A method for constructing prefabricated buildings based on BIM

Abstract

The present invention relates to the technical field of prefabricated buildings, and in particular to a prefabricated construction method based on BIM. The method comprises the following steps: obtaining construction site data, design drawing data and construction drawing data; performing data preprocessing on the construction site data to generate standardized model data; obtaining geographic information system data; performing three-dimensional laser BIM model construction based on the standardized model data and the geographic information system data to generate an initial BIM model; performing geometric calibration and material property assignment on the initial BIM model based on the design drawings and the construction drawings to generate an assembled BIM model. The present invention provides a systematic, scientific and efficient solution for the prefabricated building construction method through data standardization, accurate model construction and multi-dimensional comprehensive evaluation, which helps to improve the management level and construction efficiency of the entire construction industry.

Description

Technical Field

[0001] The present invention relates to the technical field of prefabricated buildings, and in particular to a method for constructing prefabricated buildings based on BIM. Background Art

[0002] BIM technology integrates the geometry, materials, cost and time information of buildings through three-dimensional digital models to achieve holographic display and dynamic management. In the design stage, BIM can perform precise modeling and virtual assembly to ensure the feasibility and optimization of the design; in the construction stage, the BIM model can accurately locate and simulate the installation of prefabricated components, reduce errors and rework, and improve construction efficiency and quality; in the operation and maintenance stage, BIM, as a dynamic database, records and manages building information to provide support for subsequent maintenance and renovation. BIM's collaborative work platform promotes efficient collaboration among design, construction, supervision and operation parties, shares and updates project information in real time, and reduces errors and delays in information transmission. However, existing BIM technology still faces challenges in data integration and processing, especially in the compatibility of different data formats and standards. Although the BIM platform promotes multi-party collaboration, due to the different technical levels and informationization levels of all parties, there are still problems of delayed information transmission and poor communication, which affects project management efficiency. In addition, the existing technology lacks efficient dynamic simulation and real-time monitoring methods in progress and cost control, resulting in the lag of project control. When faced with unexpected problems, BIM's construction method lacks an effective risk warning and emergency response mechanism, making it difficult to adjust quickly, affecting the smooth progress of the project. Summary of the invention

[0003] Based on this, it is necessary to provide a BIM-based prefabricated building construction method to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing an assembled building based on BIM is provided, the method comprising the following steps:

[0005] Step S1: Acquire building site data, design drawing data and construction drawing data; perform data preprocessing on the building site data to generate standardized model data;

[0006] Step S2: Acquire geographic information system data; construct a three-dimensional laser BIM model based on the standardized model data and the geographic information system data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an assembled BIM model;

[0007] Step S3: Perform dimensional simulation on the prefabricated BIM model to generate a BIM dimensional simulation plan; perform a comprehensive evaluation of the BIM dimensional simulation plan in terms of time, cost, and prefabricated assembly simulation to generate a multi-dimensional comprehensive result;

[0008] Step S4: Use the multi-dimensional comprehensive results and BIM dimension simulation solutions to build a management strategy and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

[0009] The beneficial effect of the present invention is that by acquiring building site data, design drawing data and construction drawing data, and pre-processing the building site data, standardized model data is generated, which lays a solid foundation for the subsequent BIM model construction. Standardized data not only improves the consistency and accuracy of data, but also provides convenience for further processing of data. Obtain geographic information system data and construct a three-dimensional laser BIM model based on standardized model data to generate an initial BIM model. On this basis, the initial BIM model is geometrically calibrated and material properties are assigned through design drawings and construction drawings to generate an assembled BIM model. This step not only enhances the authenticity and accuracy of the model, but also ensures the operability and reliability of the model in practical applications. The assembled BIM model is dimensionally simulated to generate a BIM dimensional simulation scheme. By comprehensively evaluating the BIM dimensional simulation scheme in terms of time, cost and assembled assembly simulation, a multi-dimensional comprehensive result is generated. This process takes into account a variety of factors, not only provides a comprehensive evaluation standard, but also provides a scientific basis for decision-making. The generation of multi-dimensional comprehensive results enables a more accurate assessment of the feasibility of the project, thereby optimizing resource allocation and reducing cost waste. Finally, the management strategy is constructed using the multi-dimensional comprehensive results and BIM dimension simulation scheme to generate a BIM prefabricated management strategy to complete the prefabricated building construction operation. This process improves the scientificity and effectiveness of management by converting complex data and models into specific management strategies. This method not only improves the efficiency of prefabricated building management, but also enhances the overall controllability and predictability of the project. Overall, this method provides a systematic, scientific and efficient solution for the construction of prefabricated building methods through data standardization, precise model construction and multi-dimensional comprehensive evaluation, which helps to improve the management level and construction efficiency of the entire construction industry.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire building site data, design drawing data and construction drawing data;

[0012] Step S12: Cleaning the construction site data to generate construction site cleaned data;

[0013] Step S13: Perform data standardization processing on the building site cleaning data to generate standardized model data.

[0014] The present invention obtains building site data, design drawing data and construction drawing data, and this process ensures the comprehensiveness and diversity of data sources, and provides rich original data for subsequent data processing and analysis. These data cover the actual situation on site, design intent and construction plan, and lay the foundation for building an accurate and comprehensive BIM model. Data cleaning is performed on the building site data to generate building site cleaning data. This process improves the quality and consistency of data by removing redundant data, correcting erroneous data and completing missing data. Data cleaning can not only eliminate unnecessary information and reduce the impact of noise on the model, but also ensure the accuracy and reliability of data, thereby improving the accuracy of subsequent data processing and analysis. Data standardization is performed on the building site cleaning data to generate standardized model data. Data standardization eliminates the heterogeneity between different data sources by converting data into a unified format and dimension, and enhances the comparability and integrability of data. Standardized data not only helps to improve the utilization efficiency of data, but also facilitates the integration and collaboration between different data sets. This process can ensure the consistency and operability of data in different data analysis and model building processes, and reduce errors and deviations caused by inconsistent data formats.

[0015] Preferably, step S2 comprises the following steps:

[0016] Step S21: Acquire geographic information system data, wherein the geographic information system data includes terrain, elevation and landform features;

[0017] Step S22: using 3D laser scanning technology to establish network topology nodes for geographic information system data, and generating a geographic information network topology map; using standardized model data to fill in network node data for the geographic information network topology map, and generating prefabricated building node filling data;

[0018] Step S23: construct a three-dimensional laser BIM model based on the prefabricated building node filling data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an prefabricated BIM model.

[0019] The present invention obtains geographic information system data, including terrain, elevation and geomorphic features, which fully reflect the natural conditions and geographical environment of the building site and provide important basic data for subsequent model construction. The acquisition of terrain, elevation and geomorphic feature data enables the model to more accurately reflect the actual site conditions, improving the reality and operability of the model. Use three-dimensional laser scanning technology to establish network topology nodes for geographic information system data and generate a geographic information network topology map. This step converts complex geographic information into a structured network topology map, making data processing and analysis more intuitive and efficient. The application of three-dimensional laser scanning technology improves the accuracy and efficiency of data acquisition and ensures the accuracy and integrity of the geographic information network topology map. Use standardized model data to fill the network node data of the geographic information network topology map to generate prefabricated building node filling data. This process makes the model more comprehensive and accurate by integrating the building model data with the geographic information data, and improves the comprehensive utilization value of the data. A three-dimensional laser BIM model is constructed based on the prefabricated building node filling data to generate an initial BIM model. By applying the building node filling data to the construction of the BIM model, the initial BIM model not only has high-precision geographic information, but also can accurately reflect the spatial structure and geometric form of the building. Subsequently, the initial BIM model is geometrically calibrated and material properties are assigned based on the design drawings and construction drawings to generate an assembled BIM model. This process combines the design intent and construction plan, so that the BIM model has high-precision geometry while giving it real material properties and construction information, enhancing the practicality and operability of the model.

[0020] Preferably, step S22 includes the following steps:

[0021] Step S221: extracting geological segmentation data features by using point cloud segmentation on the terrain, elevation and geomorphic features to generate geological feature segmentation data;

[0022] Step S222: Establishing network topology nodes for the geological feature segmentation data to generate a geographic information network topology map;

[0023] Step S223: Filling the geographic information network topology map with standardized model data to generate Kalman filter filling data; filling the Kalman filter filling data with prefabricated building nodes to generate prefabricated building node filling data.

