Fabricated building construction progress automatic monitoring method

Abstract

The invention relates to the technical field of building construction, in particular to a prefabricated building construction progress automatic monitoring method. The method comprises the following steps: obtaining a building construction design drawing; based on the building construction design, prefabricated building component design data is extracted; building a building construction three-dimensional model based on the prefabricated building component design data; building component transportation simulation is conducted according to the building construction three-dimensional model, and building component transportation data are obtained; identifying a component cracking degree based on the building component transportation data; performing connection failure analysis based on the component cracking degree to obtain component connection failure data; predicting a reinforcement corrosion risk based on the component connection failure data; evaluating assembly durability based on the reinforcement corrosion risk; and extracting hoisting design data of the building construction design drawing. Based on the building construction technology, the efficiency and accuracy of prefabricated building construction progress monitoring are improved, and the accuracy of construction plan execution and the resource scheduling efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a method for automatically monitoring the construction progress of an assembled building. Background Art

[0002] The traditional automatic monitoring method for the construction progress of prefabricated buildings has a delayed and incomplete information collection process, usually relying on manual records, fixed-point inspections or simple on-site image collection methods, which cannot achieve real-time dynamic perception of the component transportation process, installation process and structural response, resulting in delayed and inaccurate construction progress data; the integrated analysis of the entire construction process is usually limited to the surface comparison between the construction plan and the actual progress, ignoring the multi-factor and multi-stage correlation mechanism from component design, transportation, installation, structural connection to pollution impact, and lacks a unified model support and linkage analysis framework. When faced with complex construction site environments (such as obstacles, pollutants, weather impacts, etc.), it is difficult to flexibly identify and dynamically adjust construction strategies, and it is impossible to effectively The ineffective integration of key control factors such as lifting path planning, obstacle detection and component collision prediction leads to large deviations between actual operations and simulations. The indirect effects of component connection failure, steel bar corrosion, and oil accumulation on durability and construction rhythm are ignored. There is a lack of the ability to combine structural degradation mechanisms with construction progress assessment, making it difficult to timely warn of potential progress delays or quality risks. There is a lack of refined and data-based means in construction progress assessment, and it is impossible to achieve the integrated analysis of multi-source heterogeneous information such as pollution, obstacles, and connection defects. Only a rough assessment is made by comparing planned and actual quantities, which cannot reflect the actual construction status. This leads to decision-making blind spots and response lags in construction management, which restricts the development of intelligent and efficient prefabricated building construction. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for automatically monitoring the construction progress of prefabricated buildings to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a method for automatically monitoring the construction progress of an assembled building comprises the following steps: Step S1: obtaining a building construction design drawing; extracting prefabricated building component design data based on the building construction design; and constructing a building construction three-dimensional model based on the prefabricated building component design data; Step S2: Simulating the transportation of building components based on the three-dimensional building construction model to obtain building component transportation data; identifying the degree of component cracking based on the building component transportation data; performing connection failure analysis based on the degree of component cracking to obtain component connection failure data; predicting the risk of steel bar corrosion based on the component connection failure data; and assessing component durability based on the steel bar corrosion risk. Step S3: extracting the hoisting design data from the building construction design drawings; optimizing the hoisting path according to the hoisting design data to obtain an optimized hoisting path; and identifying obstacles according to the optimized hoisting path to obtain obstacle data. Step S4: Perform component contamination analysis based on the obstacle data to obtain component contamination data; evaluate the construction delay progress based on the component contamination data and the obstacle data; and upload the construction delay progress to the construction monitoring platform to perform the automatic construction monitoring task.

[0005] The present invention achieves multi-dimensional automatic monitoring of construction progress by integrating key links such as component design, transportation simulation, connection status analysis, hoisting path optimization, and pollution identification in the construction process of prefabricated buildings. First, by extracting building component design data and constructing a three-dimensional model with the help of building construction design drawings, it is possible to accurately express the spatial structure and assembly relationship of prefabricated building components, laying the model foundation for subsequent intelligent analysis of construction progress. Through transportation simulation of the three-dimensional model, the dynamic response behavior of the components during transportation can be predicted in advance, thereby assisting in identifying the fatigue risk and cracking degree of the components and improving the proactiveness of construction quality control. Based on the component cracking analysis, the risk of connection failure and steel corrosion is further predicted, which helps to achieve durability assessment and structural degradation early warning, and enhances the proactive management capability of construction quality and progress delays. Combining hoisting design data to carry out path optimization not only improves the safety and efficiency of hoisting operations, but also assists in realizing intelligent scheduling and dynamic path adjustment of hoisting operations by analyzing indicators such as hook sway and overload rate in crane operation data. The obstacle identification process further integrates point cloud data based on the path space model for collision detection, improving the ability to perceive obstacles in complex on-site environments and the ability to avoid them during lifting. In terms of pollution analysis, by extracting oil accumulation information from obstacle data and identifying the location of contaminated components, the contaminated components are identified in combination with the pollution diffusion trend, thereby refining the evaluation dimension of component durability. Finally, key data from the construction process is uploaded to the construction monitoring platform, building a full-process, multi-factor intelligent monitoring system that integrates design data, transportation response, structural status, construction environment and pollution factors. This effectively makes up for the shortcomings of traditional monitoring methods such as data acquisition lag, lack of real-time response and full-process analysis capabilities, and realizes refined, dynamic and visual automatic monitoring of construction progress, which will help promote the overall improvement of the intelligent level of prefabricated building construction.

[0006] Preferably, step S1 is specifically as follows: Step S11: Obtaining building construction design drawings; Step S12: extracting prefabricated building component design data based on the building construction design; Step S13: Collect component codes according to prefabricated building component design data; Step S14: Classify component types based on component codes to obtain basic component data and connected component data; Step S15: defining a connection mode for the basic component data according to the connection component data to obtain connection mode data; Step S16: Constructing a three-dimensional building construction model based on the basic component data, the connection component data, and the connection method data.

[0007] The present invention achieves the structured collection and digital expression of information in the initial stage of prefabricated building construction by refining the component data extraction and three-dimensional model construction process, overcoming the shortcomings of traditional monitoring methods that rely on manual records, data acquisition lags, and missing structural information. Through digital analysis of design drawings, basic data such as component structure, position, type, etc. are comprehensively extracted to ensure the integrity and accuracy of model input; with the help of component coding mechanism, unique identification and traceability management of components are achieved, providing data support for subsequent transportation tracking, installation status analysis and damage location. The classification of component types not only helps to distinguish the functional positioning of the main structural components and connecting parts, but also provides a logical basis for connection behavior modeling and force analysis. The systematic definition of the connection method enables the component overlap logic during construction to be clearly reflected in the model, laying the foundation for subsequent calculations such as assembly relationship simulation and collision detection. Integrating data on basic components, connection components, and connection methods in the construction of three-dimensional models not only forms a digital twin that truly restores the building structure, but also enables the entire model to have expansion capabilities such as dynamic simulation, path planning, and state prediction. It supports subsequent multi-stage, multi-factor intelligent linkage analysis from the source, effectively filling the technical gap of traditional methods that lack unified model support and a full-process analysis framework, and provides a strong guarantee for the realization of full-process, multi-dimensional, and visual construction progress monitoring.

[0008] Preferably, step S16 is specifically as follows: Step S161: Determine the construction site framework based on the basic component data; Step S162: lap-joining building components on the construction site skeleton based on the connection component data to obtain building component lap-joining data; Step S163: performing component collision detection based on the building component overlap data to obtain component collision data; Step S164: adjusting the construction sequence based on the number of component collisions to obtain construction sequence adjustment data; Step S165: adjusting the data according to the construction sequence to perform virtual assembly of the model to obtain a three-dimensional model of the building construction.

[0009] The present invention realizes spatial logic verification and dynamic construction path optimization in the construction modeling stage by constructing a construction site skeleton and performing component overlap and collision detection, breaking through the problems of static models and rigid construction sequences in traditional methods. The construction skeleton is built based on basic component data to provide a geometric basis and spatial constraints for subsequent spatial layout and structural assembly, thereby enhancing the spatial authenticity of the model; on this basis, the connection component data is introduced to perform component overlap, which not only realizes the accurate expression of the component splicing relationship, but also lays the foundation for subsequent installation logic simulation. Collision detection can timely identify potential spatial conflicts and component interference problems, avoid construction obstacles caused by improper installation sequence or layout overlap, and effectively reduce construction rework rate and safety hazards. With the help of collision data, the construction sequence is dynamically adjusted to make the construction process more in line with the actual conditions on site, thereby improving the overall construction efficiency and rationality. Finally, the adjusted sequence is embedded in the virtual assembly process, giving the three-dimensional construction model the functions of time evolution and logical assembly. This not only improves the dynamic simulation capability of the model, but also realizes the linkage expression of the entire process from component layout to construction scheduling, providing high-precision and high-reliability model support for multi-stage progress simulation, assembly coordination and progress optimization, and solving the technical bottleneck of the traditional method's lack of model-driven and dynamic adjustment capabilities of construction strategies.

[0010] Preferably, step S2 is specifically as follows: Step S21: performing a building component transportation simulation based on the building construction three-dimensional model to obtain building component transportation data; Step S22: Calculating transportation vibration values ​​based on the transportation data of the building components; obtaining the yield strength of the building components; and predicting fatigue areas of the building components based on the transportation vibration values ​​and the yield strength of the building components. Step S23: extracting low-thickness building component data based on the building component transportation data; performing crack region prediction on the low-thickness building component data according to the transportation vibration value to obtain crack region data; Step S24: performing component crack region intersection calculation based on the building component fatigue region and crack region data to obtain component crack region data; determining the component crack degree based on the component crack data; Step S25: performing connection failure analysis based on the degree of component cracking to obtain component connection failure data; Step S26: predicting steel bar corrosion risk based on component connection failure data; Step S27: Assessing component durability based on the steel bar corrosion risk.

