Fabricated building component management method and system based on BIM

Abstract

The invention provides a BIM-based fabricated building component management method and system, and the method comprises the steps: firstly obtaining a building information model data set of a target fabricated building project, a component attribute parameter set covering a prefabricated component unit, and a component geometric feature set, and then carrying out the multi-level component feature analysis of the building information model data set, obtaining a standardized geometric feature sequence and a dynamic construction state feature set containing a transportation positioning offset, a hoisting stress distribution gradient and an installation collision risk coefficient, and then performing cross-stage collaborative analysis on the features based on a preset construction stage matching strategy to generate a component matching strategy set; and the component dynamic optimization module is used for indicating component installation sequence adjustment and connection node precision compensation, finally generating a component dynamic optimization instruction set according to the component matching strategy set, and synchronizing the component dynamic optimization instruction set to the building information model data set to update component attributes, so that efficient management of the fabricated building components is realized.

Description

Technical Field

[0001] The present invention relates to the field of BIM technology. Specifically, it relates to a method and system for managing prefabricated building components based on BIM. Background Art

[0002] At present, with the booming development of prefabricated buildings, the demand for refined and intelligent component management is becoming increasingly urgent. However, there are obvious shortcomings in the existing technologies.

[0003] When the existing technologies obtain information on prefabricated building projects, the data integration is not comprehensive. Most of them only focus on the basic geometric dimensions of components, such as key attribute parameters like component material type, dimensional error threshold, and positioning coordinates of connection nodes. A complete data set has not been formed, which affects the accuracy of subsequent construction management decisions and increases construction risks and costs. In terms of component feature analysis, the existing methods are mostly single-stage or single-dimensional analyses, and cannot deeply analyze the multi-level features of components during the entire construction period. It is difficult to accurately obtain dynamic information such as the positioning offset during the component transportation stage, the stress distribution gradient during the component hoisting stage, and the collision risk coefficient during the component installation stage, which is not conducive to discovering potential construction problems in advance and taking countermeasures, and affects construction efficiency and quality.

[0004] In terms of formulating component matching strategies, the existing technologies lack a cross-stage collaborative analysis mechanism. They often determine strategies based on experience and partial information, without comprehensively considering the mutual influence of components in different construction stages, resulting in unreasonable component installation sequences and insufficient connection node accuracy, reducing the structural safety and service function of prefabricated buildings. Moreover, the existing technologies lack the ability of dynamic optimization and iterative update. After determining the management plan, it is difficult to adjust and optimize in a timely manner according to the actual construction situation, and cannot meet the requirements of complex and changeable construction sites, easily causing construction delays and quality problems. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, embodiments of the present invention provide a method for managing prefabricated building components based on BIM. The method includes: Obtain a building information model data set of a target prefabricated building project. The building information model data set includes a component attribute parameter set and a component geometric feature set of multiple prefabricated component units. The component attribute parameter set includes component material type, component dimensional error threshold, and component connection node positioning coordinates; Perform multi-level component feature analysis on the building information model data set to obtain a standardized geometric feature sequence and a dynamic construction state feature set for each prefabricated component unit. Among them, the dynamic construction state feature set includes the positioning offset during the component transportation stage, the stress distribution gradient during the component hoisting stage, and the collision risk coefficient during the component installation stage; Based on a preset construction stage matching strategy, perform cross-stage collaborative analysis and processing on the standardized geometric feature sequence and the dynamic construction state feature set to generate a component matching strategy set for the precast component unit, where the component matching strategy set is used to indicate the component installation sequence adjustment plan and the connection node accuracy compensation parameters; Generate a component dynamic optimization instruction set according to the component matching strategy set, and synchronize the component dynamic optimization instruction set to the building information model data set to trigger the component attribute iterative update operation.

[0006] On the other hand, an embodiment of the present invention further provides a BIM-based prefabricated building component management system, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0007] Based on the above aspects, the embodiments of the present invention significantly improve the systematicness, accuracy and coordination of prefabricated building component management. Specifically, by obtaining the building information model data set including the component attribute parameter set and the geometric feature set, the comprehensive integration of key information such as the material type, dimension error threshold and connection node positioning coordinates of the precast component unit is realized. On this basis, the multi-level component feature analysis technology converts the original data into a standardized geometric feature sequence and a dynamic construction state feature set, which not only reveals the physical characteristics and state changes of the component in different construction stages, but also introduces dynamic parameters such as the positioning offset in the transportation stage, the stress distribution gradient in the hoisting stage and the collision risk coefficient in the installation stage. Further, based on the preset construction stage matching strategy, the cross-stage collaborative analysis and processing of the standardized geometric feature sequence and the dynamic construction state feature set are realized, so that the generation of the component matching strategy set can comprehensively consider the performance and mutual influence of the components during the entire construction cycle, and thus formulate a more scientific and reasonable component installation sequence adjustment plan and connection node accuracy compensation parameters. These component matching strategies not only optimize the component installation process and reduce construction risks, but also improve the accuracy and stability of component connection through the setting of accuracy compensation parameters, thereby improving the overall quality of the prefabricated building. Finally, by generating a component dynamic optimization instruction set according to the component matching strategy set and synchronizing the instruction set to the building information model data set to trigger the component attribute iterative update operation, the closed-loop control and continuous optimization of the component management process are realized, ensuring that the component management strategy can be adjusted in real time with the progress of the construction and the change of on-site conditions, so as to maintain the effectiveness and adaptability of the component management strategy. Description of the Drawings

[0008] Figure 1It is a schematic diagram of the execution process of the BIM-based prefabricated building component management method provided by an embodiment of the present invention.

[0009] Figure 2 It is a schematic diagram of exemplary hardware and software components of the BIM-based prefabricated building component management system provided by an embodiment of the present invention. Detailed implementation manners

[0010] The present invention will be specifically described below with reference to the accompanying drawings of the specification. Figure 1 It is a schematic diagram of the process of the BIM-based prefabricated building component management method provided by an embodiment of the present invention. The BIM-based prefabricated building component management method will be introduced in detail below.

[0011] Step S110: Obtain the building information model data set of the target prefabricated building project. The building information model data set includes the component attribute parameter sets and component geometric feature sets of multiple prefabricated component units. The component attribute parameter sets include component material types, component size error thresholds, and component connection node positioning coordinates.

[0012] In this embodiment, taking a prefabricated residential building project as an example, which includes multiple residential buildings and supporting auxiliary facilities. In the early stage of the project, it is necessary to obtain the building information model data set of the target prefabricated building project from the building information model (BIM) file provided by the design unit. The building information model data set is the core carrier of the entire project data, covering the detailed information of multiple prefabricated component units in the project.

[0013] For the component attribute parameter set, taking the precast stair component as an example, its component material type is determined by design to be reinforced concrete, which has high strength and durability and can meet the use requirements of the stairs. The component size error threshold is set to ensure the accuracy of the component during production and installation. For this precast stair, its length size error threshold is set to plus or minus 5 millimeters, the width size error threshold is set to plus or minus 3 millimeters, and the height size error threshold is set to plus or minus 2 millimeters. The component connection node positioning coordinates clarify the installation position of this precast stair in the entire building structure. Assuming that its connection node positioning coordinates are represented in three-dimensional space as X coordinate of 20 meters, Y coordinate of 15 meters, and Z coordinate of 3 meters, this means that the connection node of this precast stair needs to be accurately installed at this position to ensure accurate connection with other components and the stability of the building structure.

[0014] The geometric feature set of components describes the specific shape and geometric dimensions of precast components. Still taking the precast staircase as an example, its geometric feature set includes the number of steps of the staircase, the height and width of each step, the overall length and width of the staircase, the shape and dimensions of the handrail, etc. For example, this precast staircase has a total of 18 steps, the height of each step is 150 mm, the width is 300 mm, the overall length of the staircase is 6 m, the width is 1.2 m, and the diameter of the handrail is 50 mm. These detailed geometric feature information is crucial for the production, transportation and installation of components.