[0024] The present invention extracts geological segmentation data features by using point cloud segmentation technology for terrain, elevation and geomorphic features, and generates geological feature segmentation data. Point cloud segmentation technology refines and segments complex terrain and geomorphic data, so that geological feature data can more accurately reflect the actual terrain conditions, thereby improving the accuracy and detail of the data. The generation of geological feature segmentation data provides high-quality basic data for the subsequent establishment of network topology nodes, thereby ensuring the accuracy and comprehensiveness of geographic information. The network topology nodes are established for the geological feature segmentation data, and a geographic information network topology map is generated. By structuring the geological feature data into a network topology map, the processing and analysis of the data are made more systematic and efficient. The establishment of network topology nodes not only helps to improve the efficiency of data organization and management, but also can better support subsequent data filling and model construction. The generation of the geographic information network topology map provides a good data structure for filling standardized model data, thereby ensuring the integration and consistency of the data. The geographic information network topology map is filled with standardized model data, and Kalman filter filling data is generated. The filling of standardized model data eliminates the heterogeneity between data and improves the comparability and compatibility of data by unifying and normalizing data from different sources and formats. The generation of Kalman filter filling data dynamically adjusts and optimizes the data by applying Kalman filter technology, making the data smoother and more accurate, reducing the impact of noise and errors, and improving the stability and reliability of the data. The Kalman filter filling data is used to fill the nodes of the prefabricated building to generate the prefabricated building node filling data. This step makes the model more accurate and comprehensive by applying the processed high-quality data to the node filling of the prefabricated building model, thereby improving the comprehensive utilization value of the data.

[0025] Preferably, step S23 includes the following steps:

[0026] Step S231: acquiring point cloud data from the prefabricated BIM model through three-dimensional laser scanning; performing high-dimensional data analysis on the prefabricated building node filling data and the point cloud data to generate prefabricated building principal component analysis data and prefabricated building independent component analysis data;

[0027] Step S232: constructing a BIM three-dimensional model based on the prefabricated building principal component analysis data and the prefabricated building independent component analysis data to generate an initial BIM model;

[0028] Step S233: calibrate the geometric accuracy of the initial BIM model based on the design drawing data to generate a calibrated BIM model; assign physical and mechanical properties to the calibrated BIM model based on the construction drawing data to generate an assembled BIM model, wherein the assembled BIM model includes a timing module and a cost module.

[0029] The present invention obtains point cloud data from the assembled BIM model through three-dimensional laser scanning, and performs high-dimensional data analysis on the assembled building node filling data and point cloud data to generate the assembled building principal component analysis data and the assembled building independent component analysis data. The application of three-dimensional laser scanning technology ensures the high precision and high density of the point cloud data, so that the data can fully reflect the geometric form and spatial structure of the building. Through high-dimensional data analysis, the generation of principal component analysis data and independent component analysis data further improves the analysis depth and refinement of the data, helps to identify and extract key features and independent factors in the data, and enhances the interpretability and utilization value of the data. Based on the principal component analysis data of the assembled building and the independent component analysis data of the assembled building, the BIM three-dimensional model is constructed to generate an initial BIM model. This process integrates the results of high-dimensional data analysis, so that the initial BIM model not only has a high-precision geometric form, but also can reflect the key features and independent factors of the building, thereby improving the accuracy and comprehensiveness of the model. The data fusion of principal component analysis and independent component analysis provides a scientific basis and high-quality data support for model construction, ensuring the authenticity and reliability of the model. The geometric accuracy of the initial BIM model is calibrated based on the design drawing data to generate a calibrated BIM model. This step combines the design drawing data to perform fine geometric calibration on the initial BIM model to ensure the accuracy and consistency of the model in terms of geometric form. Geometric accuracy calibration not only improves the realism of the model, but also provides an accurate geometric basis for the subsequent assignment of physical and mechanical properties. Based on the construction drawing data, the physical and mechanical properties of the calibrated BIM model are assigned to generate an assembled BIM model, where the assembled BIM model includes a timing module and a cost module. This process combines the construction drawing data to give the model real physical and mechanical properties, so that the model not only has geometric accuracy, but also can reflect the actual physical performance and mechanical characteristics of the building. The introduction of the timing module and the cost module further enhances the practicality and comprehensiveness of the model, enabling the model to support the management and analysis of time and cost, and providing comprehensive data support for the planning and implementation of assembled buildings.

[0030] Preferably, step S3 comprises the following steps:

[0031] Step S31: Perform multi-dimensional simulation on the prefabricated BIM model to generate a 4D time dimension solution and a 5D cost dimension solution;

[0032] Step S32: integrating the 4D time dimension solution and the 5D cost dimension solution into a multi-dimensional scale simulation solution to generate a BIM dimension simulation solution;

[0033] Step S33: Establish an evaluation system through multi-criteria decision making to conduct a comprehensive evaluation of the BIM dimensional simulation solution in terms of time, cost and prefabricated assembly simulation, and generate a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score.

[0034] The present invention generates a 4D time dimension scheme and a 5D cost dimension scheme by multi-dimensional simulation of the assembled BIM model. This process enables the project to conduct a comprehensive visual analysis of the time schedule and cost of the project before construction, ensures that the resource allocation and scheduling at different construction stages are more reasonable, and avoids resource waste and time delays caused by information asymmetry. By simulating different construction scenarios, the optimal construction plan can be selected based on data analysis, thereby effectively reducing project risks. Focus on integrating the 4D time dimension scheme with the 5D cost dimension scheme to generate a BIM dimension simulation scheme. This process not only improves the efficiency of data interaction through comprehensive analysis of multi-dimensional scales, but also enhances the correlation between dimensions, so that the management of time and cost is no longer an isolated link. By integrating data of different dimensions, the overall status of the project can be more comprehensively understood, the construction plan can be optimized, unnecessary expenses can be reduced, and the overall benefits of the project can be improved. Finally, an evaluation system is established through multi-criteria decision-making to comprehensively evaluate the BIM dimension simulation scheme. This evaluation mechanism forms a multi-dimensional comprehensive evaluation system by quantifying multiple indicators of time, cost and assembled assembly simulation, making the decision-making process more transparent and scientific. Not only can it identify high- and low-scoring solutions, but it can also make more reasonable decisions by comparing and analyzing the pros and cons of different solutions. This data-based evaluation method can transform subjective judgments into objective analysis, significantly improving the accuracy and reliability of decision-making.

[0035] Preferably, step S31 includes the following steps:

[0036] Step S311: perform critical path timing analysis on the timing module to generate time dimension 4D data; perform 4D dynamic simulation on the time dimension 4D data to generate BIM time dimension dynamic data; generate a time dimension solution based on the BIM time dimension dynamic data to generate a 4D time dimension solution;

[0037] Step S312: Obtain a cost database; extract cost data from the cost database, and perform Monte Carlo simulation on the cost module to generate cost traceability data; dynamically combine the cost traceability data and the cost data for earned value management analysis to generate BIM dynamic cost data; generate a cost dimension plan based on the BIM dynamic cost data to generate a 5D cost dimension plan.

[0038] The present invention generates time dimension 4D data by performing critical path timing analysis on the timing module. This data generation process provides a basis for the time management of the project. The key tasks that affect the project progress are identified through the critical path method, and dynamic simulation is performed on them to generate BIM time dimension dynamic data. This dynamic data not only reflects the time changes of each stage of the project, but also enables managers to monitor the project progress in real time through visualization means and adjust the construction plan in time. The 4D time dimension scheme generated based on these dynamic data further enhances the prediction and control capabilities of the construction progress and reduces the risk of cost overruns caused by time delays. The cost database is obtained and relevant cost data is extracted. The cost module is deeply analyzed through Monte Carlo simulation to generate cost traceability data. Monte Carlo simulation, as a probability-based analysis method, can handle the impact of uncertain factors on costs, thereby providing more reliable data support for cost control. The cost traceability data and cost data are dynamically combined and analyzed for earned value management to generate BIM dynamic cost data. This process combines traditional cost management with dynamic data, so that the economic status of the project can be grasped in real time, and problems that lead to budget overruns can be discovered and corrected in time. The 5D cost dimension solution generated based on these dynamic cost data not only improves the transparency of project costs, but also provides a scientific basis for decision-making, helping to formulate more reasonable resource allocation and budget control strategies.

[0039] Preferably, step S33 includes the following steps:

[0040] Step S331: extracting time series data, cost data and component data from the prefabricated BIM model; generating a time series diagram based on the time series data to generate a prefabricated building time series diagram; performing autoregressive integral moving average prediction trend analysis on the prefabricated building time series diagram to generate time performance evaluation data;

[0041] Step S332: performing multidimensional distribution estimation on the cost data to generate cost distribution estimation data; performing variational Bayesian inference on the cost distribution estimation data to generate cost-benefit evaluation data;

[0042] Step S333: simulating the assembly process based on the component data to generate assembly process simulation data; using the finite element analysis method to analyze the component connection strength of the assembly process simulation data to generate component connection strength data; performing an accelerated life test on the component connection strength data to generate durability data; using laser scanning technology to perform high-precision measurement of the design drawing data and the assembled BIM model to generate assembly deviation data; performing fuzzy comprehensive evaluation based on the component connection strength data, durability data and assembly deviation data to generate assembly characteristic evaluation data;

[0043] Step S334: embed the time performance evaluation data, the cost-effectiveness evaluation data and the assembly characteristic evaluation data into a graph for multi-dimensional comprehensive evaluation to generate a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score.