[0011] This invention effectively addresses the technical shortcoming of traditional monitoring methods, which cannot detect the impact of the transportation process on component performance, by introducing dynamic simulation and structural damage prediction analysis driven by three-dimensional models of building components during the transportation phase. By leveraging transportation simulation and statistical analysis of vibration data, the effects of transportation loads on component fatigue can be accurately modeled. Combined with component yield strength, fatigue-prone areas can be dynamically identified, providing a foundation for component health status monitoring. Furthermore, a crack prediction model is established for low-thickness components. By linking vibration excitation with weak area analysis, potential crack risk points are identified in advance, improving the sensitivity and accuracy of crack identification. Intersection analysis of fatigue and crack areas not only enhances the reliability of component crack detection but also clarifies the cracking scope and severity, forming a basis for component integrity assessment. Connection failure analysis is then conducted based on the degree of cracking, extending from microscopic damage to structural response deduction at the component connection level, providing enhanced fault chain tracing capabilities. Rebar corrosion risk is deduced through failure data, linking connection degradation with material degradation processes, promoting a deeper understanding of degradation mechanisms, moving from representative damage. Ultimately, the corrosion risk is mapped to the component durability assessment, making the assessment results quantifiable, traceable and predictable, significantly improving the digital perception level and quality control capabilities of the component status throughout the construction process, and realizing the full-process prediction and dynamic management of structural integrity and life performance under the influence of the transportation link, overcoming the shortcomings of traditional construction monitoring that lacks response to intermediate processes and degradation mechanisms.

[0012] Preferably, step S25 is specifically as follows: Step S251: calibrating the component crack area based on the component crack degree; Step S252: performing three-dimensional displacement detection on the cracked area of ​​the component to obtain three-dimensional displacement data of the component; Step S253: Calculating a three-dimensional displacement value based on the component three-dimensional displacement data; Step S254: performing over-limit displacement determination on the three-dimensional displacement value according to a preset three-dimensional displacement threshold to obtain over-limit displacement data; Step S255: identifying the component connection mode based on the component crack area; Step S256: performing connection failure determination based on the component connection mode and the over-limit displacement data to obtain connection failure data.

[0013] By combining crack zone calibration with three-dimensional displacement detection, this method can accurately identify and quantify component damage, overcoming the limitations of traditional monitoring methods that cannot dynamically detect component deformation and damage. Based on the analysis of the degree of cracking, component deformation data can be updated in real time, and displacement values ​​can be calculated based on this, providing data support for refined monitoring. By determining over-limit of three-dimensional displacement, components that exceed the normal deformation range can be effectively identified, potential structural problems can be promptly discovered, and a scientific basis for subsequent failure determination can be provided. This method, combined with the identification of component connection methods, further improves the ability to diagnose connection failures, especially for subtle deformation and fatigue accumulation in complex connection methods, and can more sensitively and accurately warn of the occurrence of connection failures. This process significantly improves the efficiency of identifying component health status through dynamic perception, real-time monitoring, and detailed analysis. It avoids the shortcomings of traditional monitoring methods that rely on manual inspections and static analysis, and achieves early warning of component failure and precise control of construction progress, thereby effectively reducing potential quality risks and ensuring the stability and safety of prefabricated building construction.

[0014] Preferably, step S256 is specifically as follows: Identify the connection mode identification field according to the component connection mode, and obtain wet joint connection data and bolt connection data; Calculate shear force based on wet joint connection data; calculate tensile force based on wet joint connection data; Perform stress conversion analysis on shear force based on tensile force to obtain wet joint stress conversion data; Stress intensification identification is performed on wet joint stress conversion data according to the over-limit displacement data to obtain wet joint stress intensification data; Evaluate the degree of failure of wet joint connections based on wet joint stress intensification data; Identify the degree of bolt loosening based on bolt connection data; Assess bolt wear based on bolt looseness; Evaluate the degree of failure of bolted connections based on the amount of bolt wear; The failure degree of wet joint connection and the failure degree of bolt connection are integrated to obtain the connection failure data.

[0015] By accurately identifying and calibrating wet joints and bolted connection methods, the present invention can provide quantitative monitoring data for different connection types, breaking through the limitations of traditional methods that ignore or cannot refine connection methods. For wet joint connections, shear force and tensile force are calculated and stress conversion analysis is performed, which can accurately assess the stress conditions encountered by the joints during construction and use, thereby identifying potential stress intensification areas and predicting the failure risk of wet joints in advance. This stress intensification identification method effectively enhances the ability to assess the degree of failure of wet joint connections, avoiding the errors caused by traditional methods that rely solely on visual observation or empirical judgment. For bolted connections, combined with the analysis of looseness and wear, it is helpful to monitor the health status of the bolts in real time, evaluate their durability, and promptly warn of the failure trend of the bolted connections. Integrating the failure data of wet joints and bolted connections can more comprehensively reflect the connection health status of the entire building structure, provide more accurate data support for quality monitoring and risk management during the construction process, and avoid the incompleteness of single connection method analysis. This method not only improves the ability to control construction quality, but also provides strong data support for optimizing construction progress and improving structural durability, greatly improving the safety and predictability of construction.

[0016] Preferably, step S26 is specifically as follows: Step S261: calibrating the failed component based on the component connection failure data to obtain failed component data; Step S262: Acquire corrosive medium data and perform preprocessing to obtain corrosive medium data to be processed; Step S263: performing steel bar corrosion simulation on the failed component data according to the corrosive medium data to be processed to obtain steel bar corrosion data; Step S264: Calculating the steel bar corrosion rate based on the steel bar corrosion data; Step S265: Evaluate the steel bar failure life according to the steel bar corrosion rate; Step S266: Estimate the corrosion risk based on the steel bar failure life.

[0017] The application can accurately identify which components have failed in the construction process by calibrating the failed components, providing a clear data basis for subsequent damage assessment and repair, and avoiding the situation of missing failed components in the traditional method. Combined with the acquisition and preprocessing of erosive medium data, external environmental factors and component failure risks can be combined to more accurately simulate the process of steel corrosion. Through steel corrosion simulation and data analysis, not only does it provide a scientific basis for assessing the corrosion degree of steel, but also can calculate the remaining life of steel according to the corrosion rate, thereby providing decision support for early warning and optimizing the repair cycle. The calculation of steel corrosion rate and the assessment of failure life reveal the dynamic changes of structural deterioration more finely, effectively combining environmental factors and construction progress, and breaking the bottleneck of relying on experience and rough estimation in traditional methods. Finally, by estimating the corrosion risk, high-risk areas can be accurately identified, and construction progress and strategies can be adjusted in time to minimize safety hazards caused by corrosion and ensure the overall stability and durability of construction. This method not only improves monitoring accuracy, but also provides strong technical support for comprehensive analysis of multiple dimensions and multiple factors in the construction process.

[0018] Preferably, step S3 is specifically: Step S31: Extracting hoisting design data of the building construction design drawing; Step S32: Simulating crane operation according to the hoisting design data to obtain crane operation data; Step S33: Calculating the hook swing amplitude based on the crane operation data; designing a smooth hoisting path according to the hook swing amplitude; Step S34: Calculating the crane overload rate based on the crane operation data; distributing intelligent hoisting tasks according to the crane overload rate; Step S35: Integrating the smooth hoisting path and the intelligent hoisting task to obtain an optimized hoisting path; Step S36: Identifying obstacles according to the optimized hoisting path to obtain obstacle data.

[0019] By extracting the hoisting design data, the present invention can accurately understand the hoisting requirements during the construction process and provide a detailed design basis for subsequent crane operations. Based on the results of the crane operation simulation, the dynamics of the crane operation can be predicted in advance, accurate operation data can be obtained, and then the swing amplitude of the hook can be calculated to design a more stable hoisting path. The optimization of this process can reduce the safety hazards caused by shaking and improve construction efficiency. Calculating the crane overload rate and reasonably allocating intelligent hoisting tasks can help ensure that the crane operates within a safe load range, thereby avoiding the risks and equipment damage caused by overload operation. Integrating the optimized hoisting path and intelligent task allocation can further improve hoisting efficiency and reduce time wasted due to unreasonable paths. Finally, through obstacle recognition, obstacles at the construction site can be identified in real time, and the hoisting path can be adjusted in time, avoiding the risk of construction progress and safety being affected by environmental instability or uncertain factors. These steps work together to improve the accuracy and safety of the hoisting process, significantly improve construction efficiency and reduce potential risks.

[0020] Preferably, step S36 is specifically as follows: Step S361: performing path space area modeling according to the optimized lifting path, thereby obtaining a path space model; Step S362: Obtaining point cloud scanning data of obstacles at the construction site; Step S363: Mapping the construction site obstacle point cloud scanning data to the path space model to obtain path space obstacle data; Step S364: performing hoisting collision detection on the path space obstacle data according to the optimized hoisting path to obtain hoisting collision data; Step S365: Obstacle identification is performed based on the hoisting collision data to obtain obstacle data.

[0021] The present invention can accurately construct a three-dimensional spatial model of the lifting path through path space modeling, providing a visual spatial framework for subsequent construction operations. This process helps to ensure the compatibility of the lifting path with the surrounding environment, avoiding unnecessary interference and unsafe factors. Obtaining point cloud scanning data of obstacles at the construction site can comprehensively and in real time capture the specific environment of the site, obtain high-precision obstacle information, and further improve the accuracy of path planning. Mapping these point cloud data into the path space model helps to accurately understand the construction site environment and ensure the effectiveness of path planning in actual operations. By performing lifting collision detection on obstacles in the path space, potential collision risks can be identified in a timely manner, reducing delays or damage to lifting operations caused by obstacle interference. Further refinement of this process, through obstacle identification and detection, allows complex factors at the construction site to be identified and addressed in real time, thereby dynamically adjusting the construction plan during the operation, avoiding the occurrence of construction accidents, and improving safety and efficiency during the construction process.