[0015] Step S111: Perform data preprocessing operations on the original building information model. The data preprocessing operations include component attribute denoising processing, geometric feature topology verification processing, and spatial coordinate system alignment processing.

[0016] Next, since there may be problems such as inaccurate, incomplete data or inconsistent coordinate systems in the original building information model, it is necessary to perform data preprocessing operations on it. In this large-scale prefabricated residential building project, the data preprocessing operations are divided into three main steps, namely component attribute denoising processing, geometric feature topology verification processing, and spatial coordinate system alignment processing.

[0017] Step S1111: In the data preprocessing operation, when performing component attribute denoising processing, traverse each precast component unit in the original building information model, and identify the abnormal parameter items in the component attribute parameter set. The abnormal parameter items include the component material type outside the preset material strength range, the dimension measurement value exceeding the component size error threshold, and the connection node positioning coordinate deviating from the design coordinate range.

[0018] When performing component attribute denoising processing, it is necessary to traverse each precast component unit in the original building information model one by one. Taking the precast balcony component in this prefabricated residential building project as an example, the preset material strength range is determined according to the design requirements. Suppose the design requirement for this precast balcony is to use concrete with a strength grade of C35, and its corresponding material strength range has clear standard values. If it is found during the traversal that the component material type of this precast balcony is marked as concrete with a strength grade of C20, this belongs to the abnormal parameter item of the component material type outside the preset material strength range. Because the strength of C20 concrete is lower than the C35 concrete strength required by the design, using this material may affect the bearing capacity and safety of the balcony.

[0019] For the dimensional measurement values, the error threshold for the length dimension of the precast balcony is set to plus or minus 8 mm, and the error threshold for the width dimension is set to plus or minus 5 mm. During the actual measurement or data recording process, if it is found that the actual length of the precast balcony is 12 mm longer than the designed length, exceeding the error threshold of plus or minus 8 mm, or the actual width is 7 mm narrower than the designed width, exceeding the error threshold of plus or minus 5 mm, then these dimensional measurement values that exceed the component dimension error threshold belong to the abnormal parameter items.

[0020] There are also strict design ranges for the connection node positioning coordinates. Suppose the designed connection node positioning coordinates of the precast balcony are between 12 m and 12.2 m for the X coordinate, between 8 m and 8.1 m for the Y coordinate, and between 4 m and 4.1 m for the Z coordinate. If during the traversal process, it is found that the X coordinate of the connection node positioning coordinates of the precast balcony is 12.5 m, exceeding the design range, or the Y coordinate is 7.9 m, also not within the design range, or the Z coordinate is 4.2 m, also deviating from the design coordinate range, then these connection node positioning coordinates that deviate from the design coordinate range belong to the abnormal parameter items. Through such a detailed traversal and identification process, all abnormal parameter items in the component attribute parameter set can be found.

[0021] Step S1112: Based on the preset abnormal replacement rules, replace the abnormal parameter items with the standard parameter items in the component attribute parameter set to generate a denoised component attribute parameter set.

[0022] After identifying the abnormal parameter items in the component attribute parameter set, replacement operations need to be performed according to the preset abnormal replacement rules. For the abnormal parameter items of the above precast balcony, assume that the preset abnormal replacement rule is that when there is a component material type that exceeds the preset material strength range, it is directly replaced with the standard material type required by the design; when there is a dimensional measurement value that exceeds the component dimension error threshold, it is replaced with the design dimension as the standard; when there is a connection node positioning coordinate that deviates from the design coordinate range, it is replaced with the coordinate value specified by the design.

[0023] For the abnormal material type of the precast balcony marked as C20 concrete, it is replaced with C35 concrete required by the design according to the rule. For the dimensional measurement value whose length exceeds the error threshold, the actually measured length value is replaced with the designed length value; for the dimensional measurement value whose width exceeds the error threshold, it is also replaced with the designed width value. For the connection node positioning coordinate that deviates from the design coordinate range, its X coordinate is replaced with the standard value within the design range, such as 12.1 m; the Y coordinate is replaced with 8.05 m; the Z coordinate is replaced with 4.05 m. Through such replacement operations, a denoised component attribute parameter set is generated to ensure the accuracy and reliability of the component attribute parameters.

[0024] Step S1113: When performing geometric feature topology verification processing, call the geometric constraint verification algorithm to verify the surface continuity error, corner alignment degree, and hole integrity in the component geometric feature set. If it is detected that the surface continuity error exceeds the first preset threshold, the corner alignment degree is lower than the second preset threshold, or the hole integrity does not meet the preset closed condition, generate a geometric repair instruction and reconstruct the component geometric feature set.

[0025] Taking the precast curved wall component in this prefabricated residential building project as an example, for the surface continuity error, the first preset threshold is set to 0.05 radians. During the production process, due to the influence of processes or other factors, the surface of the curved wall may be discontinuous. By calculating the curvature change between adjacent points on the surface of the curved wall through the geometric constraint verification algorithm, if it is found that the curvature change of a certain section of the surface causes the surface continuity error to reach 0.08 radians, exceeding the first preset threshold of 0.05 radians, this indicates that there is a problem with the surface continuity of the curved wall.

[0026] For the corner alignment degree, the second preset threshold is set to 98%. The corners of the curved wall need to be accurately aligned with other components to ensure the integrity and sealing of the building structure. During actual detection, by measuring the alignment of the corners and calculating the alignment ratio. If it is found that the alignment ratio of a certain corner is only 95%, lower than the second preset threshold of 98%, it means that the corner alignment degree does not meet the requirements.

[0027] For the hole integrity, the preset closed condition requires that the edges of the holes must be continuous and closed, without cracks or gaps. When detecting the installation holes reserved on the curved wall, it is found that there is a crack with a length of 5 mm at the edge of a certain hole, not meeting the preset closed condition.

[0028] When it is detected that the above-mentioned surface continuity error exceeds the first preset threshold, the corner alignment degree is lower than the second preset threshold, or the hole integrity does not meet the preset closed condition, the system will generate a geometric repair instruction. For the surface continuity problem, it may be necessary to grind and trim the surface of the curved wall to make its curvature change smoother to reduce the surface continuity error; for the corner alignment degree problem, it may be necessary to finely adjust or reprocess the corners to increase the alignment ratio; for the hole integrity problem, it is necessary to repair the crack at the edge of the hole to make it meet the closed condition. After completing these repair operations, reconstruct the component geometric feature set of the precast curved wall to ensure that its geometric features meet the design requirements.

[0029] Step S1114: When performing the spatial coordinate system alignment process, according to the reference coordinate system of the global positioning system at the construction site, perform translation transformation and rotation transformation on the local coordinate system in the geometric feature set of the components, so that the spatial offset between the local coordinate systems of all precast component units and the reference coordinate system is less than the third preset threshold, and obtain the building information model data set.

[0030] When performing the spatial coordinate system alignment process, it is necessary to adjust the local coordinate system in the geometric feature set of the components according to the reference coordinate system of the global positioning system at the construction site. Taking the precast roof truss component in this prefabricated residential building project as an example, each precast roof truss component has its own local coordinate system, which is used to describe its own geometric features and position. The reference coordinate system of the global positioning system at the construction site is the unified coordinate system of the entire project, and the installation positions of all components are determined based on this reference coordinate system.

[0031] Suppose the third preset threshold is set such that the spatial offset in the X, Y, and Z directions does not exceed 100 millimeters. In actual operation, first measure the initial offsets of the origin of the local coordinate system of the precast roof truss component in the X, Y, and Z directions from the origin of the reference coordinate system. For example, it is measured that the origin of the local coordinate system of this precast roof truss component is offset by 150 millimeters in the X direction, 80 millimeters in the Y direction, and 120 millimeters in the Z direction from the origin of the reference coordinate system. In order to make the spatial offset between the local coordinate system and the reference coordinate system less than the third preset threshold, it is necessary to perform translation transformation and rotation transformation on the local coordinate system.