[0044] The present invention lays a foundation for subsequent analysis by extracting time series data, cost data and component data from the assembled BIM model. The assembled building time series diagram generated based on the extracted time series data can effectively evaluate the time performance of the project through autoregressive integral sliding average prediction trend analysis. This time performance evaluation data provides a dynamic monitoring mechanism, which enables real-time tracking of construction progress, identification of potential delay risks, and appropriate corrective measures to optimize time management and improve the overall efficiency of the project. Multidimensional distribution estimation is performed on cost data, and cost-effectiveness evaluation data is generated through variational Bayesian inference. This process not only improves the accuracy of cost data analysis, but also can handle the impact of uncertainty factors on cost. Through in-depth cost-effectiveness analysis, the economic feasibility of different schemes can be better evaluated, so as to make more scientific decisions in resource allocation and budget control. This process ensures transparent management of costs and maximizes the return on investment. Assembly process simulation is performed based on component data to generate assembly process simulation data, and component connection strength analysis is performed using finite element analysis method to further generate component connection strength data. The durability of components is evaluated by accelerated life tests, and laser scanning technology is used to measure the design drawings and assembled BIM models with high precision to generate assembly deviation data. The generation of these data provides a strong basis for evaluating assembly quality and ensures the safety and reliability of building components. By performing fuzzy comprehensive evaluation on component connection strength data, durability data and assembly deviation data, the generated assembly characteristic evaluation data provides a scientific basis for project quality management, enabling timely discovery and resolution of problems during the design and construction process to ensure the long-term performance of the building. Finally, the time performance evaluation data, cost-effectiveness evaluation data and assembly characteristic evaluation data are graph-embedded, and the generation of multi-dimensional comprehensive results not only enables intuitive identification of high-scoring and low-scoring schemes for the project, but also provides clear data support for decision-making. Through this comprehensive evaluation mechanism, multiple factors such as time, cost and quality can be comprehensively considered to formulate a more reasonable management strategy, ultimately achieving efficient, economical and safe implementation of the project.

[0045] Preferably, step S4 comprises the following steps:

[0046] Step S41: construct a management strategy using a multi-dimensional comprehensive evaluation and a BIM dimension simulation solution to generate a preliminary BIM assembly management strategy;

[0047] Step S42: performing multi-agent dynamic simulation optimization on the BIM assembly management strategy to generate assembly dynamic simulation data;

[0048] Step S43: Utilize the prefabricated dynamic simulation data to optimize the BIM prefabricated management preliminary strategy through two-dimensional graph fitting analysis strategy, and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

[0049] The present invention constructs a management strategy by utilizing a multi-dimensional comprehensive evaluation and a BIM dimension simulation scheme to generate a preliminary BIM assembly management strategy. In this process, the introduction of a multi-dimensional comprehensive evaluation makes the formulation of management strategies not only rely on a single indicator, but also comprehensively considers data in multiple dimensions of time, cost and quality, providing a comprehensive perspective. Through the application of the BIM dimension simulation scheme, potential problems can be foreseen in the strategy formulation stage, and visual analysis can be performed to ensure that the preliminary strategy formulated is a reasonable choice based on real data. This data-driven decision-making process significantly improves the scientific nature of the management strategy and reduces the uncertainty of decision-making. By performing multi-agent dynamic simulation optimization on the BIM assembly management strategy, assembly dynamic simulation data is generated. This dynamic simulation can not only reflect the performance of the management strategy in practical applications, but also identify bottlenecks and problems that occur during the implementation of the strategy. The application of the multi-agent system enables the interaction between different management factors to be simulated and analyzed, thereby providing more decision-making basis. This data generated based on dynamic simulation not only improves the flexibility and adaptability of strategy optimization, but also provides real-time feedback to ensure that the management strategy can be adjusted in time to cope with the changing project environment. Finally, the prefabricated dynamic simulation data was used to perform a two-dimensional graph fitting analysis on the preliminary BIM prefabricated management strategy to generate the final BIM prefabricated management strategy. This process enables the management strategy to achieve higher accuracy and effectiveness in actual operation through data fitting analysis. Through two-dimensional graph fitting, the relationship between different variables can be intuitively observed, and the management strategy can be further optimized to adapt to specific construction environments and technical requirements. This data-based strategy optimization method not only improves the overall efficiency of prefabricated building management, but also ensures the executability and effectiveness of the management strategy.

[0050] Preferably, step S41 includes the following steps:

[0051] Step S411: constructing smart contract management based on the multi-dimensional comprehensive results and the BIM dimension simulation solution, and generating a preliminary BIM assembly management strategy, wherein the preliminary BIM assembly management strategy includes a high-scoring management strategy and a low-scoring management strategy;

[0052] Step S412: When the multi-dimensional comprehensive result is a multi-dimensional high score, the manpower, material and equipment demand forecast is performed on the prefabricated BIM model to generate resource demand data; the resource hierarchy analysis is performed on the design drawings to generate resource availability data; the cost data is optimized to minimize the construction period through a genetic algorithm to generate cost optimization allocation data; the critical path optimization is performed on the time series data to generate optimized progress data;

[0053] Step S413: Optimizing resource allocation for the BIM dimension simulation solution based on resource demand data, resource availability data, cost optimization allocation data, and optimization progress data, thereby generating a high-scoring management strategy;

[0054] Step S414: When the multi-dimensional comprehensive result is a multi-dimensional low score, the 4D time dimension plan and the 5D cost dimension plan are subjected to risk control monitoring and processing, the prefabricated building monitoring result data is generated and an early warning threshold is set; when the prefabricated building monitoring result data exceeds the early warning threshold, a low score management strategy is generated and an early warning message is sent.

[0055] The present invention constructs intelligent contract management through multi-dimensional comprehensive results and BIM dimension simulation scheme, and generates preliminary strategies for BIM assembly management, including high-scoring management strategies and low-scoring management strategies. The key to this process lies in the application of multi-dimensional comprehensive evaluation, which provides a quantitative basis for the formulation of management strategies, so that the optimal strategy can be selected based on a data-driven approach. Distinguishing high-scoring management strategies from low-scoring management strategies can help adopt different management measures in different situations to ensure the effective allocation and utilization of resources, thereby reducing project risks. When the multi-dimensional comprehensive result is a multi-dimensional high score, resource demand data is generated by predicting the demand for manpower, materials and equipment for the assembled BIM model. The accuracy of this data directly affects the efficiency and cost control of project implementation. By performing resource hierarchy analysis on the design drawings and generating resource availability data, the supply of resources can be grasped in real time, thereby performing reasonable resource allocation. In addition, the cost data is optimized to minimize the construction period using a genetic algorithm, and cost optimization allocation data is generated, which can effectively shorten the construction period and reduce construction costs while ensuring project quality. The implementation of critical path optimization further ensures the rationality and controllability of the construction progress, providing a guarantee for the smooth progress of the project. Based on resource demand data, resource availability data, cost optimization allocation data and optimization progress data, the BIM dimension simulation plan is optimized for resource allocation, thereby generating a high-scoring management strategy. The data integration and analysis at this stage ensures the feasibility and effectiveness of the plan, enabling the optimal allocation of resources under a high-scoring scenario, thereby improving the economic benefits and construction efficiency of the project. When the multi-dimensional comprehensive result is a multi-dimensional low score, the system will conduct risk control monitoring and processing on the 4D time dimension plan and the 5D cost dimension plan, generate prefabricated building monitoring result data, and set an early warning threshold. When the monitoring result data exceeds the early warning threshold, the system will automatically generate a low-assessment management strategy and send an early warning message. The introduction of this risk control mechanism ensures that the project can identify potential risks in a timely manner and take corresponding measures by monitoring data changes in real time, effectively preventing major mistakes or losses in the project.