[0022] Preferably, step S4 is specifically: Step S41: oil stain accumulation detection is performed according to the obstacle data, and oil stain accumulation data is obtained; Step S42: the component oil stain position is calibrated based on the oil stain accumulation data; Step S43: chemical component analysis is performed on the component oil stain position, and component oil stain component data is obtained; Step S44: the component oil stain aging is evaluated based on the component oil stain component data; Step S45: the oil stain diffusion trend is estimated based on the component oil stain aging, and the contaminated component is calibrated according to the oil stain diffusion trend, and component pollution data is obtained; Step S46: the construction delay progress is evaluated according to the component pollution data and the obstacle data; Step S47: the construction delay progress is uploaded to the building construction monitoring platform to perform the building construction automatic monitoring task.

[0023] The present application can accurately identify the oil stain accumulation area that affects the construction progress and quality in the construction site through oil stain accumulation detection, providing data support for subsequent pollution control. The calibration of the oil stain position helps to accurately understand the specific impact of the oil stain on the component, facilitating the targeted strengthening of subsequent cleaning and processing operations. The chemical analysis of the component oil stain component can deeply understand the corrosiveness of the oil stain to the component material and its potential risks, so as to take more accurate cleaning and maintenance measures to ensure that the construction quality is not affected by the pollutants. The oil stain aging evaluation can identify the aging degree of the oil stain, and then predict the diffusion trend of the oil stain, providing effective basis for preventing and controlling the diffusion of the oil stain and improving the construction environment. By estimating the diffusion trend of the oil stain, the construction strategy can be adjusted in time, the contaminated component can be calibrated and cleaned, so as to avoid further pollution affecting the construction progress. Based on the comprehensive evaluation of the component pollution data and the obstacle data, the construction delay progress can be effectively predicted, and timely warning information can be provided to the construction management personnel, so that measures can be taken quickly to reduce the delay of the progress caused by pollution. Finally, the construction delay progress is uploaded to the building construction monitoring platform, which can realize automatic monitoring and dynamic adjustment, ensure real-time perception and accurate management of the construction progress, avoid the influence of human factors, improve the precision and efficiency of construction management, and promote the intelligentization of prefabricated building construction. BRIEF DESCRIPTION OF DRAWINGS

[0024] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings: Figure 1 The step flowchart of the prefabricated building construction progress automatic monitoring method of the present application; Figure 2Detailed step flow diagram of step S1 in the present invention; Figure 3 Detailed flowchart of step S16 in the present invention; The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described 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 those skilled in the art without making creative efforts are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0027] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for automatically monitoring the construction progress of an assembled building, the method comprising the following steps: Step S1: obtaining a building construction design drawing; extracting prefabricated building component design data based on the building construction design; and constructing a building construction three-dimensional model based on the prefabricated building component design data; In this embodiment, the building construction design drawings are obtained, mainly including the plan layout, elevation, structure diagram, etc. of the building, and the professional CAD software (such as AutoCAD) is used for drawing import and conversion into a computer-recognizable format. Then, the prefabricated building component design data is extracted from the building design drawings, focusing on the size, shape, material, connection method of the components, and the load-carrying capacity, structural requirements, etc. of each component. These data can be automatically recognized and extracted by marking, symbols and data tables in the drawings, and the optical character recognition (OCR) technology is used to recognize the text information in the drawings to ensure the accuracy and integrity of the data. Then, based on the extracted prefabricated building component design data, the three-dimensional modeling software (such as Revit or Tekla Structures) is used to build the three-dimensional model of the building construction. In the three-dimensional modeling process, considering the actual size, structural relationship and assembly sequence of the building components, the components are accurately positioned to complete the construction of the three-dimensional model of the building construction, ensuring the accuracy and practicality of the model, and providing support for the subsequent construction process.

[0029] Step S2: Building component transportation simulation is performed according to the three-dimensional model of the building construction, and building component transportation data is obtained; the component cracking degree is identified based on the building component transportation data; connection failure analysis is performed based on the component cracking degree, and component connection failure data is obtained; the reinforcement corrosion risk is predicted based on the component connection failure data; and the component durability is evaluated based on the reinforcement corrosion risk; In this embodiment, the transportation process of building components is simulated using construction simulation software (such as Navisworks) based on a three-dimensional building construction model. During the transportation simulation, data such as the dimensions, weight, transportation route, and vehicle specifications of each component are input to simulate the component's transport path from the warehouse or factory to the construction site. The generation of transportation data involves a path planning algorithm that optimizes the simulated path based on the site's actual conditions, obstacle distribution, and traffic conditions. During the simulation, sensor technologies (such as GPS positioning and accelerometers) are used to obtain dynamic position information of the components, monitoring the movement trajectory and status of each component to ensure a smooth transportation process. During the simulation, vibration monitoring equipment is used to detect component vibration based on the component transportation data. Vibration sensors (such as accelerometers) capture vibration frequency and amplitude data during transportation, thereby identifying the degree of component cracking. A threshold is typically set; for example, a vibration frequency exceeding a certain value (e.g., 5Hz) is identified as a risk of cracking. When the cracking level reaches the set threshold, component connection failure analysis is performed in conjunction with stress analysis to obtain component connection failure data. Calculations are performed using simulated connection point data, material performance parameters, and operating conditions to determine the likelihood of connection failure. Subsequently, based on the failure data, a corrosion rate model is used to predict the corrosion risk of the rebar. Factors such as humidity and temperature in the construction environment are considered, and a corrosion rate threshold, such as 10%, is set to assess component durability and ensure the safety of each component during construction.

[0030] Step S3: extracting the hoisting design data from the building construction design drawings; optimizing the hoisting path according to the hoisting design data to obtain an optimized hoisting path; and identifying obstacles according to the optimized hoisting path to obtain obstacle data. In this embodiment, lifting data is extracted from the construction design drawings using CAD software or a BIM platform. This data primarily includes the crane's location, the model of the lifting equipment, the lifting path, the lifting weight, and its support points. The lifting design data requires detailed records of the crane's operating range, maximum lifting capacity, and stability and performance parameters. Based on the lifting design data, a lifting path optimization algorithm is used to plan the lifting path. The optimized path should take into account the component size, the crane's operating space, and site constraints. During the path optimization process, the optimal path is determined by detecting obstacles along the lifting path, evaluating the crane's operating space, and comprehensively analyzing the load capacities of different crane types. During this process, path optimization criteria can be set, such as minimizing the lifting path's operating radius or the maximum load across the bridge. The optimized path results can be visualized using 3D modeling software. Next, obstacle identification is performed based on the optimized lifting path. First, actual obstacle data for the site is acquired using 3D scanning data of the site (e.g., obtained using a lidar system). Obstacles are then classified, such as overhead obstacles and ground obstacles. Image processing technology and spatial data analysis tools are used to detect and locate obstacles and obtain detailed obstacle data, including the type, size, location and its impact on the lifting path.

[0031] Step S4: Perform component contamination analysis based on the obstacle data to obtain component contamination data; evaluate the construction delay progress based on the component contamination data and the obstacle data; and upload the construction delay progress to the construction monitoring platform to perform the automatic construction monitoring task.

[0032] In this embodiment, image recognition technology and pollution monitoring instruments are used in conjunction with on-site sensors (such as gas sensors and oil sensors) to acquire on-site pollution data. Component contamination data is generated through a detailed analysis of pollutant types, concentrations, and their potential impacts on components. This pollution data includes not only the type and concentration of pollutants, but also their effects on components, such as corrosion and mechanical performance degradation. After acquiring component contamination data, the construction scheduling model is used to assess construction delays, combined with obstacle data. This assessment dynamically predicts the construction progress by analyzing the construction schedule and the impact of pollution data on the progress (such as work stoppages or extended worker protection time due to pollution). A threshold for construction delay is set, such as an alarm for delays exceeding two hours. Finally, the assessed construction delay data is uploaded to a construction monitoring platform. This platform monitors the progress of the construction site in real time and automatically adjusts based on the actual construction status, ensuring that every task is carried out according to plan, responding promptly to emergencies, and ensuring the smooth execution of automated monitoring tasks.

[0033] Preferably, step S1 is specifically as follows: Step S11: Obtaining building construction design drawings; In this embodiment, to obtain the building construction design drawings, first obtain the relevant building construction drawings from the project design team, including floor plans, elevations, sections, and structural drawings. The design drawings are generally provided in the form of electronic files, usually in CAD format (such as DWG or DXF format). Use CAD software (such as AutoCAD) to open the design drawing file to ensure that the drawing size and scale are consistent with the actual building requirements. At this time, the key elements of the drawing include the size of the building, the location and specifications of the building components (such as walls, floor slabs, roofs, etc.), and various construction data (such as construction materials, load requirements, etc.). Based on the drawing data, the "data extraction" function in the software tool is used to automatically extract the key parameters of the various components involved in the drawing (such as component number, size, shape, etc.), and save this information as structured data for subsequent use.

[0034] Step S12: extracting prefabricated building component design data based on the building construction design; In this example, detailed design parameters for each component are extracted based on the component type (e.g., exterior walls, interior walls, floor slabs, beams, columns, etc.) and function, combined with the component information in the drawings. Data such as the geometric dimensions, material specifications, and design strength of the prefabricated building components are obtained through the dimensioning, material identification, and structural symbols in the drawings. Specifically, the "attribute extraction" function in the CAD software can be used to extract basic parameters for each component, including but not limited to length, width, height, weight, material type, and compressive strength. This data will form the design data for the building components and serve as the foundation for the subsequent construction of a 3D building construction model.