[0032] Translation transformation is to move the local coordinate system in the X, Y, and Z directions so that its origin is as close as possible to the origin of the reference coordinate system. For the above-mentioned precast roof truss component, move its local coordinate system 150 millimeters in the negative direction in the X direction, 80 millimeters in the positive direction in the Y direction, and 120 millimeters in the negative direction in the Z direction. After the translation transformation, rotation transformation may also be required to adjust the direction of the local coordinate system to make it consistent with the direction of the reference coordinate system. By continuously adjusting the parameters of translation and rotation, finally, the spatial offset between the local coordinate system of this precast roof truss component and the reference coordinate system is less than 100 millimeters in the X, Y, and Z directions. After performing such spatial coordinate system alignment processing on all precast component units in the project, an accurate building information model data set is obtained, providing a reliable data basis for subsequent construction management.

[0033] Step S120: Perform multi-level component feature analysis on the building information model data set to obtain the standardized geometric feature sequence and dynamic construction state feature set of each precast component unit. Among them, the dynamic construction state feature set includes the positioning offset during component transportation, the stress distribution gradient during component hoisting, and the collision risk coefficient during component installation.

[0034] After obtaining the accurate building information model data set, it is necessary to perform multi-level component feature analysis on it to obtain the standardized geometric feature sequence and dynamic construction state feature set of each precast component unit. Still taking this large-scale prefabricated residential building project as an example, multi-level component feature analysis will deeply excavate the feature information of each precast component unit.

[0035] Step S121: Extract the surface mesh data, internal support structure data, and connection node geometric data from the component geometric feature set, and perform curvature homogenization processing on the surface mesh data to generate standardized surface mesh features.

[0036] Taking the precast exterior wall panel component as an example, the surface mesh data describes the shape and topology of the exterior wall panel surface, which is composed of a series of mesh points and the edges connecting these mesh points. The internal support structure data records the dimensions, positions, and connection methods of the support beams, columns, etc. inside the exterior wall panel. The connection node geometric data details the geometric shapes and dimensions of the connection parts between the exterior wall panel and other components.

[0037] In the actual production process, there may be some areas on the surface of the exterior wall panel with relatively large local curvature changes, which may affect the appearance quality and installation effect of the exterior wall panel. In order to generate standardized surface mesh features, it is necessary to adjust these curvature changes. For example, by calculating the curvature value of each point on the surface mesh, the areas with large curvature changes are found. Suppose in the surface mesh of a certain precast exterior wall panel, it is found that there is an area where the curvature value is much larger than that of the surrounding area, which may lead to poor fitting with adjacent components during installation. By slightly adjusting the mesh points in this area, the curvature value of this area gradually approaches that of the surrounding area, thus achieving curvature homogenization. After such processing, the generated standardized surface mesh features can better reflect the ideal surface shape of the precast exterior wall panel, providing a more accurate basis for subsequent analysis and construction.

[0038] Step S122: Perform cross-sectional moment of inertia calculation and load transfer path analysis on the internal support structure data to obtain the static mechanical distribution characteristics of the precast component unit.

[0039] Taking a precast floor slab component as an example, the moment of inertia of the cross-section is an important indicator to measure the ability of the component to resist bending deformation. When calculating the moment of inertia of the cross-section, detailed calculations need to be carried out according to the specific shape and size of the internal support structure of the floor slab. Suppose there are primary and secondary beam structures inside the precast floor slab. The cross-sectional shape of the primary beam is rectangular, with a width of 300 mm and a height of 500 mm; the cross-sectional shape of the secondary beam is also rectangular, with a width of 200 mm and a height of 350 mm. By corresponding calculation methods, the moments of inertia of the cross-sections of the primary beam and the secondary beam are calculated respectively.

[0040] The analysis of the load transfer path is to determine how the loads on the floor slab are transferred to the foundation through the internal support structure. In this precast floor slab, when the floor slab bears loads such as personnel and equipment, the loads are first transferred to the secondary beams, then the secondary beams transfer the loads to the primary beams, and finally the primary beams transfer the loads to the columns and the foundation. By analyzing the load transfer path, the stress conditions of each support structure under the action of the loads can be understood, so as to obtain the static mechanical distribution characteristics of the precast component unit. For example, through analysis, it can be known which parts of the primary beam are under greater stress and which parts are under less stress under a certain specific load, which is very important for evaluating the safety and reliability of the component.

[0041] Step S123: Conduct a tolerance fit analysis on the geometric data of the connection nodes, calculate the difference between the theoretical gap and the actual gap between the connection nodes, and generate the node fit error characteristics.

[0042] Taking the connection node between the precast column and the beam as an example, in the design stage, the theoretical gap between the connection nodes will be determined according to the structural requirements and installation process. Suppose the designed theoretical gap between the connection node of the precast column and the beam is 10 mm. However, in the actual production and installation process, due to factors such as manufacturing errors and installation accuracy, the actual gap between the connection nodes may be different from the theoretical gap.

[0043] The actual gap of this connection node is measured to be 12 mm through actual measurement. Then the difference between the theoretical gap and the actual gap is 2 mm, and this difference is the node fit error. Such tolerance fit analysis is carried out on the connection nodes of all precast component units, and the node fit error of each connection node is calculated, so as to generate the node fit error characteristics. The node fit error characteristics can reflect the actual installation quality of the connection nodes, and are of great significance for evaluating the reliability of the component connection and the overall performance of the structure.

[0044] Step S124: Integrate the standardized surface mesh characteristics, the static mechanical distribution characteristics, and the node fit error characteristics to generate the standardized geometric feature sequence.

[0045] Taking the precast exterior wall panel, precast floor slab, and precast column-beam connection node as an example, the standardized surface grid features describe the surface shape and quality of the component, the static mechanical distribution features reflect the mechanical properties and stress conditions of the component, and the node fitting error features reflect the accuracy of component connection. These three features are spliced according to the set order and rules to form a complete standardized geometric feature sequence. For example, first arrange the data of the standardized surface grid features in the front, followed by the data of the static mechanical distribution features, and finally the data of the node fitting error features, thus generating the standardized geometric feature sequence of each precast component unit. This standardized geometric feature sequence can comprehensively describe the geometric and mechanical features of the precast component unit.

[0046] Step S125: Real-time collect the set of sensor monitoring data at the construction site, where the set of sensor monitoring data includes transportation vehicle positioning data, lifting equipment stress data, and point cloud scanning data during the installation stage.

[0047] During the construction process, it is necessary to real-time collect the set of sensor monitoring data at the construction site. For this prefabricated residential building project, the set of sensor monitoring data includes data in multiple aspects. The transportation vehicle positioning data is obtained in real-time through positioning sensors installed on the vehicles transporting precast components. For example, each truck transporting precast components is installed with a Global Positioning System (GPS) sensor, and these sensors record the vehicle's position information, including longitude, latitude, and altitude, every 10 seconds. Through these positioning data, the driving route and position of the transportation vehicle can be understood in real-time to determine whether the vehicle is driving along the predetermined route.

[0048] The lifting equipment stress data is collected through stress sensors installed at key parts of the lifting equipment. Taking the tower crane as an example, stress sensors are installed on parts such as the boom and tower body of the crane, and these sensors can monitor the stress changes of the lifting equipment during operation in real-time. When the crane lifts a precast component, the stress sensors will record the stress values borne by the boom and tower body, as well as the stress change trend. These data are very important for evaluating the safety and stability of the lifting equipment.

[0049] The point cloud scanning data during the installation stage is obtained by scanning the construction site with a three-dimensional laser scanner during the component installation stage. During the installation of precast components, a three-dimensional laser scanner is used to scan the installation site to obtain the three-dimensional point cloud data of the site. These point cloud data contain information such as the terrain of the construction site, the position and attitude of the installed components, etc. For example, when installing a precast staircase, a three-dimensional laser scanner is used to scan the installation position of the staircase to obtain the point cloud data of this position. By analyzing these data, the actual situation of the staircase installation can be understood, such as whether it is installed in place and the relative position relationship with surrounding components.