[0056] The present invention obtains building site data, design drawing data and construction drawing data, and this process ensures the comprehensiveness and diversity of data sources, and provides rich original data for subsequent data processing and analysis. These data cover the actual situation on site, design intent and construction plan, and lay the foundation for building an accurate and comprehensive BIM model. Data cleaning is performed on the building site data to generate building site cleaning data. This process improves the quality and consistency of data by removing redundant data, correcting erroneous data and completing missing data. Data cleaning can not only eliminate unnecessary information and reduce the impact of noise on the model, but also ensure the accuracy and reliability of data, thereby improving the accuracy of subsequent data processing and analysis. Data standardization is performed on the building site cleaning data to generate standardized model data. Data standardization eliminates the heterogeneity between different data sources by converting data into a unified format and dimension, and enhances the comparability and integrability of data. Standardized data not only helps to improve the utilization efficiency of data, but also facilitates the integration and collaboration between different data sets. This process can ensure the consistency and operability of data in different data analysis and model building processes, and reduce errors and deviations caused by inconsistent data formats. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the steps of a BIM-based prefabricated building construction method;

[0058] Figure 2 is a schematic diagram of a detailed implementation process of step S33;

[0059] Figure 3 It is a flowchart diagram of the detailed implementation steps of step S41.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 , a method for constructing an assembled building based on BIM, the method comprising the following steps:

[0065] Step S1: Acquire building site data, design drawing data and construction drawing data; perform data preprocessing on the building site data to generate standardized model data;

[0066] Step S2: Acquire geographic information system data; construct a three-dimensional laser BIM model based on the standardized model data and the geographic information system data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an assembled BIM model;

[0067] Step S3: Perform dimensional simulation on the prefabricated BIM model to generate a BIM dimensional simulation plan; perform a comprehensive evaluation of the BIM dimensional simulation plan in terms of time, cost, and prefabricated assembly simulation to generate a multi-dimensional comprehensive result;

[0068] Step S4: Use the multi-dimensional comprehensive results and BIM dimension simulation solution to build a management strategy and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

[0069] The beneficial effect of the present invention is that by acquiring building site data, design drawing data and construction drawing data, and pre-processing the building site data, standardized model data is generated, which lays a solid foundation for the subsequent BIM model construction. Standardized data not only improves the consistency and accuracy of data, but also provides convenience for further processing of data. Obtain geographic information system data and construct a three-dimensional laser BIM model based on standardized model data to generate an initial BIM model. On this basis, the initial BIM model is geometrically calibrated and material properties are assigned through design drawings and construction drawings to generate an assembled BIM model. This step not only enhances the authenticity and accuracy of the model, but also ensures the operability and reliability of the model in practical applications. The assembled BIM model is dimensionally simulated to generate a BIM dimensional simulation scheme. By comprehensively evaluating the BIM dimensional simulation scheme in terms of time, cost and assembled assembly simulation, a multi-dimensional comprehensive result is generated. This process takes into account a variety of factors, not only provides a comprehensive evaluation standard, but also provides a scientific basis for decision-making. The generation of multi-dimensional comprehensive results enables a more accurate evaluation of the feasibility and economy of the project, thereby optimizing resource allocation and reducing cost waste. Finally, the management strategy is constructed using the multi-dimensional comprehensive results and BIM dimension simulation scheme to generate a BIM prefabricated management strategy to complete the prefabricated building construction operation. This process improves the scientificity and effectiveness of management by converting complex data and models into specific management strategies. This method not only improves the efficiency of prefabricated building management, but also enhances the overall controllability and predictability of the project. Overall, this method provides a systematic, scientific and efficient solution for prefabricated building management through data standardization, precise model construction and multi-dimensional comprehensive evaluation, which helps to improve the management level and construction efficiency of the entire construction industry.

[0070] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for constructing a prefabricated building based on BIM of the present invention. In this example, the method for constructing a prefabricated building based on BIM includes the following steps:

[0071] Step S1: Acquire building site data, design drawing data and construction drawing data; perform data preprocessing on the building site data to generate standardized model data;

[0072] In the embodiment of the present invention, the building site data is obtained through various data acquisition technologies, including laser scanning, drone aerial photography, geographic information system (GIS) and sensor network. Laser scanning technology provides high-precision three-dimensional point cloud data, which can accurately capture the geometric features and spatial relationships of the building site, while drone aerial photography can quickly obtain a wide range of on-site image data, providing a rich source of information for subsequent analysis. In addition, GIS technology can combine on-site data with geographic spatial information to generate building data with geographic context, thereby improving the visualization effect and spatial analysis ability of the data. The obtained design drawing data and construction drawing data are stored and managed in CAD (computer-aided design) format. These data include floor plans, elevations, sections and detailed structures of the building, which can provide rich architectural design information. When processing these drawing data, technical means include image processing and computer vision technology, which can automatically identify and analyze the design drawings, thereby extracting useful building information. Raster image and vector data conversion technology can be used to convert raster image information in the drawings into editable vector data. Data cleaning aims to identify and correct errors in the data, such as missing values ​​and outliers, to improve the quality and reliability of the data. Data integration is the merging of data from different sources (such as field data and drawing data) to ensure data consistency and integrity. In this process, data fusion technologies, such as multi-source data fusion and feature matching algorithms, can effectively integrate different types of data, eliminate redundant information and optimize data structure. Finally, data standardization is to use unified data formats and standards so that data from different sources and types can be effectively applied in the same model or analysis framework.

[0073] Step S2: Acquire geographic information system data; construct a three-dimensional laser BIM model based on the standardized model data and the geographic information system data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an assembled BIM model;

[0074] In the embodiment of the present invention, obtaining GIS data is an important prerequisite for building a BIM model. GIS data includes terrain, landform, land use, environmental impact, climate conditions and infrastructure spatial information, which are collected by satellite remote sensing, aerial photography and ground survey. Remote sensing technology can provide large-scale, high-resolution surface data, while aerial photography obtains ground images through high-precision cameras. After processing, these images can generate detailed digital elevation models (DEM) and orthophotos, thereby providing the necessary geographical background for subsequent BIM model construction. The construction of a three-dimensional laser BIM model based on standardized model data and GIS data involves geometric modeling and three-dimensional visualization technology. Three-dimensional laser scanning technology obtains the precise three-dimensional coordinates of a building and its surrounding environment through a laser beam to generate point cloud data. After these point cloud data are processed and filtered, they are converted into a BIM model with a geometric shape using special modeling software (such as Revit). In this process, point cloud processing technology and geometric reconstruction algorithm (RANSAC algorithm) are widely used to ensure that the geometric features of the model can accurately reflect the actual situation. Once the initial BIM model is built, the next task is to perform geometric calibration and material property assignment to generate an assembled BIM model. The process of geometric calibration mainly involves fine-tuning the model to ensure that each part of the model is consistent with the actual building design. This requires the use of design drawings and construction drawings for comparison, and the use of computer vision technology and image processing technology to identify and correct geometric deviations in the model. In addition, material property assignment is to assign corresponding material information to the components in the BIM model, including physical properties (such as strength, thermal conductivity, density) and performance parameters (such as durability, environmental protection characteristics). In the process of material assignment, it is also necessary to use material performance simulation software (such as ANSYS) for further analysis to ensure the adaptability and safety of the selected materials in actual construction and use.

[0075] Step S3: Perform dimensional simulation on the prefabricated BIM model to generate a BIM dimensional simulation plan; perform a comprehensive evaluation of the BIM dimensional simulation plan in terms of time, cost, and prefabricated assembly simulation to generate a multi-dimensional comprehensive result;

[0076] In the embodiment of the present invention, the key technology for BIM dimension simulation is that the building information modeling software and simulation tools (such as Revit) can integrate various types of data (such as geometric information, material properties, and time series data) in the prefabricated BIM model, and perform multi-dimensional analysis on the model through the built-in simulation module. The time dimension simulation adopts 4D modeling technology, that is, time information is added to the BIM model to realize dynamic visualization of the construction progress. This process generates a construction timeline by associating construction tasks with corresponding time nodes, and can intuitively observe the progress of each construction stage and its relationship with each other, and identify potential delay risks. When performing cost dimension simulation, the cost data in the BIM model is analyzed by the cost management module. This process involves combining the various components of the project with their cost attributes, and using historical data and market prices to generate detailed cost estimates. By applying the life cycle cost analysis (LCCA) method, the economic benefits of the project throughout its life cycle can be evaluated, thereby optimizing resource allocation. Prefabricated assembly simulation involves modeling the assembly process of components at the construction site, using discrete event simulation (DES) technology or system dynamics (SD) model. These simulation technologies can simulate various variables in the assembly process (such as the flow of manpower, equipment, and materials) and analyze the assembly efficiency and effectiveness under different strategies. The data collection and analysis at this stage make it possible to evaluate the feasibility of different assembly schemes, thereby optimizing the construction strategy. After completing the dimensional simulation, the generated BIM dimensional simulation scheme will serve as the basis for comprehensive evaluation. The generation of comprehensive evaluation relies on the multi-criteria decision analysis (MCDA) method, which quantitatively evaluates multiple factors such as time, cost, and assembly efficiency by establishing an evaluation matrix.