[0035] Step S13: Collect component codes according to prefabricated building component design data; In this embodiment, component codes are collected based on the prefabricated building component design data extracted from the building construction design. A component code is a unique identifier for each prefabricated building component, typically generated by the manufacturer or designer during the design phase and indicated on the design drawings. The code for each component can be assigned based on different characteristics within the design data. For example, a floor slab component is assigned a unique number based on factors such as its type, size, material, and production batch. During implementation, each component is coded using a data management system or database tool (such as Excel or specialized BIM management software) based on the coding information noted in the design drawings, ensuring that each component can be uniquely identified during subsequent construction and tracking processes.

[0036] Step S14: Classify component types based on component codes to obtain basic component data and connected component data; In this embodiment, in prefabricated buildings, components are generally divided into basic components and connecting components. Basic components include structural components such as load-bearing walls, floor slabs, beams and columns; connecting components are parts used to connect the various basic components together, such as connecting plates, bolts, supports, etc. Based on the component coding information, all components are divided into two categories according to their functions and structural characteristics. To ensure accurate classification, it is necessary to strictly divide them according to the design drawings and known construction specifications using standardized parameters (such as bearing capacity, purpose, material specifications, etc.). For example, the components of the load-bearing walls will be classified as "basic components", while all components such as connecting plates and bolts will be classified as "connecting components". This division not only helps with construction site management, but also provides basic data for the subsequent construction of a three-dimensional model of the building construction.

[0037] Step S15: defining a connection mode for the basic component data according to the connection component data to obtain connection mode data; In this embodiment, the data definition of the connection method is usually set according to the connection form of the component (such as bolt connection, welding, tongue and groove connection, etc.). In the construction design drawings, the connection position and connection method of each component are usually indicated, and identification is performed based on these marking information. During the implementation process, the connection method between each basic component and the connection component is first defined based on the data of the connection component. For example, if the design drawing clearly states that the connection between the wall and the floor is connected by bolts, then the connection method data should include the specifications, quantity, layout position, etc. of the bolts. In some complex connection methods, it is necessary to rely on the guidance of structural engineers and adopt methods such as mechanical analysis and material strength calculation to determine the specific implementation of the connection method. These connection method data will serve as an important basis for the relationship between the components in the three-dimensional model of building construction.

[0038] Step S16: Constructing a three-dimensional building construction model based on the basic component data, the connection component data, and the connection method data.

[0039] In this embodiment, a three-dimensional model of the building construction begins to be constructed based on the basic component data, connection component data, and connection method data. At this point, BIM modeling software (such as Revit or Tekla Structures) is used to input this data and create the three-dimensional geometry of each building component based on information such as the basic component's dimensions, materials, and load requirements. Simultaneously, the connection method data is used to precisely connect the basic components and connection components according to the design requirements, ensuring that the structural relationships and connection methods in the model are consistent with actual construction requirements. The three-dimensional model must not only include the spatial position of each component but also consider factors such as the connection strength and stability between components. During this process, advanced automated modeling capabilities are used to automatically connect each component correctly according to the design drawings through programmed scripts or the software's intelligent algorithms. The resulting three-dimensional model will detail the structural layout of the entire prefabricated building, providing accurate visual support for construction management, progress tracking, and subsequent quality control.

[0040] Preferably, step S16 is specifically as follows: Step S161: Determine the construction site framework based on the basic component data; In this embodiment, foundation component data includes the geometric dimensions, materials, load-bearing requirements, and location information of various building components. This data is typically derived from building construction design drawings and previously extracted design data. The construction site skeleton refers to the basic framework structure of the construction site, including the foundation, structural load-bearing walls, floor slabs, beams, and columns. Using BIM modeling tools (such as Revit and Tekla), foundation components are precisely arranged according to the dimensions and positions specified in the design drawings, forming a preliminary building skeleton structure. During this process, "alignment" and "distribution" functions are used to ensure that all foundation components are correctly placed according to the predetermined design requirements. Foundation component data also includes the overall building height, elevations of each floor, and vertical and horizontal dimensions to ensure the geometric accuracy of the construction site skeleton. The design of the skeleton structure also considers the load-bearing capacity of the foundation components and the compatibility with adjacent components. For example, by comparing the load-bearing requirements of the floor slabs with the compressive strength of the walls, it is ensured that there is no mismatch in the structural load-bearing capacity of the foundation components. When determining the construction skeleton, all component connection nodes must be clearly marked to provide a basis for subsequent component overlap and collision detection.

[0041] Step S162: lap-joining building components on the construction site skeleton based on the connection component data to obtain building component lap-joining data; In this embodiment, building components are overlapped on the construction site skeleton based on the connection component data. The key to this step is to clarify the connection method and location between different foundation components based on the connection component data extracted earlier. Connection component data typically includes the type of connector (such as bolts, welds, connecting plates, etc.), specifications, quantity, and the spatial coordinates of each connection point. In specific operations, the connection method between each foundation component and its adjacent components is first determined based on the building design drawings. For example, the connection between the floor slab and the load-bearing wall is achieved through angle steel, while the connection between the beam and the column is achieved through bolts or steel mesh. In the BIM tool, the automated connection function is used to connect the foundation components based on the connection data. At the construction site, these connectors ensure the stability of the components and effective connection during construction. Specific parameters (such as connector specifications, tightening torque, installation location, etc.) need to be determined based on structural design specifications or construction drawings. With accurate component overlap data, the structural framework on the construction site is rationally constructed, forming a complete construction skeleton structure. Each connection point is subjected to load analysis to ensure structural stability during construction.

[0042] Step S163: performing component collision detection based on the building component overlap data to obtain component collision data; In this embodiment, after the construction site skeleton is assembled, the entire three-dimensional model is checked for collisions using the collision detection tool built into the BIM software. This collision detection tool automatically identifies areas of physical contact or overlap based on the geometric dimensions, connection point data, and installation sequence of building components. During the collision detection process, a detection threshold is first set—for example, a minimum safe clearance (e.g., 5 mm) is set to determine which components are colliding. Next, the BIM software scans the spatial relationships between all components, identifies any areas that do not meet the clearance requirements, and generates a collision report. Data for each collision point (such as the component number, location, and collision area) is recorded and serves as the basis for subsequent adjustments and optimization of the construction sequence. This collision data helps the engineering team precisely adjust the installation sequence and construction methods, avoiding construction delays and safety hazards caused by spatial conflicts.

[0043] Step S164: adjusting the construction sequence based on the number of component collisions to obtain construction sequence adjustment data; In this embodiment, the component collision data is generated by the collision detection in step S163, which contains all the conflicting components and their specific collision positions. In this step, according to the collision data, first, we need to analyze each colliding component and its impact range. If a collision occurs between two components and cannot be resolved by simple adjustment, we need to rearrange the installation order of these components. In specific operation, we can use construction scheduling software (such as Primavera, Microsoft Project, etc.) to sort the construction tasks by priority. For components that have collided, we adjust their installation order first, for example, install unaffected components first, and then install components that have collided. In addition, we can also reduce the occurrence of collisions by adjusting the lifting path or construction method, for example, changing the height or angle of the lifting path to avoid interference with other components. Construction sequence adjustment data will be generated based on these analyses and provided to the operators on the construction site to ensure that all components can be installed safely according to the new order.

[0044] Step S165: Virtual assembly of the building construction three-dimensional model according to the construction sequence adjustment data.

[0045] In this embodiment, virtual assembly is simulated by BIM software to verify that all building components can be installed smoothly. Using BIM modeling tools (such as Revit, Navisworks, etc.), according to the construction sequence adjustment data obtained in step S164, each component is placed in the model in the new order. During virtual assembly, BIM tools will automatically calculate the spatial position and connection method of each component to ensure that each component can smoothly transition along the predetermined route during installation. During virtual assembly, the connectivity and stability between components are also checked in real time, and the stress of each component is calculated through simulation to ensure the stability of the structure during construction. After virtual assembly is complete, BIM software will generate a detailed three-dimensional animation or model showing the entire construction process, which will be provided to construction personnel, supervisors, and engineers for further review and confirmation. This process not only provides precise guidance for actual construction, but also allows early identification of potential problems and timely adjustment of the construction plan.

[0046] Preferably, step S2 is specifically: Step S21: Building component transportation simulation according to the building construction three-dimensional model to obtain building component transportation data; In this embodiment, a 3D building construction model is obtained using BIM software (such as Revit or Navisworks). This model defines basic information such as the location, size, and weight of each building component. Next, specialized logistics simulation tools or transportation simulation modules are used to configure the transportation route, transportation tools (such as forklifts, cranes, or transport trucks), and component movement. The transportation route should be planned based on the actual construction site layout and on-site traffic conditions to ensure smooth transportation of each component. During the transportation simulation, factors such as transport vibration, sway, and vehicle acceleration must be considered, as these factors can potentially damage components. Through simulation calculations, transportation data for each building component is obtained, including transportation time, vibration levels, and pressure and stress during transportation. Transport vibration and stress values ​​can be simulated using sensor data or a physical vehicle model. During transportation, when values ​​reach a predetermined threshold (e.g., vibration acceleration exceeding 5 m / s²), they are automatically recorded and marked for subsequent analysis.

