural deformation threshold or the stress deformation difference exceeds the stress deformation threshold, the digital twin model immediately triggers an early warning mechanism. Quality and safety warning information is sent to construction management personnel and technical personnel through sound and light alarms, SMS push, and system pop-up windows.

[0013] Furthermore, it also includes: Converting the process standards in industry specifications and design drawings into a parameterized rule library that can be recognized by the three-dimensional digital model; Laser scanning and cameras are used to collect physical data of the construction site, and the data is compared with the parameterized rule base to output process deviations.

[0014] Compared with the existing technology, an intelligent construction simulation system based on the integration of BIM and virtual reality according to the present invention first constructs a three-dimensional digital model of the bridge based on the bridge's design drawings, geological survey data, and planned construction progress; then, with the three-dimensional digital model as the core, a digital twin model of the bridge is constructed, and the digital twin model is integrated into the VR development platform to perform a virtual simulation of the bridge construction site; then, the real-time sensor data collected by various sensors is input into the digital twin model to obtain the current construction progress, and a dynamic risk field is constructed based on the current construction progress, planned construction progress, real-time sensor data and environmental monitoring data, and risk analysis is performed based on the dynamic risk field, and early warnings are issued in high-risk areas and corresponding response strategies are adopted. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a data processing flow chart within an intelligent construction simulation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0017] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "circumferential", "radial", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0018] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0019] In the embodiments of the present application, at least one means one or more, and multiple means two or more than two. In the description of the present application, the terms "first", "second", "third" and the like are only used to distinguish the description purposes, and cannot be understood as indicating or implying relative importance, nor can be understood as indicating or implying order. In addition, the terms "first", "second" are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more than two, unless otherwise specifically limited.

[0020] In the description of the present application, the reference "one embodiment" or "some embodiments" and the like means that the specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Therefore, in the description of the present application, the terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0021] It should be noted that the "connection" in the embodiments of the present application can be understood as an electrical connection, and the connection between two electrical elements can be a direct or indirect connection between the two electrical elements. For example, A and B are connected, which can be that A and B are directly connected, or A and B are indirectly connected through one or more other electrical elements.

[0022] The intelligent construction simulation system provided by the embodiment of the present application is applied to the field of bridge construction, various sensors are arranged at the bridge construction site, and the data of various sensors are collected and processed by the processor, Figure 1 The data processing flowchart in the intelligent construction simulation system provided by the embodiment of the present application is shown in Figure 1 The processing process includes the following steps: S1, according to the design drawing of the bridge, the geological exploration data, the planned construction progress, the three-dimensional digital model of the bridge is constructed, the three-dimensional digital model includes the three-dimensional geometric shape and spatial position information of the bridge component, the layout and parameters of the equipment system, the specification and arrangement of the construction material; Among them, the bridge component includes the main beam, the pier, the abutment and the base body, the equipment system includes the lighting system, the monitoring system and the ventilation system, the construction material includes the reinforcing bar, the formwork, the planned construction progress includes the work task to be completed at each time node.

[0023] In the embodiment of the present application, each component of the bridge is associated with an element of the BIM (Building Information Modeling) model, each element in the BIM model corresponds to a component, and the element further includes construction operation information of the component, such as construction start time, expected completion time, and construction process requirements. Through such association, the construction progress plan can be intuitively displayed in the BIM model, and the state change of each model element corresponding to each construction stage can be clearly presented, which facilitates construction personnel and management personnel to more intuitively and accurately understand the construction process and perform construction progress management and resource allocation.

[0024] For example, in the bridge substructure construction stage, the "bridge pier pouring" task is associated with the model element representing the bridge pier in the BIM model, and it is clear that the model element is to be operated in this stage, and the information such as construction start time, expected completion time, and construction process requirements is bound to the model element.

[0025] S2, taking the three-dimensional digital model as the core, combining Internet of Things, sensors, and big data analysis technology to build a digital twin model of the bridge, integrating the three-dimensional digital model and the digital twin model into a VR development platform, and virtually simulating the bridge construction site; In the embodiment of the present application, the BIM model is taken as the core, and the digital twin model is built by combining Internet of Things (IoT), sensors, big data analysis, and other technologies. Various sensors are arranged at the bridge construction site, such as displacement sensors for monitoring structural deformation, stress sensors for monitoring stress changes, and environmental sensors for monitoring construction environment parameters (such as temperature, humidity, wind speed, and rainfall, etc.). The data collected by the sensors in real time is transmitted to the digital twin model through a wireless network, so that the digital twin model can reflect the real state of the bridge construction site in real time, and a virtual mapping highly consistent with the actual bridge construction process is constructed, realizing precise synchronization and interaction between the virtual and the real.