[0050] Step S126: Perform Kalman filtering on the positioning data of the transport vehicle, extract the real-time position offset of the precast component unit during the transportation stage, and calculate its cumulative offset distance from the designed path.

[0051] Kalman filtering is an optimal algorithm for estimating the state of a system. It can filter the measurement data containing noise to obtain a more accurate state estimate value. When processing the positioning data of a transport vehicle, due to certain errors and noise in GPS sensor measurements, Kalman filtering can reduce the impact of these errors and obtain more accurate vehicle position information.

[0052] Taking the vehicle transporting precast floor slabs as an example, assume that the designed driving route of the vehicle is a specified road from the precast component production plant to the construction site. During transportation, the real-time position offset of the precast floor slab during the transportation stage is extracted from the positioning data processed by Kalman filtering. Specifically, the real-time position offset refers to the deviation between the actual position of the vehicle and the corresponding position on the designed route. Assume that the designed route is a straight line. At a certain moment, through Kalman filtering, the actual longitude of the vehicle is 116.5005 degrees east longitude and the latitude is 39.9010 degrees north latitude, while the longitude corresponding to this moment on the designed route is 116.5000 degrees east longitude and the latitude is 39.9005 degrees north latitude. To calculate the offset, the difference in longitude and latitude needs to be converted into an actual distance. Usually, the geographic coordinate conversion formula can be used to multiply the difference in longitude and latitude by the corresponding coefficient to obtain the offset distance in the X direction (east-west direction) and the Y direction (north-south direction). Assume that in this area, the east-west distance corresponding to a difference of 0.0001 degrees in longitude is about 11 meters, and the north-south distance corresponding to a difference of 0.0001 degrees in latitude is about 11.1 meters. Then the offset in the X direction is (116.5005 - 116.5000) × 10000 × 11 = 55 meters, and the offset in the Y direction is (39.9010 - 39.9005) × 10000 × 11.1 = 55.5 meters. In this way, the real-time position offset of the precast floor slab during the transportation stage at this moment is obtained.

[0053] Next, calculate its cumulative offset distance from the designed path. The cumulative offset distance refers to the total offset distance between the actual driving route of the vehicle and the designed route from the start of transportation to the current moment. To calculate the cumulative offset distance, it is necessary to record the real-time position offsets at each moment and accumulate them. Suppose that in the first 10 moments after the start of transportation, the real-time position offsets at each moment in the X direction are 10 meters, 12 meters, 8 meters, 15 meters, 9 meters, 11 meters, 13 meters, 7 meters, 14 meters, and 16 meters respectively, and in the Y direction are 11 meters, 13 meters, 9 meters, 16 meters, 10 meters, 12 meters, 14 meters, 8 meters, 15 meters, and 17 meters respectively. When calculating the cumulative offset distance, first calculate the cumulative offsets in the X direction and the Y direction separately. The cumulative offset in the X direction is 10 + 12 + 8 + 15 + 9 + 11 + 13 + 7 + 14 + 16 = 125 meters, and the cumulative offset in the Y direction is 11 + 13 + 9 + 16 + 10 + 12 + 14 + 8 + 15 + 17 = 135 meters. Then, according to the Pythagorean theorem, the cumulative offset distance is equal to the square root of the sum of the square of the cumulative offset in the X direction and the square of the cumulative offset in the Y direction. That is, first calculate the square of 125 to be 15625, the square of 135 to be 18225, the sum of the two is 33850, and then take the square root of 33850 (an approximate value can be obtained using a calculator and other tools) to get the cumulative offset distance of approximately 183.98 meters. In this way, the extraction of the real-time position offset of the precast floor slab during the transportation stage and the calculation of the cumulative offset distance from the designed path are completed.

[0054] Step S127: Perform frequency-domain analysis on the stress data of the hoisting equipment, extract the amplitude peak value and frequency distribution characteristics in the direction of the principal stress, and generate the stress distribution gradient at the component hoisting stage in combination with the weight distribution parameters of the precast component unit.

[0055] When performing frequency-domain analysis on the stress data of the hoisting equipment, first, it is necessary to understand that the purpose of frequency-domain analysis is to convert the stress data from the time domain to the frequency domain to better analyze the frequency components of the stress signal. Taking the hoisting of a precast steel beam as an example, during the hoisting process, a stress sensor installed on the hoisting equipment will collect stress data in real time. Suppose 1000 stress data points are collected within a period of time, and these data are stress values that change with time. To perform frequency-domain analysis, the fast Fourier transform (FFT) algorithm can be used to convert these time-domain data into frequency-domain data.

[0056] After obtaining the frequency-domain data, it is necessary to extract the amplitude peak value and frequency distribution characteristics in the direction of the principal stress. The direction of the principal stress refers to the direction in which the hoisting equipment bears the maximum stress during the hoisting process. By analyzing the frequency-domain data, the amplitude peak value corresponding to the frequency in the direction of the principal stress can be found. Suppose that after frequency-domain analysis, it is found that the amplitude peak value in the direction of the principal stress appears at a frequency of 5 Hz, and its amplitude value is 200 kPa. The frequency distribution characteristics describe the distribution of the stress signal at different frequencies. The frequency-domain data can be divided into multiple frequency intervals, and the amplitude values within each interval are statistically analyzed to obtain the frequency distribution characteristics. For example, the frequency range from 0 to 10 Hz is divided into 5 intervals, and the total amplitude within each interval is statistically analyzed to obtain the following results: the total amplitude in the 0-2 Hz interval is 50 kPa, the total amplitude in the 2-4 Hz interval is 80 kPa, the total amplitude in the 4-6 Hz interval is 120 kPa, the total amplitude in the 6-8 Hz interval is 60 kPa, and the total amplitude in the 8-10 Hz interval is 30 kPa.

[0057] Combined with the weight distribution parameters of the precast component unit to generate the stress distribution gradient during the component hoisting stage. The weight distribution parameters of the precast steel beam include the total weight of the steel beam, the distribution of the weight in the length direction, etc. Suppose the total weight of the precast steel beam is 50 tons, and its weight is evenly distributed in the length direction. According to the mechanical principle, the stresses borne by different positions of the steel beam during hoisting are different. The stress is greater at the position close to the hoisting point and smaller at the position far from the hoisting point. By comprehensively considering the amplitude peak value in the direction of the principal stress, the frequency distribution characteristics, and the weight distribution parameters of the steel beam, the stress values at different positions of the steel beam during the hoisting stage can be calculated. For example, near the two hoisting points at both ends of the steel beam, the calculated stress value is 300 kPa, while at the middle position of the steel beam, the stress value is 150 kPa. Then, based on the stress values at these different positions, the stress distribution gradient is calculated. The stress distribution gradient represents the rate of change of stress with position. Suppose the length of the steel beam is 20 m, and the distance from one hoisting point to the middle position is 10 m, and the stress changes from 300 kPa to 150 kPa, then the stress distribution gradient for this section is (300 - 150) ÷ 10 = 15 kPa / m. Through such calculations, the stress distribution gradient of the entire precast steel beam during the hoisting stage can be obtained.

[0058] Step S128: Perform three-dimensional registration processing on the point cloud scanning data in the installation stage, compare the overlapping degree of the scanned point cloud with the designed geometric model in the building information model data set, calculate the volume ratio and spatial distance of the non-overlapping area, and generate the collision risk coefficient for the component installation stage.

[0059] Taking the installation of precast stairs as an example, 3D laser scanners were used at the installation site to obtain the point cloud scan data of the stair installation location. At the same time, the design geometric model of the precast stairs is included in the building information model data set. The purpose of 3D registration processing is to align the scanned point cloud with the design geometric model in space for subsequent overlap comparison.