[0047] Step S22: Calculating transportation vibration values ​​based on the transportation data of the building components; obtaining the yield strength of the building components; and predicting fatigue areas of the building components based on the transportation vibration values ​​and the yield strength of the building components. In this embodiment, transportation vibration values ​​are typically obtained through statistical analysis of sensor data input into the simulation. Each building component encounters different vibration sources during transportation, such as road bumps and the movement of the transport vehicle. By setting vibration sensor positions on the vehicle during transportation, the collected vibration data is associated with the position information of each component, generating a vibration data record for the component throughout the entire transportation process. The statistical process uses acceleration sensor data (for example, with a vibration frequency range of 0-100 Hz and a maximum vibration acceleration of 5 m / s²) and analyzes it as a time series. These vibration values ​​are used for subsequent fatigue zone prediction. Secondly, after obtaining the transportation vibration data, the yield strength of the building component needs to be determined. This strength value is provided by the structural design data and is typically determined by material properties or test data. For example, if the building component is made of steel, the yield strength can be obtained based on standard material strength data; the yield strength of steel is typically 250-500 MPa. Based on this vibration data and yield strength, fatigue zones of the building component are predicted. By comparing the vibration with the yield strength, areas susceptible to fatigue damage are identified.

[0048] Step S23: extracting low-thickness building component data based on the building component transportation data; performing crack region prediction on the low-thickness building component data according to the transportation vibration value to obtain crack region data; In this embodiment, low-thickness components typically refer to thin-walled structures or thin plate components, which are susceptible to large stress concentrations during transportation, leading to cracks. First, using data from the transportation simulation, relevant information for all low-thickness components, including thickness, material, weight, etc., is extracted. Data on low-thickness components can be obtained from architectural design drawings and material libraries. Next, crack areas are predicted for these low-thickness components based on transportation vibration values. Crack prediction uses a stress analysis-based method that combines the effects of vibration with the material properties of the component. For example, finite element analysis (FEA) is used to calculate the stress and strain generated by the component during transportation. If the vibration value exceeds the tolerance range of the component material (e.g., the yield strength of steel is less than 300 MPa), cracks will occur in the corresponding area. This crack area data is marked in the 3D model for subsequent inspection and repair.

[0049] Step S24: performing component crack region intersection calculation based on the building component fatigue region and crack region data to obtain component crack region data; determining the component crack degree based on the component crack data; In this embodiment, the fatigue area data and crack area data in step S22 and step S23 are extracted. These data represent the crack damage areas suffered by each building component during transportation. Then, an intersection operation is performed on these crack areas. In the specific operation, a three-dimensional modeling tool is used to calculate the intersection of different areas based on the geometric shape and force analysis data of each component. For example, if the fatigue area and crack area of ​​the component overlap, then this part of the area is the area where cracking occurs. These intersection areas are displayed in the three-dimensional model using geometric operations, and a detailed data report of the crack area is generated, including information such as the specific location, area, and depth of the crack. These data are generated by the modeling tool and updated in real time to ensure that potential cracking problems can be discovered and repaired in a timely manner during the construction process.

[0050] Step S25: performing connection failure analysis based on the degree of component cracking to obtain component connection failure data; In this embodiment, based on the component cracking data obtained in step S24, the connection condition of each component is evaluated to determine whether cracking affects the reliability of the connection. Connection failure analysis calculates the force distribution in the connection area to determine whether the cracked area will cause failure at the connection. Specifically, based on the extent and location of the cracking, finite element analysis (FEA) is used to simulate the stresses in the connection area to determine whether the cracking will cause the stress at the connection to exceed the material strength. For example, if the cracked area is close to the connection point and the crack depth exceeds a certain percentage (for example, more than 50% of the material thickness), the connection will fail. In this case, the material's fracture toughness parameters and connection strength parameters (such as the ultimate tensile strength of steel) are used to calculate the risk of connection failure. Finally, connection failure data is generated, indicating which connection points have failed due to cracking, and corresponding repair or replacement plans are proposed.

[0051] Step S26: predicting steel bar corrosion risk based on component connection failure data; In this embodiment, component connection failure will lead to a reduction in the structural bearing capacity, which in turn affects the corrosion resistance of the steel bars. First, the component connection failure data is used to determine which component connections are at risk, and a corrosion risk assessment is performed on the contact surfaces of these components. The corrosion risk assessment uses environmental factors (such as humidity, temperature, salt spray concentration, etc.) and material corrosive parameters. Based on these data, corrosion models (such as the classic Faraday law) are used to predict the corrosion rate and risk of steel bars in specific environments. If there are large cracks or water leaks at the connection failure site, the risk of steel bar corrosion will increase significantly. By calculating the corrosion rate of steel bars under different environmental conditions and combining it with the actual environmental data on site, the corrosion risk prediction data of steel bars is obtained.

[0052] Step S27: Assessing component durability based on the steel bar corrosion risk.

[0053] In this embodiment, the durability of each component is assessed based on the rebar corrosion risk data obtained in step S26, combined with the component's service life and environmental load. Parameters used in the durability assessment include the corrosion rate of the rebar, load variations in the building components, and chemical reactions occurring during construction. Based on this data, a durability assessment model (such as the ISO 15686-1 durability standard) is used to calculate and predict the component's load-bearing capacity and durability over the next few years or decades. Ultimately, component durability assessment data is generated, including the estimated service life and maintenance intervals. This, combined with the construction schedule, allows for proactive maintenance and replacement decisions.

[0054] Preferably, step S25 is specifically as follows: Step S251: calibrating the component crack area based on the component crack degree; In this embodiment, the specific crack location and extent of each building component are extracted based on the crack data. By calculating the crack depth, length, and extension on the component surface and within, detailed calibration is performed using methods such as ultrasonic testing, X-ray imaging, or infrared thermal imaging. Ultrasonic testing determines the location and depth of cracks by the time difference between reflected waves, while X-ray imaging can more clearly identify cracks within the material. The crack data for each component is calibrated using the crack depth and length to ensure that the data for each cracked area is accurate. Generally, a crack depth exceeding 30% of the component thickness is considered a severely cracked area. The cracked areas are then displayed and updated in the 3D model based on the calibration data to ensure that the location and extent of the cracked areas match the actual situation.

[0055] Step S252: performing three-dimensional displacement detection on the cracked area of ​​the component to obtain three-dimensional displacement data of the component; In this embodiment, to detect the three-dimensional displacement of the component, high-precision displacement sensors or laser scanners are first installed around the cracked area. These sensors can obtain real-time displacement changes at each point on the component surface. The laser scanner captures the position coordinates of each point by emitting laser light and measuring the reflection time, providing very accurate displacement data. Displacement sensors are typically placed around the component to collect vibration or displacement data. Regular testing is performed, and the displacement value of each test is recorded. After data collection, the displacement data of each cracked area in three-dimensional space will be obtained, including the displacement in the X, Y, and Z directions. This data can help engineers monitor the stress and deformation of the component during crack development.

[0056] Step S253: Calculating a three-dimensional displacement value based on the component three-dimensional displacement data; In this embodiment, three-dimensional displacement data is obtained, which reflects the displacement of different position points of the component at different time points. By comparing the coordinate differences of the same position point at different times, the displacement of the position point can be calculated. Specifically, for each measurement point, the position data of the point at two different time points is taken, and its displacement changes in the X, Y, and Z directions are calculated. Then, these change values ​​are combined to obtain the overall displacement of the point. By performing this calculation on multiple measurement points, the displacement data of each point is obtained. These displacement values ​​will be calibrated as relative displacement, which means that they represent changes relative to the initial position. For different areas of the component, such as cracked areas, connection points, and parts with greater stress, the change in displacement is particularly important, so it is necessary to count the displacement data of each area separately. These statistical data help to determine whether the component has undergone excessive deformation during construction or use, and thus provide a basis for subsequent risk assessment and structural safety analysis.

[0057] Step S254: performing over-limit displacement determination on the three-dimensional displacement value according to a preset three-dimensional displacement threshold to obtain over-limit displacement data; In this embodiment, a specific three-dimensional displacement threshold is set. This threshold is determined based on the building component's material, structural type, and maximum allowable deformation. For example, for concrete structures, the displacement threshold is typically set at 0.2 mm (for steel structures, 0.5 mm). If the displacement exceeds this threshold, it is considered that excessive deformation has occurred at that location. To further improve the accuracy of the determination, the displacement threshold can be divided into different regions, such as cracked areas, connected areas, or areas with greater stress, with different thresholds set for each region. For example, if the displacement in a cracked area exceeds 0.2 mm and in a connected area exceeds 0.5 mm, excessive displacement data will be generated in these areas. By comparing the displacement of each point with these thresholds, excessive displacement data is generated, and the component areas experiencing excessive displacement are marked and summarized in a report for subsequent analysis.

[0058] Step S255: identifying the component connection mode based on the component crack area; In this embodiment, the connection method of the components is extracted based on the component information defined in the building construction design drawings or the three-dimensional model. The types of connection methods include different forms such as bolt connection, welding, and plug-in. By analyzing the three-dimensional model, the connection method and connection point position between the components are identified. According to the crack position of the component, it is determined whether the crack affects the connection area. For example, if the crack area is close to the connection point, special attention should be paid to whether the connection method can withstand the additional stress caused by the crack. By identifying the connection method and combining it with the crack area of ​​the component, the influence of the connection method can be further analyzed, especially whether the crack leads to failure of the connection.

[0059] Step S256: performing connection failure determination based on the component connection mode and the over-limit displacement data to obtain connection failure data.

[0060] In this embodiment, the connection method of each component is used in combination with the over-limit displacement data to analyze whether the connection part of each component has failed. Specifically, the stress conditions of the connection parts are simulated by finite element analysis (FEA), and combined with the cracking and displacement data, it is determined whether the connection point has caused stress concentration due to over-limit displacement. For example, if the displacement of the connection point exceeds the maximum displacement allowable value of the component design (for example, 0.5 mm), the connection fails. By calculating the stress, displacement and material strength of the connection part, it is determined whether there is a risk of connection failure. If the connection fails, the connection failure data is recorded, and the specific connection type and the location where the failure occurred are indicated. These failure data will be summarized in the subsequent construction monitoring system for reference when repairing and improving the construction plan.