[0077] Step S4: Use the multi-dimensional comprehensive results and BIM dimension simulation solutions to build a management strategy and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

[0078] In the embodiment of the present invention, the basis for constructing a management strategy is a multi-dimensional comprehensive result, and the generation of the evaluation result depends on the multi-criteria decision analysis (MCDA) technology. MCDA technology can comprehensively evaluate data of different dimensions (such as time, cost and quality), and the methods used include hierarchical analysis method (AHP), weighted scoring method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). These methods quantify each evaluation index by establishing an evaluation model, and weight it according to its relative importance, thereby generating a comprehensive comprehensive evaluation. This evaluation not only reflects the advantages and disadvantages of different schemes, but also provides a clear basis for the selection of management strategies. As an important input for the construction of management strategies, the BIM dimension simulation scheme contains the dynamic information of the project and the visual expression of various construction scenes. With the help of BIM software (such as Revit), the feasibility and efficiency of different construction schemes can be analyzed in real time. This process uses simulation technology (4D and 5D simulation) to help identify potential risks and bottlenecks through dynamic management of time, cost and resources, so as to formulate a more scientific management strategy. In the process of generating BIM prefabricated management strategies, optimization algorithms also need to be applied to ensure that the strategies developed can achieve the best resource allocation and time utilization. Optimization algorithms include genetic algorithms, which can search for optimal solutions in complex decision spaces to ensure that the benefits of the project are maximized while meeting constraints. Using data visualization tools (such as Tableau and Power BI) to visualize comprehensive evaluation and management strategies can more intuitively understand the information and trends behind the data. By graphically displaying the advantages and disadvantages of each solution, decisions can be made faster and effective communication can be carried out within the team. The final generated BIM prefabricated management strategy will bring together all evaluation data and optimization results to provide comprehensive support for prefabricated building construction operations.

[0079] Preferably, step S2 comprises the following steps:

[0080] Step S21: Acquire geographic information system data, wherein the geographic information system data includes terrain, elevation and landform features;

[0081] Step S22: using 3D laser scanning technology to establish network topology nodes for geographic information system data, and generating a geographic information network topology map; using standardized model data to fill in network node data for the geographic information network topology map, and generating prefabricated building node filling data;

[0082] Step S23: construct a three-dimensional laser BIM model based on the prefabricated building node filling data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an prefabricated BIM model.

[0083] In the embodiment of the present invention, geographic information system data including terrain, elevation and geomorphic features are obtained, and these data are collected through various sensors and measuring equipment, such as GPS, remote sensing technology and LiDAR (light detection and ranging). These geographic information system data fully reflect the natural conditions of the building site and provide the necessary basic data for subsequent model construction. The network topology nodes of the geographic information system data are established using three-dimensional laser scanning technology to generate a geographic information network topology map. The three-dimensional laser scanning technology obtains high-precision point cloud data through laser ranging and reflection principles, and these data are processed to generate network topology nodes in three-dimensional space. The establishment of network topology nodes enables complex geographic information data to be organized and managed in a structured form, ensuring the systematicness and operability of the data. Subsequently, the network node data of the geographic information network topology map is filled with standardized model data to generate prefabricated building node filling data. This step combines the standardized data of the building model with the geographic information data, so that the model not only has the accuracy of geographic information, but also reflects the actual characteristics of the building, thereby improving the comprehensiveness and accuracy of the data. The three-dimensional laser BIM model is constructed based on the prefabricated building node filling data to generate an initial BIM model. By applying the node filling data of prefabricated buildings to the construction of BIM models, the high accuracy of the model in terms of geometry and spatial structure is ensured. The 3D laser BIM model construction technology converts point cloud data into a BIM model, so that the model can accurately reflect the actual building situation. Subsequently, the initial BIM model is geometrically calibrated and material properties are assigned based on the design drawings and construction drawings to generate an assembled BIM model. This process combines the data of the design drawings and construction drawings to perform fine geometric calibration on the initial BIM model to ensure that the geometry of the model is accurate; at the same time, the model is given real material properties, making the model closer to the actual building situation in terms of physical properties.

[0084] Preferably, step S22 includes the following steps:

[0085] Step S221: extracting geological segmentation data features by using point cloud segmentation on the terrain, elevation and geomorphic features to generate geological feature segmentation data;

[0086] Step S222: Establishing network topology nodes for the geological feature segmentation data to generate a geographic information network topology map;

[0087] Step S223: Filling the geographic information network topology map with standardized model data to generate Kalman filter filling data; filling the Kalman filter filling data with prefabricated building nodes to generate prefabricated building node filling data.

[0088] In the embodiment of the present invention, the point cloud segmentation technology is used to extract the features of geological segmentation data for terrain, elevation and geomorphic features, and generate geological feature segmentation data. Point cloud segmentation technology is based on high-precision three-dimensional point cloud data obtained by laser scanning or other measuring equipment. By performing algorithm processing on these data, complex terrain, elevation and geomorphic features can be finely segmented and classified to extract key geological feature data. Through the feature extraction process, these data can reflect the details and changes of actual terrain and landforms, and provide high-quality basic data for subsequent data processing and model construction. The network topology nodes are established for the geological feature segmentation data to generate a geographic information network topology map. This step converts the geological feature segmentation data into structured network topology nodes so that the data can be organized and managed in a network structure. The establishment of the network topology nodes uses an algorithm to connect and build relationships between the segmented geological feature data to generate a network topology map reflecting the actual geographical environment. This method not only improves the efficiency of data organization and management, but also enables geographic information data to be presented and analyzed in a more intuitive and systematic way. The geographic information network topology map is filled with standardized model data to generate Kalman filter filling data. Standardized model data filling unifies and standardizes data from different sources and formats by normalizing the geographic information network topology map, so that the data can be seamlessly connected and integrated. Kalman filtering technology removes noise and uncertainty by dynamically adjusting and optimizing the data, improving the smoothness and reliability of the data. The generation of Kalman filter filling data ensures the high quality and high accuracy of the data, providing a reliable data foundation for the subsequent filling of prefabricated building nodes. Finally, the prefabricated building node filling data is generated by filling the prefabricated building nodes with the Kalman filter filling data.

[0089] Preferably, step S23 includes the following steps:

[0090] Step S231: acquiring point cloud data from the prefabricated BIM model through three-dimensional laser scanning; performing high-dimensional data analysis on the prefabricated building node filling data and the point cloud data to generate prefabricated building principal component analysis data and prefabricated building independent component analysis data;

[0091] Step S232: constructing a BIM three-dimensional model based on the prefabricated building principal component analysis data and the prefabricated building independent component analysis data to generate an initial BIM model;

[0092] Step S233: calibrate the geometric accuracy of the initial BIM model based on the design drawing data to generate a calibrated BIM model; assign physical and mechanical properties to the calibrated BIM model based on the construction drawing data to generate an assembled BIM model, wherein the assembled BIM model includes a timing module and a cost module.

[0093] In the embodiment of the present invention, obtaining point cloud data from the prefabricated BIM model through three-dimensional laser scanning is a key step. Three-dimensional laser scanning technology uses the principle of laser reflection to obtain high-precision three-dimensional point cloud data, which fully reflects the spatial geometry and details of the building. By performing high-dimensional data analysis on the prefabricated building node filling data and point cloud data, the prefabricated building principal component analysis data and prefabricated building independent component analysis data are generated. High-dimensional data analysis technology plays an important role in this process. Principal component analysis (PCA) extracts the most representative principal components in the data by reducing the dimensionality of the data, so that the data retains the main features while reducing redundancy and noise; independent component analysis (ICA) reveals the potential structure and relationship in the data by extracting independent components. These analysis techniques ensure the high dimensionality and high precision of the data and improve the interpretability and utilization value of the data. Based on the prefabricated building principal component analysis data and prefabricated building independent component analysis data, the BIM three-dimensional model is constructed to generate an initial BIM model. By integrating principal component analysis data and independent component analysis data, the initial BIM model not only has high-precision geometric form, but also can reflect the key features and independent factors of the building. The three-dimensional model construction technology converts these high-dimensional data into a three-dimensional model, making the model more accurate and comprehensive, and can truly reflect the actual situation of the building. The data fusion of principal component analysis and independent component analysis provides a scientific basis and high-quality data support for model construction, ensuring the authenticity and reliability of the model. The initial BIM model is geometrically calibrated based on the design drawing data to generate a calibrated BIM model. This process combines the design drawing data to perform fine geometric calibration on the initial BIM model to ensure that the geometric form of the model is consistent with the actual design drawing. Geometric accuracy calibration not only improves the accuracy of the model, but also provides an accurate geometric basis for the subsequent physical and mechanical property assignment. Subsequently, the physical and mechanical properties of the calibrated BIM model are assigned based on the construction drawing data to generate an assembled BIM model, which includes a timing module and a cost module. The physical and mechanical property assignment combines the construction drawing data to give the model real physical properties and mechanical characteristics, so that the model not only has geometric accuracy, but also can reflect the actual physical behavior of the building.