[0060] Step S1281: Extract the feature point set in the design geometric model, where the feature point set includes the center points of connection nodes and the corner points of component contours.

[0061] First, extract the feature point set in the design geometric model. For the design geometric model of precast stairs, the feature point set includes the center points of connection nodes and the corner points of component contours. The center point of a connection node refers to the central position of the connection part between the stairs and other components, such as the node center where the stairs are connected to the floor slab. Suppose the precast stairs have two connection nodes, and their three-dimensional coordinates in the design geometric model are (X1, Y1, Z1) and (X2, Y2, Z2) respectively. The corner points of component contours refer to the positions of the corners of the stair contour, such as the four corner points of the stair tread. Suppose the stair tread is rectangular, and its four corner points have three-dimensional coordinates of (X3, Y3, Z3), (X4, Y4, Z4), (X5, Y5, Z5), and (X6, Y6, Z6) respectively. Combining these center points of connection nodes and corner points of component contours gives the feature point set of the design geometric model.

[0062] Step S1282: Perform downsampling processing on the point cloud scan data in the installation stage to generate a downsampled point cloud set, and calculate the curvature distribution parameters of each point in the downsampled point cloud set.

[0063] Since the original point cloud data usually contains a large number of points and the data volume is large, in order to improve the processing efficiency, downsampling processing is required. There are many methods for downsampling processing, such as the voxel grid filtering method. Suppose the original point cloud data contains 10,000 points. Through the voxel grid filtering method, the point cloud data is divided into small voxel grids, and only one point is retained in each voxel grid. After downsampling processing, the obtained downsampled point cloud set contains 2,000 points.

[0064] Next, calculate the curvature distribution parameters of each point in the downsampled point cloud set. Curvature is a parameter that describes the degree of bending of the point cloud surface. For each point in the downsampled point cloud set, the curvature of the point can be obtained by calculating the distribution of points in its neighborhood. For example, for a certain point P, select the points within a neighborhood with a radius of 0.1 meter around it. By analyzing the spatial distribution of these points, the curvature value of point P can be calculated. Such calculations are performed for all points in the downsampled point cloud set, and the curvature distribution parameters of each point in the downsampled point cloud set are obtained.

[0065] Step S1283: Based on the curvature distribution parameters of the feature point set and the downsampled point cloud set, perform a rough point cloud registration operation to generate an initial transformation matrix.

[0066] The purpose of rough point cloud registration is to roughly align the scanned point cloud with the designed geometric model. By comparing the curvature distribution parameters of the feature point set and the downsampled point cloud set, the corresponding relationship between the two is found. For example, find the points in the feature point set with curvature values similar to those of a certain point in the downsampled point cloud set, and use them as corresponding points. Suppose 10 points are found in the feature point set corresponding to 10 points in the downsampled point cloud set. Based on these corresponding points, an initial transformation matrix can be calculated using methods such as the least squares method. The initial transformation matrix contains rotation and translation information, and is used to perform preliminary rotation and translation on the downsampled point cloud set to roughly align it with the designed geometric model. Suppose the initial transformation matrix obtained through calculation is a 4×4 matrix, which contains information such as rotation angles and translation distances in the X, Y, and Z directions.

[0067] Step S1284: Optimize the rotation and translation parameters of the initial transformation matrix through the Iterative Closest Point (ICP) algorithm to generate a fine registration transformation matrix.

[0068] Optimize the rotation and translation parameters of the initial transformation matrix through the Iterative Closest Point (ICP) algorithm. The ICP algorithm is a commonly used point cloud fine registration algorithm, which finds the best match between the scanned point cloud and the designed geometric model through continuous iteration. In each iteration, first find the closest point of each point in the downsampled point cloud set in the designed geometric model, and then calculate a new transformation matrix based on these closest point pairs to update the initial transformation matrix. After multiple iterations, until the change in the transformation matrix is less than a preset threshold. Suppose after 10 iterations, the change in the transformation matrix is less than the preset threshold of 0.001. At this time, the obtained transformation matrix is the fine registration transformation matrix. The fine registration transformation matrix can more accurately align the downsampled point cloud set with the designed geometric model.

[0069] Step S1285: Apply the fine registration transformation matrix to the downsampled point cloud set to generate a registered point cloud set that is spatially aligned with the designed geometric model.

[0070] Specifically, it is to transform the coordinates of each point in the downsampled point cloud set through the fine registration transformation matrix. Suppose the coordinate of a certain point in the downsampled point cloud set is (X, Y, Z), and the fine registration transformation matrix is a 4×4 matrix. Expand the coordinate of this point to homogeneous coordinates (X, Y, Z, 1), and then multiply it by the fine registration transformation matrix to obtain the transformed coordinate (X', Y', Z'). Perform such transformations on all points in the downsampled point cloud set to generate a registered point cloud set that is spatially aligned with the designed geometric model.

[0071] Step S1286: Calculate the volume ratio of the non-overlapping region between the registered point cloud set and the designed geometric model to the total volume of the designed geometric model, and extract the minimum Euclidean distance from each point in the non-overlapping region to the surface of the designed geometric model.

[0072] The method of three-dimensional space segmentation can be used to divide the space where the designed geometric model and the registered point cloud set are located into small cube units. For each cube unit, determine whether it is completely inside the designed geometric model, completely inside the point cloud, partially overlapping, or completely non-overlapping. Statistically sum up the total volume of the non-overlapping cube units and the total volume of the cube units occupied by the designed geometric model. Suppose the total volume of the cube units occupied by the designed geometric model is 10 cubic meters, and the total volume of the non-overlapping cube units is 1 cubic meter. Then the volume ratio of the non-overlapping region to the total volume of the designed geometric model is 1÷10 = 0.1.

[0073] Extract the minimum Euclidean distance from each point in the non-overlapping region to the surface of the designed geometric model. For each point in the non-overlapping region, calculate its Euclidean distance to all points on the surface of the designed geometric model, and then take the minimum value as the minimum Euclidean distance from this point to the surface of the designed geometric model. Suppose there are 100 points in the non-overlapping region, and such calculations are performed for each point to obtain 100 minimum Euclidean distance values.

[0074] Step S1287: Generate the collision risk coefficient for the component installation stage based on the weighted summation result of the volume ratio and the minimum Euclidean distance.

[0075] Suppose the weight of the volume ratio is 0.6 and the weight of the minimum Euclidean distance is 0.4. First, normalize the minimum Euclidean distance values so that their value range is between 0 and 1. Suppose the average value of the normalized minimum Euclidean distance is 0.2 and the volume ratio is 0.1. Then the collision risk coefficient is 0.6×0.1 + 0.4×0.2 = 0.14. Through such calculations, the collision risk coefficient of the precast staircase in the installation stage is obtained, and this coefficient can be used to evaluate the likelihood of collisions occurring during the installation process.

[0076] Step S129: Combine the real-time position offset, the stress distribution gradient in the component hoisting stage, and the collision risk coefficient in the component installation stage into the dynamic construction state feature set.

[0077] For components such as precast floor slabs, precast steel beams, and precast stairs, splice their real-time position offsets during the transportation stage (such as the offsets in the X and Y directions calculated as above), the stress distribution gradients during the hoisting stage (such as the stress distribution gradients at different positions of the steel beam), and the collision risk coefficients during the installation stage (such as the collision risk coefficient of 0.14 for the stairs) in a set order. For example, first arrange the data of the real-time position offsets in the front, followed by the data of the stress distribution gradients of the component during the hoisting stage, and finally the data of the collision risk coefficients of the component during the installation stage, thus forming the dynamic construction state feature set of each precast component unit. This dynamic construction state feature set can comprehensively reflect the real-time state of the precast component during the construction process and provide an important basis for subsequent construction management decisions.