[0061] Preferably, step S256 is specifically as follows: Identify the connection mode identification field according to the component connection mode, and obtain wet joint connection data and bolt connection data; In this embodiment, the connection method of each component is identified by analyzing the component connection design drawings and on-site installation data. Wet joint connection data primarily includes information such as the wet joint type, size, material, and design load of the connection area. Bolt connection data includes information such as the number, size, material, and installation status of the bolts. This data, obtained by analyzing design drawings and on-site inspection data, ensures the accurate identification of each connection method and connection location.

[0062] Calculate shear force based on wet joint connection data; Calculate tensile force based on wet joint connection data; In this embodiment, the shear force at the wet joint is determined using the shear force calculation formula using the geometric shape and material properties of the wet joint connection, combined with on-site load data. Specifically, the shear force is obtained by calculating the force area and load distribution at the wet joint, and the mechanical properties of the wet joint, such as shear strength, are taken into account to determine whether the shear force exceeds the connection design standard. The tensile force is calculated based on the tensile load borne by the wet joint, combined with the geometric dimensions of the wet joint, the material strength, and the mechanical environment of the component. The calculation of the tensile force takes into account the material properties of the wet joint and the stress distribution during construction, ensuring that accurate tensile force data is obtained through specific parameters such as the joint area, the material elastic modulus, etc.

[0063] Perform stress conversion analysis on shear force based on tensile force to obtain wet joint stress conversion data; In this example, the stress conversion relationship between tensile and shear forces is used to convert the effect of tensile force on wet joints into shear stress values ​​through a stress conversion model. This process requires the application of mechanical principles, such as the proportional relationship between shear and tensile forces, to derive wet joint stress conversion data based on actual joint conditions (such as joint stress distribution and material fatigue).

[0064] Stress intensification identification is performed on wet joint stress conversion data according to the over-limit displacement data to obtain wet joint stress intensification data; In this embodiment, displacements exceeding the safety range are identified by comparing the measured 3D displacement data with the designed displacement threshold. These excessive displacements are then combined with the stress conversion data of the wet joint to analyze their impact on wet joint stress, identify areas of increased stress, and generate wet joint stress intensification data. This process requires data acquisition from devices such as acceleration sensors, displacement sensors, and stress sensors, and judgment based on set thresholds.

[0065] Evaluate the degree of failure of wet joint connections based on wet joint stress intensification data; In this example, the load-bearing capacity and stability of wet joints are assessed based on the locations and values ​​of increased stress in the wet joints. Using standard failure models (such as finite element analysis models), the actual stress in the wet joints is compared with the critical stress of the material to determine the failure risk level of the wet joints. Calculations are performed to determine whether the wet joints are at potential risk of failure and to quantify the extent of the joint failure.

[0066] Identify the degree of bolt loosening based on bolt connection data; In this embodiment, bolt loosening is identified by checking the bolt torque and the gap between the bolt and the connecting plate. Using a torque wrench and a gap measuring instrument, the tightening status of each bolt is tested and the degree of looseness is calculated. If the degree of looseness exceeds a set threshold, the bolt is identified as loose and the specific location of the loosening is recorded.

[0067] Assess bolt wear based on bolt looseness; In this example, the tightening condition of loose bolts is analyzed and combined with a wear model to estimate the amount of bolt wear. Using specific wear assessment criteria, such as bolt wear rate and looseness, combined with field data, the overall degree of bolt wear is assessed, and the remaining service life and durability of the bolts are then estimated.

[0068] Evaluate the degree of failure of bolted connections based on the amount of bolt wear; In this embodiment, wear data, combined with the bolt connection's design strength and material fatigue standards, is used to assess the risk of bolted connection failure. The relationship between bolt wear and connection strength can be calculated using a mechanical model. If wear exceeds a predetermined standard, the failure level of the bolted connection is assessed, thereby determining whether the entire connection poses a safety hazard.

[0069] The failure degree of wet joint connection and the failure degree of bolt connection are integrated to obtain the connection failure data.

[0070] In this example, failure risk data for the entire assembly connection is derived by combining the failure levels of wet joints and bolted connections. This process involves weighting the failure levels of wet joints and bolted connections according to preset weight coefficients, ultimately generating comprehensive connection failure data for subsequent construction progress assessment and risk warning.

[0071] Preferably, step S26 is specifically as follows: Step S261: calibrating the failed component based on the component connection failure data to obtain failed component data; In this embodiment, connection failure data for each component is obtained, including the degree of failure of wet joints and bolted connections. This data is calculated through the previous steps and covers the stress intensification of wet joints and the degree of loosening and wear of bolts. By analyzing these failure data, the specific location and type of failed components are identified. Specifically, the failed component data should include the component ID, connection location, failure type (such as wet joint failure, loose bolts), failure degree (such as mild, moderate, severe), and failure time node. This data is obtained through a combination of on-site sensors, monitoring equipment, and manual inspection, and the failure status of the component is detailed and calibrated according to certain calibration standards, such as the component's design strength, material properties, load level, etc.

[0072] Step S262: Acquire corrosive medium data and perform preprocessing to obtain corrosive medium data to be processed; In this embodiment, corrosive medium data is acquired and preprocessed to obtain the corrosive medium data to be processed. Corrosive medium data typically originates from an environmental monitoring system, which collects information on factors such as humidity, temperature, air pollutants (such as sulfur dioxide and nitrogen oxides), and salt concentration. Preprocessing involves filtering, denoising, and standardizing the raw data to ensure accuracy and consistency. Specifically, sensors are used to monitor the temperature and humidity of each construction area, and chemical analysis methods are used to collect and analyze corrosive gases in the surrounding air to determine the medium concentration in different areas. Changes in the corrosive medium are recorded and stored according to a set time period (e.g., hourly or daily) to provide data support for subsequent corrosion simulations.

[0073] Step S263: performing steel bar corrosion simulation on the failed component data according to the corrosive medium data to be processed to obtain steel bar corrosion data; In this example, steel bar corrosion simulation is performed based on data from failed components, particularly the stress and corrosion environment in wet joints and bolted connection areas, combined with data from corrosive media. The simulation, based on a corrosion kinetics model, considers the influence of factors such as humidity, temperature, and pollutant concentration to calculate the extent of steel bar corrosion. By establishing a steel bar corrosion model, the corrosion rate and depth of steel bars under different environments are simulated. Combined with the actual construction environment, the corrosion profile of the steel bars in each failed component is determined, ultimately generating steel bar corrosion data. This process uses mathematical models, such as the Nernst equation or corrosion rate models, to calculate the steel bar corrosion rate based on environmental data and the steel bar exposure time.

[0074] Step S264: Calculating the steel bar corrosion rate based on the steel bar corrosion data; In this embodiment, the corrosion rate is calculated based on the corrosion depth and time of the steel bars under specific environmental conditions. First, by monitoring environmental data, the changes in environmental factors such as the concentration of the corrosive medium, humidity, and temperature are determined. This is then combined with the initial state of the steel bars (such as the surface treatment and material of the steel bars), and a corrosion rate model (such as a linear corrosion model or a nonlinear corrosion model) is used for calculation. By comparing the corrosion depth at different time points and combining it with the external environmental conditions, the annual corrosion rate of the steel bars is ultimately determined. The most common unit is millimeter per year (mm / year). For example, if the corrosion depth of the steel bars increases from 0.5mm to 0.8mm within a certain measurement period, the calculated corrosion rate is 0.3mm / year.

[0075] Step S265: Evaluate the steel bar failure life according to the steel bar corrosion rate; In this example, the calculated steel bar corrosion rate, combined with the initial condition of the steel bar (such as its diameter, material, and strength), is used to assess the steel bar's lifespan before failure. This assessment method uses a formula based on the design service life of the steel bar and its actual corrosion rate to calculate the expected failure time of the steel bar at the current corrosion rate. For example, if the design life of the steel bar is 50 years, the corrosion rate is 0.2 mm / year, and the minimum safe thickness of the steel bar is 5 mm, the remaining lifespan before failure can be calculated. If the current corrosion depth is 1 mm, the estimated remaining lifespan is 20 years.

[0076] Step S266: Estimate the corrosion risk based on the steel bar failure life.

[0077] In this example, a risk matrix approach is used to combine the failure life of the steel bars with the environmental corrosion level to generate corrosion risk assessment data for the structure. The final corrosion risk level is determined based on the degree of steel bar failure and corrosion rate, combined with construction design standards. The corrosion risk is estimated based on factors such as the steel bar corrosion rate, the degree of environmental pollution, and humidity and temperature fluctuations. This is then compared with the actual corrosion level in the construction area to produce a comprehensive corrosion risk rating. For example, if the steel bar has a short failure life and the environment is in a high-corrosion-risk area, the component's corrosion risk is considered high.

[0078] Preferably, step S3 is specifically as follows: Step S31: extracting the hoisting design data of the building construction design drawing; In this embodiment, building construction design drawings are obtained, including the dimensions, weight, hoisting position, and related hoisting design information of each component. The design drawings should include parameters such as the hoisting point, hoisting sequence, crane specifications, and crane operating radius for each component. This data can be imported into the system using digital drawing extraction tools (such as CAD software or BIM systems), automatically identifying hoisting-related data. The system needs to extract key data from the design drawings, including the crane operating radius, load distribution, lifting point height, and component geometric characteristics. A detailed analysis of the design drawings is performed to ensure that the extracted data is correct and meets on-site construction requirements.