[0094] Preferably, step S3 comprises the following steps:

[0095] Step S31: Perform multi-dimensional simulation on the prefabricated BIM model to generate a 4D time dimension solution and a 5D cost dimension solution;

[0096] Step S32: integrating the 4D time dimension solution and the 5D cost dimension solution into a multi-dimensional scale simulation solution to generate a BIM dimension simulation solution;

[0097] Step S33: Establish an evaluation system through multi-criteria decision making to conduct a comprehensive evaluation of the BIM dimensional simulation solution in terms of time, cost and prefabricated assembly simulation, and generate a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score.

[0098] In an embodiment of the present invention, a multi-dimensional simulation is performed on the assembled BIM model to generate a 4D time dimension solution and a 5D cost dimension solution. The 4D time dimension solution integrates the time factor into the BIM model, so that the time progress of the construction project can be intuitively visualized and analyzed. The specific technical means include using time series analysis and dynamic simulation technology, combining the construction plan and progress data in the BIM model, to generate a 4D time dimension solution reflecting the actual construction progress. This process can help monitor and adjust the construction plan in real time by dynamically displaying the construction progress, and improve the accuracy and efficiency of time management. The 5D cost dimension solution reflects the budget and cost structure of the project by integrating cost data with the BIM model. Using cost analysis and budget control technology, combined with the cost information in the BIM model, a 5D cost dimension solution is generated, so that the economic factors of the project can be comprehensively analyzed and managed. The 4D time dimension solution and the 5D cost dimension solution are integrated with multi-dimensional scale simulation solutions to generate a BIM dimension simulation solution. This step integrates simulation solutions of different dimensions so that data of multiple dimensions of time and cost can be uniformly managed and analyzed within a comprehensive framework. Specific technical means include multi-dimensional data fusion and integration technology, which realizes seamless connection and comprehensive analysis of data through standardized processing and unified format conversion of data of different dimensions. The integration of multi-dimensional scale simulation scheme enables the model to fully reflect the multi-dimensional characteristics of the project, provide a comprehensive perspective, and provide a scientific basis for the overall management and optimization of the project. Through multi-criteria decision-making, an evaluation system is established to conduct a comprehensive evaluation of the BIM dimension simulation scheme in terms of time, cost and prefabricated assembly simulation, and generate multi-dimensional comprehensive results. The multi-criteria decision-making method comprehensively evaluates the BIM dimension simulation scheme by considering multiple evaluation indicators. Specific technical means include constructing a multi-criteria decision-making model and evaluation system, and using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) decision analysis technology to evaluate and weight the key factors of time, cost and prefabricated assembly. The evaluation system generates multi-dimensional comprehensive results by combining the weights and evaluation results of each evaluation indicator, including multi-dimensional high scores and multi-dimensional low scores. Multi-dimensional high scores and low scores provide clear optimization directions and decision-making basis by comparing and analyzing the advantages and disadvantages of different schemes.

[0099] Preferably, step S31 includes the following steps:

[0100] Step S311: perform critical path timing analysis on the timing module to generate time dimension 4D data; perform 4D dynamic simulation on the time dimension 4D data to generate BIM time dimension dynamic data; generate a time dimension solution based on the BIM time dimension dynamic data to generate a 4D time dimension solution;

[0101] Step S312: Obtain a cost database; extract cost data from the cost database, and perform Monte Carlo simulation on the cost module to generate cost traceability data; dynamically combine the cost traceability data and the cost data for earned value management analysis to generate BIM dynamic cost data; generate a cost dimension plan based on the BIM dynamic cost data to generate a 5D cost dimension plan.

[0102] In an embodiment of the present invention, a critical path timing analysis is performed on the timing module to generate time dimension 4D data. The critical path timing analysis technology helps identify and optimize critical tasks by identifying the most critical task path in the construction project, that is, the longest path that determines the project duration. Specific technical means include using project management software and algorithms to perform timing analysis on each task of the project, determine the critical path and generate corresponding 4D time dimension data. These 4D data not only contain the time progress information of the project, but also can intuitively display the relationship and dependency of each task on the time axis. Subsequently, the time dimension 4D data is subjected to 4D dynamic simulation to generate BIM time dimension dynamic data. The 4D dynamic simulation technology enables the time progress of the project to be dynamically visualized by combining the BIM model and the time dimension data, thereby more intuitively displaying the construction progress. This dynamic simulation not only improves the accuracy of project progress management, but also enables all relevant parties to monitor the construction progress in real time and make necessary adjustments. Based on the BIM time dimension dynamic data, the time dimension scheme is generated, and the 4D time dimension scheme is finally generated. This step analyzes the dynamic simulation data to formulate a scientific time management scheme for the project to ensure the rationality and controllability of the construction progress. Obtaining the cost database and extracting cost data from it is a key step. The cost database contains the historical cost data, budget data and real-time cost information of the project. Relevant data is obtained from it through data extraction technology to provide a basis for subsequent cost analysis. Specific technical means include using database management systems and data mining technologies to query and extract the cost database to obtain the required cost data. Next, Monte Carlo simulation is performed on the cost module to generate cost traceability data. Monte Carlo simulation is a statistical analysis method based on random sampling. It predicts the distribution and risk of project costs by performing multiple random simulations on cost data. This technical means can reveal the uncertainty and risk factors in cost data and generate cost traceability data that reflects the dynamic changes in project costs. Cost traceability data and cost data are dynamically combined and analyzed by earned value management to generate BIM dynamic cost data. Earned value management (EVM) is a project management technology that evaluates the performance and health of a project by dynamically monitoring and analyzing the actual progress and cost of the project. Specific technical means include applying the earned value management model to cost traceability data and cost data, and generating dynamic BIM cost data by calculating the earned value, planned value and actual cost of the project. These dynamic cost data not only reflect the real-time cost situation of the project, but also predict future cost trends, providing a scientific basis for cost control and management. Finally, based on the BIM dynamic cost data, a cost dimension solution is generated to generate a 5D cost dimension solution.

[0103] As an example of the present invention, refer to Figure 2 As shown, in this example, step S33 includes:

[0104] Step S331: extracting time series data, cost data and component data from the prefabricated BIM model; generating a time series diagram based on the time series data to generate a prefabricated building time series diagram; performing autoregressive integral moving average prediction trend analysis on the prefabricated building time series diagram to generate time performance evaluation data;

[0105] Step S332: performing multidimensional distribution estimation on the cost data to generate cost distribution estimation data; performing variational Bayesian inference on the cost distribution estimation data to generate cost-benefit evaluation data;

[0106] Step S333: simulating the assembly process based on the component data to generate assembly process simulation data; using the finite element analysis method to analyze the component connection strength of the assembly process simulation data to generate component connection strength data; performing an accelerated life test on the component connection strength data to generate durability data; using laser scanning technology to perform high-precision measurement of the design drawing data and the assembled BIM model to generate assembly deviation data; performing fuzzy comprehensive evaluation based on the component connection strength data, durability data and assembly deviation data to generate assembly characteristic evaluation data;

[0107] Step S334: embed the time performance evaluation data, the cost-effectiveness evaluation data and the assembly characteristic evaluation data into a graph for multi-dimensional comprehensive evaluation to generate a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score.