[0026] Virtual reality (VR) technology is adopted to integrate the BIM model and the digital twin model into a VR environment. With the help of Unity, Unreal Engine, and other VR development platforms, the BIM model and the digital twin model are optimized to enable smooth operation on the VR development platform (such as HTC Vive, Oculus Rift, etc.). Immersive interactive experience is provided for users, and users can "enter" the virtual environment of the bridge construction site by wearing a VR headset, and can perform omnidirectional roaming, viewing, and operation to intuitively perceive and deeply understand the bridge construction process.

[0027] S3, inputting real-time sensor data collected by various sensors into the digital twin model to obtain the current construction progress, and constructing a dynamic risk field in the three-dimensional digital model based on the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data. The dynamic risk field includes a structural risk factor, a progress risk factor, and an environmental risk factor; Specifically, this step includes the following sub-steps: S31, mapping the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data to a unified spatiotemporal coordinate system, and calculating an initial structural risk factor, an initial progress risk factor, and an initial environmental risk factor based on the converted data; S32, normalizing the initial structural risk factor, the initial progress risk factor, and the initial environmental risk factor to form an initial risk field; S33: Based on the initial risk field, define a risk diffusion equation and solve it by finite difference to obtain a dynamic risk field. The dynamic risk field is used to predict the risk distribution in a future preset time period.

[0028] In an embodiment of the present invention, the current construction progress, the planned construction progress, the real-time sensing data and the environmental monitoring data are input, wherein the real-time sensing data includes stress data, deformation data, bridge temperature data and bridge vibration data, and the environmental monitoring data includes wind speed, rainfall and ambient temperature.

[0029] Data from different sources are mapped to a unified space-time coordinate system, and then risk factors are calculated to construct a dynamic risk field. The dynamic risk field includes three aspects: initial structural risk factor, initial progress risk factor, and initial environmental risk factor. Among them, the initial structural risk factor is used to assess the risks of stress exceeding limits and abnormal deformation of bridges under construction, and quantify the structural risk; the initial progress risk factor is used to assess the delay rate of key construction paths during the construction process, and quantify the construction progress risk; the initial environmental risk factor is used to assess the impact of strong winds on bridge hoisting, and quantify the environmental risk.

[0030] Normalize these factors and normalize all factors to the interval [0,1] to form the initial risk field. ,in, represents the initial structural risk factor, represents the initial schedule risk factor, represents the initial environmental risk factor, represents time. Then, based on the initial risk field, the risk diffusion equation is defined and solved by finite difference to obtain the dynamic wind field. The risk diffusion equation is defined as follows: ; in, Indicates the diffusion data of each risk factor in different directions in the initial risk field. The speed of risk propagation in different directions is different. For example, the vertical direction of risk diffusion is faster than the horizontal direction. Represents the risk attenuation coefficient, and the risk naturally decreases over time, Indicates external risk sources, such as sudden loads, construction errors, etc. represents the dynamic risk field.

[0031] Wherein, D uses a diffusion matrix to represent the diffusion data in different directions. In the embodiment of the present invention, D is a 3*3 matrix, and different diffusion coefficients are assigned in different directions: ; During bridge construction, risk propagation speeds vary in different directions. D is a key physical parameter matrix used to quantify the propagation characteristics of risks (such as structural stress, deformation, and construction conflicts) in different spatial directions. The physical meaning and mathematical definition of its matrix elements are as follows: Diagonal elements 、 and Represent the risk diffusion ability on the X, Y and Z axes respectively, where It represents the diffusion coefficient of risk along the longitudinal direction of the bridge (such as the axial direction of the main beam). For example, the risk of welding defects in steel box beams spreads rapidly along the length of the beam. It represents the diffusion coefficient of risk in the transverse direction (such as the width of the bridge deck), such as the transverse extension of the bridge deck concrete crack; It represents the diffusion coefficient of risk in the vertical direction (gravity direction), for example, the chain reaction caused by the settlement of bridge pier foundation is transmitted vertically.