[0079] Step S32: performing crane operation simulation according to the hoisting design data to obtain crane operation data; In this embodiment, based on the lifting design data extracted in step S31, a simulation is performed using lifting operation simulation software, taking into account the crane type, lifting path, operating radius, and operating range. This simulation software simulates the lifting process of the crane at different operating positions and angles, depending on the lifting task, and calculates the crane's movement trajectory, operating radius, and actual load. By setting specific crane parameters (such as maximum lifting capacity and operating range), the crane's operation is simulated to obtain crane operation data, including information such as the lifting path, operating load, and crane movement speed. This data can help evaluate the crane's operating status and provide basic data for subsequent steps.

[0080] Step S33: Calculating the hook swing amplitude based on the crane operation data; designing a stable lifting path according to the hook swing amplitude; In this embodiment, hook shaking is a phenomenon caused by the swing of the hoisting load during the crane operation, which affects the stability of the hoisting operation. First, the vertical and horizontal movement of the crane hook is calculated by analyzing the crane operation data. The crane operation data includes parameters such as crane load, crane speed, and hoisting height. Based on these parameters, the hook shaking amplitude is calculated by a dynamic mechanical model. Specifically, considering factors such as the crane's hoisting load, lifting height, and crane stability, the crane hook is simulated and calculated to obtain the hook shaking amplitude. By setting a reasonable threshold, the tolerance range of the hook shaking is determined. If the hook shaking amplitude exceeds the specified range, the hoisting path will be adjusted.

[0081] Step S34: Calculating the crane overload rate based on the crane operation data; allocating intelligent lifting tasks according to the crane overload rate; In this embodiment, the crane overload rate refers to the ratio of the actual load of the crane to its maximum rated load. Based on the crane operation data, the real-time load of the crane in different operating states is obtained, and the overload rate is calculated in combination with the maximum rated lifting capacity of the crane. The calculation process requires the collection of crane load data, which is usually monitored by a load sensor on the crane or a crane operation control system. The maximum load threshold of the crane is set in the system, and the actual overload rate is calculated based on the data sent back by the load sensor. For example, if the maximum lifting capacity of the crane is 10 tons and the current hoisting load is 8 tons, the overload rate is 80%. If the overload rate exceeds the set safety threshold, it is necessary to adjust the hoisting task or replace a stronger crane.

[0082] Step S35: Integrate the stable lifting path and the intelligent lifting task to obtain an optimized lifting path; In this embodiment, an optimal lifting path is calculated using an algorithmic model (such as a genetic algorithm or particle swarm optimization algorithm) combined with data such as the crane's load capacity, lifting path, crane speed, and workspace. During the optimization process, priority is given to minimizing hook swing and avoiding crane overload to ensure the stability and safety of crane operations. The lifting path is adjusted based on actual conditions to avoid collisions with the surrounding environment and ensure that each lifting operation is within the crane's safe load range. Ultimately, an optimized lifting path is obtained, providing guidance for crane operations.

[0083] Step S36: Obstacle identification is performed according to the optimized lifting path to obtain obstacle data.

[0084] In this embodiment, obstacle identification can be monitored in real time by devices such as laser radar, ultrasonic sensors, and cameras. By scanning the environment around the lifting path, existing obstacles such as buildings, other equipment, and operators are identified. The obstacle position data obtained by the sensor is combined with the optimization of the lifting path for real-time analysis. Based on the position and size of the obstacle and the operating range of the crane, it is determined whether the lifting path conflicts with the obstacle. If a conflict occurs, the system needs to adjust the lifting path in time to avoid the obstacle and ensure the smooth progress of the lifting operation. The identified obstacle data should include information such as the coordinates, type, size, and distance relative to the crane of the obstacle to provide decision support for subsequent operations.

[0085] Preferably, step S36 is specifically as follows: Step S361: performing path space area modeling according to the optimized lifting path, thereby obtaining a path space model; In this embodiment, the spatial information of the lifting path is extracted based on the optimized lifting path. This process requires obtaining the three-dimensional coordinate data of the lifting path, including the starting point, ending point, and passing points of the path. Using BIM technology and CAD system, a spatial area model of the path is created through the distance and relative position relationship between the path points. In the process of establishing the path space model, the spatial relationship between each path point is first calculated to determine the geometric shape of the path. According to the operating radius of the crane, the lifting path angle of rise and fall, and the size of the working space, a three-dimensional area model containing the path space is established. The boundary of the path space model is defined by the lifting height of the crane, the lifting radius, and the safety range to ensure that there are no obstacles in the path to hinder the lifting operation. In the model, a safety zone can be set to avoid the crane exceeding the specified operating range during lifting.

[0086] Step S362: Obtain the point cloud scanning data of the construction site obstacles; In this embodiment, the point cloud data of the obstacles is obtained by scanning the environment of the construction site using a LiDAR scanning device or a ground scanner. The point cloud scanning device generates three-dimensional data of the construction site by emitting laser and receiving reflected laser signals. The scanning process is usually divided into multiple scanning areas, and the point cloud data is collected step by step. The obtained point cloud data contains three-dimensional coordinate information of the obstacles, where the coordinate values (X, Y, Z) of each point represent the position of the obstacle surface. The accuracy of the point cloud data is affected by the resolution and scanning distance of the scanning device, and the commonly used resolution is within 10 centimeters per meter. The scanning data is usually filtered and cleaned to remove noise points and retain valid obstacle information, ensuring the accuracy and reliability of the data.

[0087] Step S363: Map the point cloud scanning data of the construction site obstacles to the path space model to obtain path space obstacle data; In this embodiment, the point cloud data is converted to the same coordinate system as the path space model. This conversion process needs to ensure that the coordinates of the point cloud data are consistent with the coordinate system of the path space model, usually using coordinate transformation technology for data matching. To achieve this, reference points or known coordinate points can be used as a reference to map the point cloud data into the coordinate system of the path space model. After processing, the mapped point cloud data can extract obstacle data that coincides or is close to the path space. These data include the position, size, and relative distance of the obstacles to the path space model, providing basic data for subsequent lifting operation collision detection.

[0088] Step S364: Perform lifting collision detection on the path space obstacle data according to the optimized lifting path to obtain lifting collision data; In this embodiment, the established path space model is used, and in combination with the path space obstacle data, collision detection is performed on the path of each lifting point. The collision detection adopts a detection method based on a spatial geometry algorithm. By calculating the spatial distance between the lifting path point and the obstacle data, it is determined whether there is an intersection or overlap. When the distance between certain points on the lifting path and the obstacle is less than the set safety distance threshold, it is determined that a collision has occurred. The collision detection process will traverse the entire lifting path and check the collision between all points on the path and the obstacle one by one. Each potential collision area in the lifting path will be marked, and the specific location of the collision and the severity of the collision will be recorded to obtain the lifting collision data.

[0089] Step S365: Obstacle identification is performed based on the hoisting collision data to obtain obstacle data.

[0090] In this embodiment, based on the hoisting collision data obtained in the previous step, the relationship between each collision area and the surrounding environment is analyzed to further identify the type and type of obstacles. Obstacles can be buildings, temporary facilities, transportation equipment, etc. at the construction site. Using data analysis technology, combined with the existing path space and obstacle data, the spatial attributes of each obstacle are determined. The data of each obstacle will include information such as its three-dimensional position, volume, type, shape, and orientation relative to the hoisting path. In addition, by comparing multiple scan data, it is possible to determine whether the obstacle is dynamic (such as the movement of construction equipment) or static (such as a building structure), and classify and mark it. Ultimately, the identified obstacle data will provide detailed information for optimizing the hoisting path and formulating a safe operation plan.

[0091] Preferably, step S4 is specifically as follows: Step S41: performing oil accumulation detection based on the obstacle data to obtain oil accumulation data; In this embodiment, obstacle data collected on-site is used to identify all obstacles within the construction area, particularly those areas where oil accumulation may occur, such as equipment, pipelines, or platform surfaces. High-precision sensors (such as lidar, infrared cameras, or ultrasonic sensors) are used to scan the construction area, and image processing techniques are used to analyze the surface to detect whether oil accumulation exists. For oil detection, specific reflectivity or heat change thresholds can be set to identify oil based on the reflectivity or thermal images of different surface materials. The degree of oil accumulation is positively correlated with the intensity of its reflected signal. The sensor analyzes the scanned surface reflection data to determine whether oil accumulation exists and records its location, area, and thickness, thereby obtaining oil accumulation data.

[0092] Step S42: calibrating the component oil stain position based on the oil stain accumulation data; In this embodiment, the oil accumulation data obtained in the first step is used to calibrate the coordinates and spatial position of each detected oil accumulation area. The boundaries and distribution of the oil accumulation areas are identified through lidar data or image data obtained by the camera, and the specific positions of these areas relative to the construction site components are determined. In this process, it is necessary to use spatial positioning technology to calibrate the position of the oil accumulation through a precise coordinate system to ensure that its position is accurate to the millimeter level. The oil accumulation data includes parameters such as the location, shape, area and accumulation height of the oil. Ultimately, the calibrated oil position will serve as the basic data for subsequent analysis.

[0093] Step S43: performing chemical composition analysis on the component oil stain location to obtain component oil stain composition data; In this embodiment, chemical composition analysis is performed on the component oil stain location to obtain component oil stain composition data. Based on the calibrated oil stain location, chemical analysis is performed using a chemical analysis instrument (such as an infrared spectrometer or X-ray fluorescence analyzer). The instrument analyzes the oil stain's spectral characteristics or chemical reaction properties to determine the primary components of the oil stain, such as petroleum, lubricants, solvents, and other chemical substances. The analysis process involves extracting an oil stain sample and performing chemical reagent reactions or spectral analysis to obtain detailed compositional data. During the analysis, the concentrations of specific components (such as moisture content, fatty acids, and metallic elements) are quantified, generating oil stain composition data that reflects the nature of the oil stain.