[0078] Step S130: Based on a preset construction stage matching strategy, perform cross-stage collaborative analysis and processing on the standardized geometric feature sequence and the dynamic construction state feature set to generate a component matching strategy set for the precast component unit, where the component matching strategy set is used to indicate the component installation sequence adjustment plan and the connection node precision compensation parameters.

[0079] Taking this large-scale prefabricated residential building project as an example, different construction stages have different requirements and risks, and it is necessary to comprehensively consider the standardized geometric feature sequence and the dynamic construction state feature set to formulate a reasonable component matching strategy.

[0080] Step S131: Construct a construction stage matching strategy library, where the construction stage matching strategy library includes a transportation stage path optimization strategy, a hoisting stage stress balance strategy, and an installation stage collision avoidance strategy.

[0081] The construction stage matching strategy library includes various strategies for different construction stages. The transportation stage path optimization strategy aims to ensure that precast components can be transported to the construction site safely and efficiently. The hoisting stage stress balance strategy is used to ensure the safety of components and hoisting equipment during hoisting and avoid damage caused by stress concentration. The installation stage collision avoidance strategy is to prevent collisions during component installation and ensure the installation quality and construction progress.

[0082] Step S1311: Collect the construction data set of historical prefabricated building projects, where the construction data set includes the optimized path parameters, stress balance records, and collision avoidance plans of successful cases.

[0083] Collect the construction data sets of historical prefabricated building projects. These data sets are important bases for constructing the matching strategy library in the construction stage. Through the analysis of multiple historical projects, some successful experiences and strategies can be summarized. For example, the construction data of 10 historical prefabricated building projects are collected, which include the optimized path parameters for transporting precast components in each project, such as the driving time and traffic conditions of different routes; the stress balance records in the hoisting stage, such as the stress changes of different components during hoisting and the stress balance measures taken; and the collision avoidance plans in the installation stage, such as how to adjust the installation sequence and perform node precision compensation.

[0084] Step S1312: Conduct cluster analysis on the optimized path parameters of the successful cases, extract the frequently occurring path correction patterns, and encode them into the rule set of the path optimization strategy in the transportation stage.

[0085] Conduct cluster analysis on the optimized path parameters of the successful cases. Cluster analysis is a method of grouping data objects into multiple classes or clusters, so that the objects in the same cluster have high similarity, while the objects in different clusters have large differences. In this process, the transportation distance, estimated driving time, road conditions (such as congestion level, road condition quality, etc.) are used as the characteristic parameters for clustering.

[0086] Suppose that among the 10 historical projects collected, a total of 50 different transportation path parameters are recorded. First, preprocess these path parameters, standardize different characteristic parameters so that they have the same dimension and value range for subsequent clustering calculations. For example, the value range of the transportation distance may be from a few hundred meters to dozens of kilometers, while the estimated driving time may be from a few minutes to several hours. Through standardization, they are unified into the range of 0 to 1.

[0087] Adopt the common K-means clustering algorithm for clustering operations. Here, it is assumed that these paths are divided into 3 clusters (K = 3). At the beginning of the algorithm, randomly select 3 path parameters as the initial clustering centers. Then, calculate the similarity between each path parameter and these 3 clustering centers (usually measured by the Euclidean distance), and assign each path to the cluster where the clustering center with the highest similarity is located. Next, recalculate the center of each cluster as the new clustering center. Continuously repeat this process until the clustering center no longer changes significantly or reaches the preset number of iterations.

[0088] After cluster analysis, 3 clusters are obtained. Each cluster is analyzed in detail to extract the frequently occurring path correction patterns. For example, in a certain cluster, it is found that when encountering road congestion, the frequently occurring path correction pattern is to select a detour route that is slightly farther but has better road conditions and less traffic flow. Such patterns are recorded and summarized in detail, including the triggering conditions (such as the congestion level reaching the set threshold), the correction methods (specific detour routes), etc.

[0089] Encode these frequently occurring path correction patterns into a rule set for the path optimization strategy in the transportation stage. The rule set adopts a clear and explicit logical structure. For example, "If the congestion level of the current path exceeds 80%, then select Route B for detour". Through such a rule set, in the actual transportation process, path adjustment decisions can be quickly made based on real-time road condition information, improving transportation efficiency and reducing transportation risks.

[0090] Step S1313: Conduct a regression analysis on the stress balance records, establish a fitting relationship between the stress distribution gradient and the number of temporary support points configured, and generate a decision tree for the stress balance strategy in the hoisting stage.

[0091] Conduct a regression analysis on the collected stress balance records with the aim of finding the quantitative relationship between the stress distribution gradient and the number of temporary support points configured. Assume that in the stress balance records of historical projects, there are stress distribution gradient data and corresponding numbers of temporarily supported points configured during the hoisting of 20 different components. The stress distribution gradient data can be obtained by real-time collection through stress sensors installed during the hoisting process, while the number of temporarily supported points configured is determined according to the actual hoisting plan.

[0092] First, clean and preprocess these data to remove outliers and missing values. Then, select a suitable regression model. Here, a linear regression model is taken as an example. The linear regression model attempts to find a linear equation to describe the relationship between the stress distribution gradient (denoted as Y) and the number of temporarily supported points configured (denoted as X), that is, Y = aX + b, where a and b are coefficients to be determined.

[0093] Use the least squares method to estimate the values of coefficients a and b. The principle of the least squares method is to minimize the sum of the squares of the distances from all data points to the regression line. By calculating for 20 data points, the specific values of coefficients a and b are obtained. For example, after calculation, a = 0.5 and b = 10, then the obtained regression equation is Y = 0.5X + 10. This means that the stress distribution gradient increases linearly with the increase in the number of temporarily supported points configured. For each additional temporarily supported point, the stress distribution gradient increases by approximately 0.5.

[0094] According to the obtained fitting relationship, a decision tree for the stress balance strategy in the hoisting stage is generated. A decision tree is a model for making decisions based on a tree structure. Each internal node is a test on a feature, each branch is a test output, and each leaf node is a class or value. In this scenario, the root node of the decision tree is the stress distribution gradient, and branches are made according to different value ranges of the stress distribution gradient. For example, if the stress distribution gradient is less than 20, then there is no need to add temporary support points; if the stress distribution gradient is between 20 and 50, then it is recommended to add 1 temporary support point; if the stress distribution gradient is greater than 50, then it is recommended to add 2 temporary support points. Through such a decision tree, during the actual hoisting process, the number of temporary support points to be configured can be quickly determined according to the real-time monitored stress distribution gradient, ensuring stress balance during the hoisting process and improving hoisting safety.

[0095] Step S1314: Conduct association rule mining on the collision avoidance plan, identify the causal relationship chain between the adjustment of the installation sequence and the decrease in the collision risk coefficient, and construct an inference graph of the collision avoidance strategy in the installation stage.

[0096] Association rule mining is a method of discovering interesting association relationships from a large amount of data. In this scenario, the focus is on the relationship between the adjustment of the installation sequence and the decrease in the collision risk coefficient. Suppose in the collision avoidance plans of historical projects, the adjustment situations of the installation sequence and the corresponding changes in the collision risk coefficient when installing 30 different components are recorded.

[0097] First, encode the adjustment situations of the installation sequence and the changes in the collision risk coefficient and convert them into a data format suitable for association rule mining. For example, the adjustment of the installation sequence can be encoded as "sequence before adjustment - sequence after adjustment", and the change in the collision risk coefficient can be encoded as "decrease" or "not decreased".

[0098] Use the Apriori algorithm for association rule mining. The Apriori algorithm is a classic association rule mining algorithm. It uses an iterative method of searching layer by layer, starting from a single item set and continuously generating larger item sets until no larger frequent item sets can be generated. During this process, two thresholds, the minimum support and the minimum confidence, are set. The minimum support indicates that the frequency of an item set in the dataset must reach a set proportion, and the minimum confidence indicates that the credibility of an association rule must reach a set degree.