[0032] In addition, the off-diagonal elements represent the coupling diffusion effect of risks in different directions. = , which represents the interaction between longitudinal and transverse risks, such as the redistribution of longitudinal stress caused by transverse vibration of the main beam (such as wind vibration); = , which represents the coupling of longitudinal and vertical risks, such as the change of the main cable tension (longitudinal) caused by the tilt of the tower (vertical); = , which represents the coupling of lateral and vertical risks, such as local overload of the bridge deck (vertical) leading to shear deformation of the diaphragm (lateral).

[0033] In an embodiment of the present invention, an anisotropic risk diffusion equation is used in bridge construction risk prediction. Traditional isotropic diffusion models (such as the heat conduction equation) assume that risks are evenly propagated. However, the anisotropic diffusion equation can more accurately simulate the risk propagation laws in real construction scenarios by introducing direction-dependent diffusion characteristics.

[0034] Then, in this embodiment of the present invention, the risk diffusion equation is solved using the finite difference method. This method can employ the Crank-Nicolson implicit scheme to ensure numerical stability or utilize GPU-accelerated computation for real-time updates. Finally, the dynamic risk field R(x, y, z, t) is obtained, predicting the risk distribution for the next 24 to 72 hours.

[0035] S4. Based on the dynamic risk field, risk propagation path analysis and dynamic warning threshold adjustment are performed to obtain a risk heat map, warning level, and recommended response strategies.

[0036] This step includes the following sub-steps: S41, inputting the dynamic risk field into a spatiotemporal convolutional neural network model to obtain a modified risk field; S42: Perform risk propagation path analysis and dynamic warning threshold adjustment on the modified risk field to obtain the risk heat map, the warning level, and the recommended response strategy.

[0037] In the embodiment of the present invention, the dynamic risk field and historical accident data (risk events of similar projects) are input into the spatiotemporal convolutional neural network model. The spatiotemporal convolutional neural network model is first trained using the historical accident data. Then, the dynamic risk field is input into the trained spatiotemporal convolutional neural network model for correction to obtain a corrected risk field. The corrected risk field is then subjected to risk propagation paths, specifically Use the gradient ascent method to find the peak point of the risk field, form a risk propagation chain, and calculate the risk entropy (a measure of uncertainty); finally, adjust the dynamic warning threshold, adopt an adaptive threshold (such as one based on extreme value theory EVT), and combine it with counterfactual analysis (simulating the effects of different intervention measures) to obtain a risk heat map, warning level, and recommended response strategies.

[0038] Among them, the risk heat map is used to visualize high-risk areas and show the areas with higher risks so that relevant personnel can intuitively understand where the risks lie and take timely measures; the warning levels include low, medium, high and emergency, and the recommended response strategies include adjusting the construction sequence and strengthening the structure.

[0039] As an implementation manner, after step S3, the following steps are further included: The current construction progress is compared and analyzed with the planned construction progress, and the progress deviation rate is calculated to determine whether to issue an early warning based on the progress deviation rate, wherein the current construction progress includes the actual workload completed by each construction process at the current time node.

[0040] Among them, various types of sensors include displacement sensors, stress sensors, temperature sensors and humidity sensors. Displacement sensors are used to monitor the structural deformation data of the bridge under construction, stress sensors are used to monitor the stress change data of the bridge under construction, and environmental sensors are used to monitor the construction environment parameter data of the construction site; The steps of comparing and analyzing the current construction progress with the planned construction progress, calculating a progress deviation rate, and determining whether to issue an early warning based on the progress deviation rate include: Inputting the real-time sensor data into the digital twin model to obtain the current construction progress; Calculate the progress deviation rate between the current construction progress and the planned construction progress. The specific calculation formula is: , SVR represents the progress deviation rate, EV represents the current construction progress, and PV represents the planned construction progress; If the progress deviation rate is greater than 0, it indicates that the current construction progress is ahead of schedule; if the progress deviation rate is less than 0, it indicates that the current construction progress is behind schedule.

[0041] In this embodiment of the present invention, big data analysis technology is used to conduct a detailed comparison of the current construction progress with the planned construction progress within a digital twin model. This analysis analyzes construction progress deviations, determining whether the actual progress lags behind or ahead of the planned progress, the extent of the deviation, and its impact on subsequent processes. The results of the construction progress deviation analysis include the specific deviation value, the construction processes involved, and an assessment of the impact on subsequent construction.