[0108] In the embodiment of the present invention, extracting time series data, cost data and component data from the prefabricated BIM model is the basis for subsequent analysis. The time series data generates a prefabricated building time series diagram by recording the time information of the project progress and key tasks. The specific technical means include using project management software and data visualization tools to convert the time series data into an intuitive time series chart. Next, the prefabricated building time series chart is subjected to autoregressive integrated moving average forecast trend analysis (ARIMA) to generate time performance evaluation data. The ARIMA model is a statistical method for time series prediction. By modeling and predicting time series data, the time performance of the project is evaluated and potential time risks and trends are revealed. Multidimensional distribution estimation is performed on the cost data to generate cost distribution estimation data. The multidimensional distribution estimation technology identifies the distribution characteristics and potential cost risks of the data by performing statistical analysis on the cost data. The specific technical means include using statistical software and algorithms to perform multidimensional distribution fitting and estimation on the cost data to generate estimated data reflecting the cost structure. Variational Bayesian Inference is performed on the cost distribution estimation data to generate cost-effectiveness evaluation data. Variational Bayesian inference is a technique for inferring complex probabilistic models. By performing Bayesian inference on cost data, the cost-effectiveness of a project is evaluated, and potential problems and optimization opportunities in cost management are identified. Assembly process simulation is performed based on component data to generate assembly process simulation data. Assembly process simulation technology analyzes the assembly process and flow of components by simulating the assembly process of prefabricated buildings. Specific technical means include using computer simulation software to model and simulate component data to generate detailed assembly process simulation data. Subsequently, the finite element analysis method is used to analyze the component connection strength of the assembly process simulation data to generate component connection strength data. Finite element analysis (FEA) is a numerical method for structural mechanics analysis. By simulating and analyzing the connection strength of components, its structural performance and stability are evaluated. Accelerated life tests are performed on component connection strength data to generate durability data. Accelerated life tests predict the durability and life of components by simulating their use under extreme conditions. Finally, laser scanning technology is used to perform high-precision measurements on the design drawing data and prefabricated BIM models to generate assembly deviation data. Laser scanning technology obtains geometric deviation data generated during the assembly process through high-precision three-dimensional scanning. These data integrate component connection strength data, durability data, and assembly deviation data, and use fuzzy comprehensive evaluation methods to generate assembly characteristic evaluation data. Fuzzy comprehensive evaluation is a multi-criteria decision-making method based on fuzzy logic. It generates evaluation results that reflect assembly quality and characteristics by comprehensively analyzing and evaluating multidimensional data. The time performance evaluation data, cost-effectiveness evaluation data, and assembly characteristic evaluation data are graph-embedded into a multi-dimensional comprehensive evaluation to generate a multi-dimensional comprehensive result.Graph embedding technology embeds multi-dimensional data into low-dimensional space for comprehensive analysis and evaluation. Specific technical means include using data mining and machine learning algorithms to fuse and embed data of different dimensions to generate comprehensive evaluation results. The multi-dimensional comprehensive results not only include high and low scores in multiple dimensions, but also provide comprehensive evaluation and optimization suggestions for the project.

[0109] Preferably, step S4 comprises the following steps:

[0110] Step S41: construct a management strategy using the multi-dimensional comprehensive results and the BIM dimension simulation solution to generate a preliminary BIM assembly management strategy;

[0111] Step S42: performing multi-agent dynamic simulation optimization on the BIM assembly management strategy to generate assembly dynamic simulation data;

[0112] Step S43: Utilize the prefabricated dynamic simulation data to optimize the BIM prefabricated management preliminary strategy through two-dimensional graph fitting analysis strategy, and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

[0113] In the embodiment of the present invention, a management strategy is constructed using multi-dimensional comprehensive results and BIM dimension simulation schemes to generate a preliminary BIM assembly management strategy. Specific technical means include combining the multi-dimensional comprehensive evaluation results with the BIM dimension simulation scheme data, applying the decision support system (DSS) and multi-criteria decision analysis (MCDA) method, and constructing a preliminary management strategy framework. The multi-dimensional comprehensive results cover the time, cost and assembly characteristics of the project. Through data fusion and analysis, a comprehensive management strategy is generated to provide a basis for subsequent optimization. The BIM assembly management strategy is optimized by multi-agent dynamic simulation to generate assembly dynamic simulation data. Multi-agent system (MAS) is a method for studying complex systems by simulating the behavior and interaction of multiple intelligent agents. Specific technical means include constructing a multi-agent simulation model, in which each agent represents a key role or component in a project, and evaluating and optimizing the preliminary management strategy by simulating the dynamic behavior and interaction of these agents. Dynamic simulation captures the response of the system in different situations through iteration and feedback mechanisms, and generates assembly dynamic simulation data. These data can reflect the dynamic effects and potential problems of management strategies in practical applications, and provide data support for further optimization. The two-dimensional graph fitting analysis strategy is optimized for the preliminary BIM assembly management strategy using the assembly dynamic simulation data to generate the BIM assembly management strategy. The specific technical means include using data fitting and optimization algorithms to analyze the assembly dynamic simulation data. The two-dimensional graph fitting analysis projects the multidimensional data onto a two-dimensional plane and uses statistical analysis and graphic visualization technology to reveal the relationship and pattern between the data. The dynamic simulation data is fitted and optimized using regression analysis, principal component analysis (PCA) and multidimensional scaling analysis (MDS) methods to identify key variables and optimization directions. Through these technical means, the preliminary management strategy can be effectively optimized to make it more scientific and efficient. The BIM assembly management strategy is finally generated.

[0114] As an example of the present invention, refer to Figure 3 As shown, in this example, step S41 includes:

[0115] Step S411: constructing smart contract management based on the multi-dimensional comprehensive results and the BIM dimension simulation solution, and generating a preliminary BIM assembly management strategy, wherein the preliminary BIM assembly management strategy includes a high-scoring management strategy and a low-scoring management strategy;

[0116] Step S412: When the multi-dimensional comprehensive result is a multi-dimensional high score, the manpower, material and equipment demand forecast is performed on the prefabricated BIM model to generate resource demand data; the resource hierarchy analysis is performed on the design drawings to generate resource availability data; the cost data is optimized to minimize the construction period through a genetic algorithm to generate cost optimization allocation data; the critical path optimization is performed on the time series data to generate optimized progress data;

[0117] Step S413: Optimizing resource allocation for the BIM dimension simulation solution based on resource demand data, resource availability data, cost optimization allocation data, and optimization progress data, thereby generating a high-scoring management strategy;

[0118] Step S414: When the multi-dimensional comprehensive result is a multi-dimensional low score, the 4D time dimension plan and the 5D cost dimension plan are subjected to risk control monitoring and processing, the prefabricated building monitoring result data is generated and an early warning threshold is set; when the prefabricated building monitoring result data exceeds the early warning threshold, a low score management strategy is generated and an early warning message is sent.

[0119] In an embodiment of the present invention, smart contract management is constructed based on multi-dimensional comprehensive results and BIM dimension simulation schemes to generate a preliminary BIM assembly management strategy. The specific technical means of this step include the construction and implementation of smart contracts, and the use of blockchain technology to ensure the transparency and immutability of contracts. The multi-dimensional comprehensive results cover the evaluation of the time, cost and assembly characteristics of the project. By combining these evaluation data with the BIM dimension simulation scheme, a preliminary management strategy framework is constructed, which includes high-scoring management strategies and low-scoring management strategies. This smart contract management can improve the automation of project management and ensure transparency and fairness of all parties in contract execution. When the multi-dimensional comprehensive results are multi-dimensional high scores, a series of resource demand forecasting and optimization measures are implemented. The manpower, material and equipment demand forecasts for the assembled BIM model are generated to generate resource demand data. The specific technical means include using historical data and prediction models to predict the resource demand of the project through time series analysis. These can accurately predict future resource demand based on past data trends and current project progress. Resource hierarchy analysis is performed on the design drawings to generate resource availability data. Resource hierarchy analysis technology evaluates the availability and reasonable allocation of resources by analyzing the resource distribution and demand in the design drawings. The cost data is optimized to minimize the construction period through genetic algorithm, and the cost optimization allocation data is generated. Genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism. It finds the optimal allocation plan of cost and construction period by simulating the biological evolution process. The critical path optimization of time series data is performed to generate optimized progress data. The critical path optimization technology analyzes the critical path of the project, identifies the key tasks that affect the project progress, and optimizes them to ensure that the project is completed on time. Based on resource demand data, resource availability data, cost optimization allocation data and optimized progress data, the BIM dimension simulation scheme is optimized for resource allocation to generate a high-scoring management strategy. Specific technical means include multi-criteria decision analysis (MCDA) and resource optimization algorithm, which ensure the efficient allocation and management of resources by comprehensively analyzing and optimizing data of different dimensions. Resource allocation optimization can maximize the utilization efficiency of resources, reduce project costs and improve the overall performance of the project. When the multi-dimensional comprehensive result is a multi-dimensional low score, risk control and monitoring processing are implemented. First, the 4D time dimension scheme and the 5D cost dimension scheme are monitored for risk control, and the prefabricated building monitoring result data is generated and the warning threshold is set. Specific technical means include risk assessment models and monitoring systems, which use sensors and data analysis technology to monitor the progress and risk status of projects in real time. The setting of early warning thresholds is based on statistical analysis and empirical data. When the monitoring result data exceeds the early warning threshold, a low-score management strategy is generated and an early warning message is sent. The low-score management strategy includes a series of risk mitigation and emergency measures to ensure that the project can respond and adjust quickly when problems arise, reducing potential losses and risks.