[0099] Through the Apriori algorithm, the association rules between the adjustment of the installation sequence and the decrease in the collision risk coefficient are mined. For example, it is found that when the installation sequence is adjusted from "A - B - C" to "B - A - C", the support degree of the decrease in the collision risk coefficient is 80%, and the confidence degree is 90%. This means that this adjustment of the installation sequence occurs in 80% of the cases, and in these cases, there is a 90% probability that the collision risk coefficient will decrease.

[0100] According to the mined association rules, the causal relationship chain between the adjustment of the installation sequence and the decrease in the collision risk coefficient is identified. For example, further analysis reveals that when there is an overlap in the installation space of component A and component B, installing component B first can avoid collision with component A during installation, thereby reducing the collision risk coefficient.

[0101] Based on these causal relationship chains, an inference graph of the collision avoidance strategy in the installation stage is constructed. The inference graph is a visual graphical structure that shows the causal relationships and inference processes between different factors. In this inference graph, the nodes represent the installation sequence adjustment schemes and the collision risk coefficients, and the edges represent the causal relationships between them. For example, an edge points from the node of "adjusting the installation sequence from A - B - C to B - A - C" to the node of "decrease in the collision risk coefficient", and the weight of the edge can be set as the confidence degree of the association rule, which is 90%. Through this inference graph, during the actual installation process, according to the real - time collision risk coefficient and the installation situation of the components, the appropriate installation sequence adjustment scheme can be quickly inferred to effectively avoid the collision risk.

[0102] Step S1315: Store the rule set, decision tree, and inference graph into the construction stage matching strategy library, and configure dynamic weight coefficients to adapt to the priorities of different construction scenarios.

[0103] Store the rule set of the generated path optimization strategy in the transportation stage, the decision tree of the stress balance strategy in the hoisting stage, and the inference graph of the collision avoidance strategy in the installation stage into the construction stage matching strategy library. The construction stage matching strategy library can be stored in the form of a database for convenient subsequent query and use.

[0104] Configure dynamic weight coefficients to adapt to the priorities of different construction scenarios. Different construction scenarios have different requirements for the transportation, hoisting, and installation stages. Therefore, different weights need to be assigned to the strategies of each stage. For example, according to the environmental parameters of the current construction scenario, such as weather conditions, equipment availability, and artificial skill levels, set the initial weights for the transportation stage, hoisting stage, and installation stage.

[0105] For example, under harsh weather conditions such as heavy rain, the safety and reliability during the transportation stage become particularly important. At this time, the weight of the path optimization strategy for the transportation stage can be set to 0.6, the weight of the stress balance strategy for the hoisting stage can be set to 0.2, and the weight of the collision avoidance strategy for the installation stage can be set to 0.2. In the case of low equipment availability, the smooth progress of the hoisting stage may be affected. At this time, the weight of the stress balance strategy for the hoisting stage can be increased to 0.5, the weight of the path optimization strategy for the transportation stage can be set to 0.3, and the weight of the collision avoidance strategy for the installation stage can be set to 0.2.

[0106] Real-time monitor the execution effects of the component matching strategy set. The execution effects include the offset reduction rate after path correction, the gradient descent rate after stress balance, and the overlap improvement rate after collision avoidance. For example, during the transportation stage, record the offset distances before and after path correction and calculate the offset reduction rate; during the hoisting stage, record the stress distribution gradients before and after stress balance and calculate the gradient descent rate; during the installation stage, record the component overlap degrees before and after collision avoidance and calculate the overlap improvement rate.

[0107] Based on the deviation degree between the execution effect and the expected target, dynamically adjust the dynamic weight coefficient. If the offset reduction rate during the transportation stage does not reach the expected target, it indicates that the effect of the path optimization strategy for the transportation stage is not good, and the weight of the path optimization strategy for the transportation stage can be appropriately increased; if the gradient descent rate during the hoisting stage reaches the expected target, it indicates that the effect of the stress balance strategy for the hoisting stage is good, and its weight can be appropriately reduced. Feed the adjusted dynamic weight coefficient back to the construction stage matching strategy library to optimize the decision-making tendency of subsequent collaborative analysis and processing, so that the construction management can flexibly adjust the strategy according to the actual situation and improve the construction efficiency and quality.

[0108] Step S132: For the path optimization strategy of the transportation stage, dynamically adjust the driving path of the transport vehicle according to the real-time position offset and the cumulative offset distance, and mark the risk offset area in the building information model data set to generate a path replanning instruction.

[0109] Taking the vehicle transporting precast wall panels as an example, the real-time position offset and the cumulative offset distance are calculated through the real-time collected positioning data of the transport vehicle.

[0110] Suppose during the transportation process, the real-time position offset shows that the vehicle has offset 30 meters in the X direction and 20 meters in the Y direction, and the cumulative offset distance has reached 50 meters. According to the rule set of the path optimization strategy for the transportation stage, if the cumulative offset distance exceeds the preset threshold of 40 meters, path adjustment is required.

[0111] At this time, the system will dynamically adjust the driving route of the transport vehicle according to the real-time road condition information and the route correction modes in the rule set. For example, if there is a serious congestion on the route where the current vehicle is driving, and the rule set stipulates that a detour route can be selected in this case. The system will query the map database, find a detour route that is slightly farther but has better road conditions and less traffic flow, and send the route information to the navigation system of the transport vehicle.

[0112] At the same time, mark the risk deviation area in the building information model data set. The risk deviation area refers to the area where the vehicle deviating from the designed route may cause safety risks or affect the construction progress. According to the real-time position deviation and the cumulative deviation distance, determine the range where the vehicle currently deviates from the designed route, and mark this area in the building information model data set in a specific color or marking method. For example, mark the risk deviation area as red so that the construction management personnel can intuitively understand the transportation situation of the vehicle.

[0113] Generate a path re-planning instruction, and the instruction content includes the new driving route information, the estimated arrival time, etc. Send the path re-planning instruction to the driver of the transport vehicle and the construction management terminal. The driver can adjust the driving route according to the instruction, and the construction management personnel can monitor the transportation status of the vehicle in real time.

[0114] Step S133: For the stress balance strategy in the hoisting stage, analyze the non-uniformity of the stress distribution gradient in the component hoisting stage. If it is detected that the gradient value of the stress concentration area exceeds the fourth preset threshold, calculate the number and position of the additional temporary support points required, and generate a stress compensation instruction.

[0115] Taking the hoisting of a large precast roof truss as an example, stress data is collected in real time through stress sensors installed on the hoisting equipment and components, and the stress distribution gradient in the component hoisting stage is calculated.

[0116] The non-uniformity of the stress distribution gradient is manifested as different stress change rates at different positions of the component, and there may be areas of stress concentration. Analyze the collected stress distribution gradient data and calculate the stress gradient value at each position. Assume that the fourth preset threshold is set at 30 kPa / m. During the analysis, it is found that the stress gradient value at a certain corner position of the roof truss reaches 35 kPa / m, exceeding the fourth preset threshold, indicating that there is a stress concentration in this area.

[0117] According to the decision tree of the stress balance strategy in the hoisting stage, calculate the number and position of the additional temporary support points required. It can be seen from the decision tree that when the stress distribution gradient value is between 30 and 50 kPa / m, it is recommended to add 1 temporary support point. Combining the structural characteristics and stress distribution of the roof truss, determine the position of the temporary support point as a position that is relatively close to the stress concentration area and has a relatively stable structure, such as on the adjacent beam of the roof truss.

[0118] Generate stress compensation instructions, the instruction content includes the number of temporary support points to be added, the specific positions, and the installation requirements of the support points, etc. Send the stress compensation instructions to the hoisting operation personnel, and the operation personnel can install temporary support points at the specified positions according to the instructions to balance the stress distribution of the components during hoisting and improve the safety of hoisting.

[0119] Step S134: For the collision avoidance strategy in the installation stage, according to the magnitude of the collision risk coefficient of the component in the installation stage, prioritize the non-overlapping areas. If the volume ratio of the non-overlapping area exceeds the fifth preset threshold or the spatial distance exceeds the sixth preset threshold, generate an installation sequence adjustment instruction and node precision compensation parameters.