[0042] In a BIM model for bridge construction, model elements encompass a wide range of content. From a structural perspective, this includes the precise 3D geometry and spatial position of bridge components such as the main beam, piers, abutments, and foundations. From an equipment systems perspective, this includes the layout and parameters of electromechanical equipment like lighting, monitoring, and ventilation systems. Furthermore, from a construction details perspective, this includes the specifications and placement of specific construction materials like rebar and formwork. These model elements accurately represent the properties, characteristics, and interrelationships of each bridge component in digital form.

[0043] Associating tasks in different construction stages with model elements means that, in the process of constructing a 4D BIM model (a three-dimensional model plus a time dimension), the work tasks to be completed in each stage in the construction plan are associated with the corresponding model elements. For example, in the construction stage of the bridge substructure, the task of "bridge pier pouring" is associated with the model element representing the bridge pier in the BIM model, and it is clear that this model element will be operated in this stage, including the construction start time, the expected completion time, the construction process requirements, and other information will be bound to this model element. Through this association, the construction progress plan can be intuitively displayed in the BIM model, and the state changes of the model elements corresponding to each construction stage can be clearly presented, which facilitates construction personnel and management personnel to more intuitively and accurately understand the construction process and conduct construction progress management and resource allocation.

[0044] As an implementation, after step S4, further comprising: S5, in the digital twin model, comparing and analyzing the structure deformation data and the stress change data in the real-time sensing data with the structure deformation range and the stress change range in the three-dimensional digital model, and issuing a quality and safety warning once data anomalies are monitored.

[0045] The structure deformation data is compared with the structure deformation range in the three-dimensional digital model to calculate a structure deformation difference value; The stress change data is compared with the stress change range to calculate a stress deformation difference value; A statistical analysis method is used to analyze the data change trend, and a structure deformation threshold value and a stress deformation threshold value are pre-set, and when the structure deformation difference value exceeds the structure deformation threshold value or the stress deformation difference value exceeds the stress deformation threshold value, the digital twin model immediately triggers a warning mechanism; Quality and safety warning information is issued to construction management personnel and technical personnel through sound and light alarms, SMS push, and system pop-up windows.

[0046] In the embodiment of the application, the structure deformation data is compared with the design allowed deformation range in the three-dimensional digital model to calculate the difference between the actual deformation amount and the design deformation threshold value; the stress change data is compared with the material stress design threshold value to calculate the deviation rate of the actual stress value and the design stress threshold value. A statistical analysis method is used to analyze the data change trend to determine whether the structure deformation and the stress change are within a reasonable range. For example, the moving average method is used to analyze the change trend of the deformation data, and if the deformation data at a plurality of consecutive time points exceeds the design threshold value and shows an increasing trend, it is considered to be abnormal.

[0047] Pre-set quality and safety warning thresholds are used. When the difference between structural deformation data and design parameters exceeds the set deformation warning threshold, or the deviation rate between stress change data and design parameters exceeds the stress warning threshold, or the data change trend indicates potential risks, the digital twin model immediately triggers the warning mechanism. Quality and safety warning information is sent to construction managers, technicians, and other relevant personnel through various means, including audio and visual alarms, SMS push notifications, and system pop-up windows.

[0048] As an implementation method, it further includes: Converting the process standards in industry specifications and design drawings into a parameterized rule library that can be recognized by the three-dimensional digital model; Laser scanning and cameras are used to collect physical data of the construction site, and the data is compared with the parameterized rule base to output process deviations.

[0049] In this embodiment of the present invention, real-time construction data is compared with a BIM process standard library to calculate deviations and assess risk levels. Input: preprocessed construction entity data (point cloud model, AI recognition parameters), and process standard parameters in the BIM model (e.g., a ±5mm tolerance for formwork installation).

[0050] Key geometric features, such as formwork edge lines and rebar distribution points, are extracted from the point cloud model. Algorithms (such as ICP Iterative Closest Point) are used to calculate the spatial deviation between the physical features and the BIM model. For example, the distance deviation between the measured edge line and the BIM model design line is calculated. For rebar tying, the difference between the measured rebar spacing identified by AI and the BIM standard value, which might be a design spacing of 200mm, is calculated. Risk grading is performed based on the ratio of the deviation value to the threshold, with warning levels assigned, such as a minor deviation ≤30% and a severe deviation >80%. Outputs include a deviation analysis report and risk level labels. The deviation analysis report includes a 3D visual deviation cloud map and the specific deviation value. Risk level labels include green for acceptable, yellow for warning, and red for exceeding the standard.