[0120] The beneficial effect of the present invention is that by acquiring building site data, design drawing data and construction drawing data, and pre-processing the building site data, standardized model data is generated, which lays a solid foundation for the subsequent BIM model construction. Standardized data not only improves the consistency and accuracy of data, but also provides convenience for further processing of data. Obtain geographic information system data and construct a three-dimensional laser BIM model based on standardized model data to generate an initial BIM model. On this basis, the initial BIM model is geometrically calibrated and material properties are assigned through design drawings and construction drawings to generate an assembled BIM model. This step not only enhances the authenticity and accuracy of the model, but also ensures the operability and reliability of the model in practical applications. The assembled BIM model is dimensionally simulated to generate a BIM dimensional simulation scheme. By comprehensively evaluating the BIM dimensional simulation scheme in terms of time, cost and assembled assembly simulation, a multi-dimensional comprehensive result is generated. This process takes into account a variety of factors, not only provides a comprehensive evaluation standard, but also provides a scientific basis for decision-making. The generation of multi-dimensional comprehensive results can more accurately evaluate the feasibility and economy of the project, thereby optimizing resource allocation and reducing cost waste. Finally, the management strategy is constructed using the multi-dimensional comprehensive results and BIM dimension simulation scheme to generate a BIM prefabricated management strategy to complete the prefabricated building construction operation. This process improves the scientificity and effectiveness of management by converting complex data and models into specific management strategies. This method not only improves the efficiency of prefabricated building management, but also enhances the overall controllability and predictability of the project. Overall, this method provides a systematic, scientific and efficient solution for prefabricated building management through data standardization, precise model construction and multi-dimensional comprehensive evaluation, which helps to improve the management level and construction efficiency of the entire construction industry.

[0121] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0122] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing an assembled building based on BIM, characterized in that: The following steps are involved: Step S1: Acquire building site data, design drawing data and construction drawing data; Preprocess the construction site data to generate standardized model data; Wherein step S1 comprises the following steps: Step S11: Acquire building site data, design drawing data and construction drawing data; Step S12: Cleaning the construction site data to generate construction site cleaned data; Step S13: performing data standardization processing on the building site cleaning data to generate standardized model data; Step S2: Acquire geographic information system data; construct a three-dimensional laser BIM model based on the standardized model data and the geographic information system data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an assembled BIM model; wherein step S2 includes the following steps: Step S21: Acquire geographic information system data, wherein the geographic information system data includes terrain, elevation and landform features; Step S22: using 3D laser scanning technology to establish network topology nodes for geographic information system data, and generating a geographic information network topology map; using standardized model data to fill in network node data for the geographic information network topology map, and generating prefabricated building node filling data; Step S23: construct a three-dimensional laser BIM model based on the prefabricated building node filling data to generate an initial BIM model; perform geometric calibration and material property assignment on the initial BIM model based on the design drawings and construction drawings to generate an prefabricated BIM model; wherein step S23 includes the following steps: Step S231: acquiring point cloud data through three-dimensional laser scanning; performing high-dimensional data analysis on the prefabricated building node filling data and the point cloud data to generate prefabricated building principal component analysis data and prefabricated building independent component analysis data; Step S232: constructing a BIM three-dimensional model based on the prefabricated building principal component analysis data and the prefabricated building independent component analysis data to generate an initial BIM model; Step S233: calibrating the geometric accuracy of the initial BIM model based on the design drawing data to generate a calibrated BIM model; assigning physical and mechanical properties to the calibrated BIM model based on the construction drawing data to generate an assembled BIM model, wherein the assembled BIM model includes a timing module and a cost module; Step S3: perform dimensional simulation on the prefabricated BIM model to generate a BIM dimensional simulation plan; conduct a comprehensive evaluation of the BIM dimensional simulation plan in terms of time, cost and prefabricated assembly simulation to generate a multi-dimensional comprehensive result; wherein step S3 includes the following steps: Step S31: Perform multi-dimensional simulation on the prefabricated BIM model to generate a 4D time dimension solution and a 5D cost dimension solution; Step S32: integrating the 4D time dimension solution and the 5D cost dimension solution into a multi-dimensional scale simulation solution to generate a BIM dimension simulation solution; Step S33: Establishing an evaluation system through multi-criteria decision making to comprehensively evaluate the BIM dimensional simulation scheme in terms of time, cost and prefabricated assembly simulation, and generating a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score; Step S4: Use the multi-dimensional comprehensive results and BIM dimension simulation solutions to build a management strategy and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

2. The method for constructing a prefabricated building based on BIM according to claim 1, characterized in that: Step S22 includes the following steps: Step S221: extracting geological segmentation data features by using point cloud segmentation on the terrain, elevation and geomorphic features to generate geological feature segmentation data; Step S222: Establishing network topology nodes for the geological feature segmentation data to generate a geographic information network topology map; Step S223: Filling the geographic information network topology map with standardized model data to generate Kalman filter filling data; filling the Kalman filter filling data with prefabricated building nodes to generate prefabricated building node filling data.

3. The method for constructing a prefabricated building based on BIM according to claim 1, characterized in that: Step S31 includes the following steps: Step S311: perform critical path timing analysis on the timing module to generate time dimension 4D data; perform 4D dynamic simulation on the time dimension 4D data to generate BIM time dimension dynamic data; generate a time dimension solution based on the BIM time dimension dynamic data to generate a 4D time dimension solution; Step S312: Obtain a cost database; extract cost data from the cost database, and perform Monte Carlo simulation on the cost module to generate cost traceability data; dynamically combine the cost traceability data and the cost data for earned value management analysis to generate BIM dynamic cost data; generate a cost dimension plan based on the BIM dynamic cost data to generate a 5D cost dimension plan.

4. The method for constructing a prefabricated building based on BIM according to claim 1, characterized in that: Step S33 includes the following steps: Step S331: extracting time series data, cost data and component data from the prefabricated BIM model; generating a time series diagram based on the time series data to generate a prefabricated building time series diagram; performing autoregressive integral moving average prediction trend analysis on the prefabricated building time series diagram to generate time performance evaluation data; Step S332: performing multidimensional distribution estimation on the cost data to generate cost distribution estimation data; performing variational Bayesian inference on the cost distribution estimation data to generate cost-benefit evaluation data; Step S333: simulating the assembly process based on the component data to generate assembly process simulation data; using the finite element analysis method to analyze the component connection strength of the assembly process simulation data to generate component connection strength data; performing an accelerated life test on the component connection strength data to generate durability data; using laser scanning technology to perform high-precision measurement of the design drawing data and the assembled BIM model to generate assembly deviation data; performing fuzzy comprehensive evaluation based on the component connection strength data, durability data and assembly deviation data to generate assembly characteristic evaluation data; Step S334: embed the time performance evaluation data, the cost-effectiveness evaluation data and the assembly characteristic evaluation data into a graph for multi-dimensional comprehensive evaluation to generate a multi-dimensional comprehensive result, wherein the multi-dimensional comprehensive result includes a multi-dimensional high score and a multi-dimensional low score.

5. The method for constructing a prefabricated building based on BIM according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: construct a management strategy using the multi-dimensional comprehensive results and the BIM dimension simulation solution to generate a preliminary BIM assembly management strategy; Step S42: performing multi-agent dynamic simulation optimization on the BIM assembly management strategy to generate assembly dynamic simulation data; Step S43: Utilize the prefabricated dynamic simulation data to optimize the BIM prefabricated management preliminary strategy through two-dimensional graph fitting analysis strategy, and generate a BIM prefabricated management strategy to complete the prefabricated building construction operation.

6. The method for constructing a prefabricated building based on BIM according to claim 5, characterized in that: Step S41 includes the following steps: Step S411: constructing smart contract management based on the multi-dimensional comprehensive results and the BIM dimension simulation solution, and generating a preliminary BIM assembly management strategy, wherein the preliminary BIM assembly management strategy includes a high-scoring management strategy and a low-scoring management strategy; Step S412: When the multi-dimensional comprehensive result is a multi-dimensional high score, the manpower, material and equipment demand forecast is performed on the prefabricated BIM model to generate resource demand data; the resource hierarchy analysis is performed on the design drawings to generate resource availability data; the cost data is optimized to minimize the construction period through a genetic algorithm to generate cost optimization allocation data; the critical path optimization is performed on the time series data to generate optimized progress data; Step S413: Optimizing resource allocation for the BIM dimension simulation solution based on resource demand data, resource availability data, cost optimization allocation data, and optimization progress data, thereby generating a high-scoring management strategy; Step S414: When the multi-dimensional comprehensive result is a multi-dimensional low score, the 4D time dimension plan and the 5D cost dimension plan are subjected to risk control monitoring and processing, the prefabricated building monitoring result data is generated and an early warning threshold is set; when the prefabricated building monitoring result data exceeds the early warning threshold, a low score management strategy is generated and an early warning message is sent.

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