[0094] Step S44: Evaluate the component oil pollution aging based on the component oil pollution composition data; In this embodiment, the aging of the oil stain is evaluated based on the oil stain composition data obtained in step S43, combined with environmental factors (such as temperature, humidity, exposure time during construction, etc.). The aging of the oil stain is usually related to changes in the chemical composition of the oil stain, the oxidation process, and environmental influences (such as sunlight exposure and rain erosion). By comparing the oil stain composition data with the standard aging model, the aging state of the oil stain is calculated, and the aging degree and change trend of the oil stain are obtained. The aging evaluation of the oil stain can use a chemical reaction rate model or a corrosion model to analyze the changes in the oil stain in different time periods, and by comparing experimental data, a specific aging value is obtained. The aging evaluation criteria include indicators such as changes in the viscosity of the oil stain, loss of volatile substances, and changes in chemical composition.

[0095] Step S45: estimating the oil pollution diffusion trend based on the component oil pollution aging; calibrating the contaminated components according to the oil pollution diffusion trend to obtain component pollution data; In this embodiment, the diffusion trend of oil pollution is analyzed based on the aging data of oil pollution. By establishing an oil pollution diffusion model and combining it with the aging evaluation data, the diffusion range and rate of oil pollution in different time periods are predicted. The diffusion trend of oil pollution is affected by environmental factors (such as wind speed, humidity, temperature, etc.) and the type of oil pollution (such as oiliness, viscosity, etc.). Through numerical simulation or physical experiments, a mathematical model of oil pollution diffusion is established, and the diffusion range of oil pollution is predicted using this model. Over time, oil pollution will spread to the surrounding component surfaces. Based on the diffusion prediction, the contaminated components are calibrated to obtain component contamination data. This data includes information such as the location of the contaminated component, the degree of contamination, the diffusion range, and the time of contamination.

[0096] Step S46: Evaluate the construction delay progress based on the component contamination data and the obstacle data; In this embodiment, component contamination data and obstacle data are combined to analyze the impact of oil accumulation and diffusion on the construction process. The presence of contaminated components affects the construction progress, requiring cleaning, treatment, inspection, and other operations, delaying the original construction plan. By establishing a construction progress model, the impact time of oil contamination on each operation step is considered, and delays in construction tasks are evaluated. The calculation of delayed progress can be based on increased operating time, adjustments to personnel and equipment, and extended time for related safety inspections. The specific delay time will be adjusted according to the severity of the oil contamination, the construction stage, and on-site management measures.

[0097] Step S47: Upload the construction delay progress to the construction monitoring platform to perform the automatic construction monitoring task.

[0098] In this embodiment, the construction site's progress monitoring system is used to organize the assessed construction delay progress data and upload it to a construction monitoring platform in a predefined format. The platform receives and stores construction delay data, providing real-time updates on project progress. By analyzing the gap between delay data and the scheduled construction period, the platform implements intelligent management, helping project managers make real-time scheduling and decisions, allowing them to adjust construction plans or implement remedial measures in a timely manner. The uploaded data includes information such as delay type, duration, and impact, enabling personnel to make more accurate assessments of construction progress.

[0099] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0100] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatically monitoring the construction progress of an assembled building, characterized in that: The following steps are involved: Step S1: Obtain building construction design drawings; Extracting prefabricated building component design data based on building construction design; constructing a three-dimensional building construction model based on the prefabricated building component design data; Step S2: simulating the transportation of building components based on the three-dimensional building construction model to obtain building component transportation data; identifying the degree of component cracking based on the building component transportation data; and performing connection failure analysis based on the degree of component cracking to obtain component connection failure data. Predicting steel corrosion risk based on component connection failure data; Assess component durability based on reinforcement corrosion risk; Step S3: extracting the hoisting design data from the building construction design drawings; optimizing the hoisting path according to the hoisting design data to obtain an optimized hoisting path; Obstacle identification is performed based on the optimized lifting path to obtain obstacle data; Step S4: Perform component contamination analysis based on the obstacle data to obtain component contamination data; evaluate the construction delay progress based on the component contamination data and the obstacle data; and upload the construction delay progress to the construction monitoring platform to perform the automatic construction monitoring task.

2. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Obtaining building construction design drawings; Step S12: extracting prefabricated building component design data based on the building construction design; Step S13: Collect component codes according to prefabricated building component design data; Step S14: Classify component types based on component codes to obtain basic component data and connected component data; Step S15: defining a connection mode for the basic component data according to the connection component data to obtain connection mode data; Step S16: Constructing a three-dimensional building construction model based on the basic component data, the connection component data, and the connection method data.

3. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 2, characterized in that: Step S16 is specifically as follows: Step S161: Determine the construction site framework based on the basic component data; Step S162: lap-joining building components on the construction site skeleton based on the connection component data to obtain building component lap-joining data; Step S163: performing component collision detection based on the building component overlap data to obtain component collision data; Step S164: adjusting the construction sequence based on the number of component collisions to obtain construction sequence adjustment data; Step S165: adjusting the data according to the construction sequence to perform virtual assembly of the model to obtain a three-dimensional model of the building construction.

4. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: performing a building component transportation simulation based on the building construction three-dimensional model to obtain building component transportation data; Step S22: Calculating transportation vibration values ​​based on the building component transportation data; Obtain the yield strength of building components; predict the fatigue area of ​​building components based on transportation vibration values ​​and yield strength of building components; Step S23: extracting low-thickness building component data based on the building component transportation data; performing crack region prediction on the low-thickness building component data according to the transportation vibration value to obtain crack region data; Step S24: performing component crack area intersection calculation based on the building component fatigue area and crack area data to obtain component crack area data; Determining the degree of component cracking based on component cracking data; Step S25: performing connection failure analysis based on the degree of component cracking to obtain component connection failure data; Step S26: predicting steel bar corrosion risk based on component connection failure data; Step S27: Assessing component durability based on the steel bar corrosion risk.

5. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 4, characterized in that: Step S25 is specifically as follows: Step S251: calibrating the component crack area based on the component crack degree; Step S252: performing three-dimensional displacement detection on the cracked area of ​​the component to obtain three-dimensional displacement data of the component; Step S253: Calculating a three-dimensional displacement value based on the component three-dimensional displacement data; Step S254: performing over-limit displacement determination on the three-dimensional displacement value according to a preset three-dimensional displacement threshold to obtain over-limit displacement data; Step S255: identifying the component connection mode based on the component crack area; Step S256: performing connection failure determination based on the component connection mode and the over-limit displacement data to obtain connection failure data.

6. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 5, characterized in that: Step S256 is specifically as follows: Identify the connection mode identification field according to the component connection mode, and obtain wet joint connection data and bolt connection data; Calculate shear force based on wet joint connection data; calculate tensile force based on wet joint connection data; Perform stress conversion analysis on shear force based on tensile force to obtain wet joint stress conversion data; Stress intensification identification is performed on wet joint stress conversion data according to the over-limit displacement data to obtain wet joint stress intensification data; Evaluate the degree of failure of wet joint connections based on wet joint stress intensification data; Identify the degree of bolt loosening based on bolt connection data; Assess bolt wear based on bolt looseness; Evaluate the degree of failure of bolted connections based on the amount of bolt wear; The failure degree of wet joint connection and the failure degree of bolt connection are integrated to obtain the connection failure data.

7. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 4, characterized in that: Step S26 is specifically as follows: Step S261: calibrating the failed component based on the component connection failure data to obtain failed component data; Step S262: Acquire corrosive medium data and perform preprocessing to obtain corrosive medium data to be processed; Step S263: performing steel bar corrosion simulation on the failed component data according to the corrosive medium data to be processed to obtain steel bar corrosion data; Step S264: Calculating the steel bar corrosion rate based on the steel bar corrosion data; Step S265: Evaluate the steel bar failure life according to the steel bar corrosion rate; Step S266: Estimate the corrosion risk based on the steel bar failure life.

8. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting the hoisting design data of the building construction design drawing; Step S32: performing crane operation simulation according to the hoisting design data to obtain crane operation data; Step S33: Calculating the hook swing amplitude based on the crane operation data; Design a smooth lifting path based on the hook's swing amplitude; Step S34: Calculating the crane overload rate based on the crane operation data; allocating intelligent lifting tasks according to the crane overload rate; Step S35: Integrate the stable lifting path and the intelligent lifting task to obtain an optimized lifting path; Step S36: Obstacle identification is performed according to the optimized lifting path to obtain obstacle data.

9. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 8, characterized in that: Step S36 is specifically as follows: Step S361: performing path space area modeling according to the optimized lifting path, thereby obtaining a path space model; Step S362: Obtaining point cloud scanning data of obstacles at the construction site; Step S363: Mapping the construction site obstacle point cloud scanning data to the path space model to obtain path space obstacle data; Step S364: performing hoisting collision detection on the path space obstacle data according to the optimized hoisting path to obtain hoisting collision data; Step S365: Obstacle identification is performed based on the hoisting collision data to obtain obstacle data.

10. The method for automatically monitoring the construction progress of prefabricated buildings according to claim 1, wherein: Step S4 is specifically as follows: Step S41: performing oil accumulation detection based on the obstacle data to obtain oil accumulation data; Step S42: calibrating the component oil stain position based on the oil stain accumulation data; Step S43: performing chemical composition analysis on the component oil stain location to obtain component oil stain composition data; Step S44: Evaluate the component oil pollution aging based on the component oil pollution composition data; Step S45: estimating the oil pollution diffusion trend based on the component oil pollution aging; Calibrate the contaminated components according to the oil pollution diffusion trend to obtain component pollution data; Step S46: Evaluate the construction delay progress based on the component contamination data and the obstacle data; Step S47: Upload the construction delay progress to the construction monitoring platform to perform the automatic construction monitoring task.

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