[0051] In summary, the embodiments of the present invention provide an intelligent construction simulation system based on the integration of BIM and virtual reality, which has the following advantages: (1) Construction visualization: Construction personnel, managers, and other project participants can immerse themselves in the bridge's design and construction process, transitioning from traditional 2D drawings to an intuitive 3D immersive experience. The spatial relationships, construction sequence, and construction techniques of the bridge's various structural components can be clearly observed, greatly improving their understanding of complex bridge projects and effectively avoiding construction errors caused by poor communication or misunderstandings.

[0052] (II) Precise management of construction progress (analysis of construction progress deviation), based on 4D BIM model and digital twin technology, real-time monitoring of bridge construction progress. The actual construction progress and the planned progress are compared and analyzed in the virtual model. Once the progress deviation is found, the system can timely issue a warning and provide adjustment suggestions for construction personnel through data analysis. For example, when a certain construction process is delayed, the system can automatically analyze the impact of the delay on the subsequent process and the overall construction period, helping the construction team to quickly develop reasonable catch-up measures to ensure the smooth progress of the project according to the plan, effectively avoiding the cost increase caused by the delay of the construction period.

[0053] (III) Ensure construction quality and safety (structural safety risk assessment), in the digital twin model, through sensors to collect real-time data of bridge structure stress, deformation, etc., and compare and analyze with the design parameters in the BIM model. Once the data anomaly is detected, the system immediately issues a quality and safety warning, prompting the construction personnel to take timely measures to handle it, preventing the occurrence of quality and safety accidents. At the same time, VR technology is used for construction safety training and drilling, allowing construction personnel to experience various dangerous scenarios in a virtual environment, improving safety awareness and emergency handling capacity.

[0054] (IV) Optimize construction resource allocation (analysis of construction resource utilization efficiency), with the help of big data analysis technology, the use of human resources, materials, equipment, etc. in the construction process is monitored and analyzed in real time. According to the construction progress and actual demand, accurately allocate various resources to avoid waste and shortage of resources. For example, by analyzing the use frequency and inventory of materials, the procurement and arrival time of materials can be reasonably arranged; according to the running state and use efficiency of equipment, the scheduling and maintenance plan of equipment can be optimized to improve the utilization efficiency of construction resources and reduce construction cost.

[0055] (V) Process standardization verification: Through the built-in process standard library in the BIM model (such as template installation precision, steel bar binding interval), combined with the real-time construction data collected by digital twin (such as laser scanning point cloud, AI image recognition), the automatic early warning of process execution deviation is realized.

[0056] (VI) Material quality traceability: In the BIM model, associate material information (such as steel reinforcement batch, concrete proportioning), combined with Internet of Things sensor monitoring data such as temperature and humidity during transportation, warehouse environment, etc., form a material full life cycle quality file. Once quality problems are found, the responsible link can be quickly located, such as a bridge project that uses this technology to shorten the material traceability time from 72 hours to 15 minutes.

[0057] The various modules in the above intelligent construction simulation system based on BIM and virtual reality fusion can be realized by software, hardware, or a combination thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0058] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0060] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An intelligent construction simulation system based on the integration of BIM and virtual reality, characterized in that: The intelligent construction simulation system is applied to the field of bridge construction. Various sensors are arranged at the bridge construction site, and data from the various sensors are collected and processed by a processor. The system includes the following steps: S1, constructing a three-dimensional digital model of the bridge based on the bridge design drawings, geological survey data, and planned construction schedule, wherein the three-dimensional digital model includes the three-dimensional geometric shape and spatial position information of the bridge components, the layout and parameters of the equipment system, and the specifications and arrangement of the construction materials; S2, using the three-dimensional digital model as the core, combining the Internet of Things, sensors, and big data analysis technologies to build a digital twin model of the bridge, integrating the three-dimensional digital model and the digital twin model into a VR development platform, and performing a virtual simulation of the bridge construction site; S3, inputting real-time sensor data collected by various sensors into the digital twin model to obtain the current construction progress, and constructing a dynamic risk field in the three-dimensional digital model based on the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data. The dynamic risk field includes a structural risk factor, a progress risk factor, and an environmental risk factor; S4. Based on the dynamic risk field, risk propagation path analysis and dynamic warning threshold adjustment are performed to obtain a risk heat map, warning level, and recommended response strategies.

2. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 1, characterized in that: In step S3, the real-time sensing data includes stress data and deformation data, and the environmental monitoring data includes wind speed, rainfall, and ambient temperature. In the three-dimensional digital model, a dynamic risk field is constructed based on the current construction progress, the planned construction progress, the real-time sensing data, and the environmental monitoring data. The steps include: Mapping the current construction progress, the planned construction progress, the real-time sensor data, and the environmental monitoring data to a unified spatiotemporal coordinate system, and calculating an initial structural risk factor, an initial progress risk factor, and an initial environmental risk factor based on the converted data; Normalizing the initial structural risk factor, the initial progress risk factor, and the initial environmental risk factor to form an initial risk field; Based on the initial risk field, a risk diffusion equation is defined and solved by finite difference to obtain a dynamic risk field. The dynamic risk field is used to predict the risk distribution in a preset time period in the future.

3. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 1, characterized in that: In step S4, the risk propagation path analysis and dynamic warning threshold adjustment are performed based on the dynamic risk field to obtain a risk heat map, warning level and recommended response strategy. The steps include: Inputting the dynamic risk field into a spatiotemporal convolutional neural network model to obtain a modified risk field; The modified risk field is subjected to risk propagation path analysis and dynamic warning threshold adjustment to obtain the risk heat map, the warning level and the recommended response strategy.

4. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 2, characterized in that: The calculation formula of the risk diffusion equation is as follows: ； in, represents the diffusion data of each risk factor in different directions in the initial risk field, represents the risk attenuation coefficient, represents external risk sources, represents the dynamic risk field.

5. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 1, characterized in that: After step S3, the method further includes: The current construction progress is compared and analyzed with the planned construction progress, and the progress deviation rate is calculated to determine whether to issue an early warning based on the progress deviation rate, wherein the current construction progress includes the actual workload completed by each construction process at the current time node.

6. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 1, characterized in that: After step S4, the method further includes: S5. In the digital twin model, the structural deformation data and stress change data in the real-time sensing data are compared and analyzed with the structural deformation range and stress change range in the three-dimensional digital model. Once data anomalies are monitored, a quality and safety warning is issued.

7. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 5, characterized in that: Various types of sensors include displacement sensors, stress sensors, temperature sensors, and humidity sensors. Displacement sensors are used to monitor the structural deformation data of the bridge under construction. Stress sensors are used to monitor the stress change data of the bridge under construction. Environmental sensors are used to monitor the construction environment parameter data at the construction site. The steps of comparing and analyzing the current construction progress with the planned construction progress, calculating a progress deviation rate, and determining whether to issue an early warning based on the progress deviation rate include: Inputting the real-time sensor data into the digital twin model to obtain the current construction progress; Calculating a progress deviation rate between the current construction progress and the planned construction progress; If the progress deviation rate is greater than 0, it indicates that the current construction progress is ahead of schedule; if the progress deviation rate is less than 0, it indicates that the current construction progress is behind schedule.

8. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 6, characterized in that: Step S5 includes: Comparing the structural deformation data with the structural deformation range in the three-dimensional digital model to calculate the structural deformation difference; Comparing the stress change data with the stress change range to calculate the stress-deformation difference; Statistical analysis methods are used to analyze data change trends, and structural deformation thresholds and stress deformation thresholds are pre-set. When the structural deformation difference exceeds the structural deformation threshold or the stress deformation difference exceeds the stress deformation threshold, the digital twin model immediately triggers an early warning mechanism. Quality and safety warning information is sent to construction management personnel and technical personnel through sound and light alarms, SMS push, and system pop-up windows.

9. The intelligent construction simulation system based on the integration of BIM and virtual reality according to claim 1, characterized in that: Also includes: Converting the process standards in industry specifications and design drawings into a parameterized rule library that can be recognized by the three-dimensional digital model; Laser scanning and cameras are used to collect physical data of the construction site, and the data is compared with the parameterized rule base to output process deviations.

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