[0120] Taking the installation of a precast elevator shaft as an example, through the processing of the point cloud scanning data in the installation stage, the collision risk coefficient of the component in the installation stage, as well as the volume ratio and spatial distance of the non-overlapping areas are obtained.

[0121] Assume that the fifth preset threshold is set to 0.2 and the sixth preset threshold is set to 0.5 meters. During the analysis, it is found that there are multiple non-overlapping areas between the elevator shaft and the surrounding structures. Calculate the volume ratio and spatial distance of each non-overlapping area. Prioritize these non-overlapping areas according to the collision risk coefficient from high to low.

[0122] If the volume ratio of a certain non-overlapping area reaches 0.25, exceeding the fifth preset threshold, or the spatial distance from a certain point in this non-overlapping area to the surrounding structures reaches 0.6 meters, exceeding the sixth preset threshold, it indicates that there is a high collision risk in this area. At this time, according to the inference graph of the collision avoidance strategy in the installation stage, generate an installation sequence adjustment instruction and node precision compensation parameters.

[0123] The installation sequence adjustment instruction may advance or postpone the installation sequence of this elevator shaft to avoid collision with the surrounding structures. The node precision compensation parameters are to ensure the connection precision between the elevator shaft and the surrounding structures, such as adjusting parameters such as the bolt tightening torque and gasket thickness of the connection nodes. Send the installation sequence adjustment instruction and node precision compensation parameters to the installation operation personnel, and the operation personnel can adjust the installation sequence and perform node precision compensation according to the instructions to reduce the collision risk in the installation stage.

[0124] Step S135: Integrate the path replanning instruction, stress compensation instruction, and installation sequence adjustment instruction into the component matching strategy set.

[0125] For each precast component unit, summarize the corresponding path replanning instruction in the transportation stage, stress compensation instruction in the hoisting stage, and installation sequence adjustment instruction in the installation stage.

[0126] For example, for precast wall panels, the path re-planning instruction includes the new driving route and the estimated arrival time; the stress compensation instruction includes the number and location of temporary support points to be added; the installation sequence adjustment instruction includes the adjusted installation sequence and the node precision compensation parameters. These instructions are sorted according to the set format to form a complete set of component matching strategies. The set of component matching strategies can be stored and managed in the form of electronic documents or database records, which is convenient for construction management personnel to query and use, and is used to indicate the installation sequence adjustment plan of components and the connection node precision compensation parameters to ensure the smooth progress of the construction process of the entire prefabricated building project.

[0127] Step S140: Generate a component dynamic optimization instruction set according to the set of component matching strategies.

[0128] After obtaining the set of component matching strategies, it is necessary to generate a component dynamic optimization instruction set according to it. The component dynamic optimization instruction set is a further refinement and concretization of the set of component matching strategies, and includes detailed adjustment information on component attributes and construction processes to achieve dynamic optimization management of prefabricated building components.

[0129] Step S141: Analyze the risk offset area coordinates in the path re-planning instruction, associate and mark the key control points after the transportation path is modified in the building information model data set, and update the expected arrival time parameter in the component attribute parameter set.

[0130] Analyze the risk offset area coordinates in the path re-planning instruction. Taking the vehicle transporting precast beams as an example, the path re-planning instruction includes the coordinate information of the risk offset area, such as the coordinate range of the boundary of this area in three-dimensional space. By analyzing this coordinate information, the position of the risk offset area can be accurately determined.

[0131] Associate and mark the key control points after the transportation path is modified in the building information model data set. After the transportation path is modified, there will be some key turning points and control points, which are very important for accurately grasping the transportation route and monitoring the vehicle position. In the building information model data set, these key control points are marked with specific marks or symbols, such as represented by small blue circles. At the same time, establish the association relationship between these key control points and the transportation path, so that construction management personnel can intuitively view the changes in the transportation path in the building information model.

[0132] Update the expected arrival time parameter in the component attribute parameter set. According to the new transportation route and the vehicle's driving speed, recalculate the expected arrival time of the precast beam. Suppose the original expected arrival time was 10:00 am. Due to the route adjustment, the new estimated driving time has increased by 30 minutes. Then update the expected arrival time in the component attribute parameter set to 10:30 am. This can timely and accurately reflect the transportation status of the component and provide a basis for subsequent construction arrangements.

[0133] Step S142: Parse the position of the temporary support points in the stress compensation instruction, insert the support point geometric model into the component geometric feature set, and update the load transfer path in the static mechanical distribution feature.

[0134] For the case of hoisting precast floor slabs, the stress compensation instruction clearly specifies the specific positions of the temporary support points to be added, such as the coordinate positions in a specific area of the floor slab. By parsing this position information, the accurate positions of the temporary support points in three-dimensional space can be determined.

[0135] Insert the support point geometric model into the component geometric feature set. According to the position of the temporary support point and the design requirements, generate the geometric model of the support point. The support point geometric model can be a simple cylinder or cuboid, and its size and shape are determined according to the actual support requirements and structural characteristics. For example, for the temporary support points of precast floor slabs, if the design requires that the maximum load they bear is 50 kN, according to mechanical calculations and material properties, it is determined that the support points are in the shape of a cylinder with a bottom diameter of 200 mm and a height of 1 m.

[0136] After determining the shape and size of the support point geometric model, accurately insert it into the corresponding temporary support point position in the component geometric feature set. This requires precise coordinate positioning to ensure that the relative position relationship between the support point geometric model and the precast floor slab meets the requirements of the stress compensation instruction. For example, through 3D modeling software, accurately match the center point coordinates of the support point geometric model with the coordinates of the temporary support point position determined in the stress compensation instruction, so as to achieve the accurate insertion of the support point geometric model into the component geometric feature set.

[0137] Next, update the load transfer path in the static mechanical distribution feature. Before inserting the temporary support points, the load transfer path of the precast floor slab was determined based on the original structural design. For example, the load on the floor slab is transmitted to the surrounding beams and columns through its own structure. After inserting the temporary support points, the load transfer path will change.

[0138] To update the load transfer path, it is first necessary to analyze the impact of the temporary support points on the mechanical properties of the original structure. The finite element analysis method can be used to model and perform mechanical analysis on the precast floor slab structure after inserting the support points. For example, the precast floor slab is divided into multiple finite element units, and corresponding parameters are set according to the material properties and boundary conditions. Considering the existence of the temporary support points, the constraint conditions of the support points are added to the finite element model to simulate their supporting effect on the floor slab structure.

[0139] Through finite element analysis, the stress distribution and deformation of the floor slab structure after inserting the temporary support points are obtained. According to the analysis results, the load transfer path is re-determined. For example, some of the loads that were originally transferred to the surrounding beams will now be transferred to the ground through the temporary support points. Specifically, assume that before inserting the temporary support points, the load in a certain area of the floor slab was mainly transferred to the columns through the beams, with a transfer ratio of 80% to the beams and 20% to the columns. After inserting the temporary support points, through finite element analysis, it is found that 30% of the load in this area is transferred to the ground through the temporary support points, 50% is transferred to the beams, and 20% is transferred to the columns.

[0140] Update the new load transfer path information to the static mechanical distribution characteristics. This includes recording the starting point, the structural members passed through, and the ending point of the load transfer, and at the same time clarifying the load distribution ratio on each transfer path. For example, record in the form of a text description: "For the load in area A of the floor slab, 30% is transferred to the ground through the temporary support point S, 50% is transferred to column C1 through beam L, and 20% is transferred to column C2 through beam L". In this way, the operation of inserting the geometric model of the support point into the component geometric feature set and updating the load transfer path in the static mechanical distribution characteristics is completed, providing accurate mechanical information for subsequent construction and structural safety assessment.

[0141] Step S143: Analyze the priority sorting result in the installation sequence adjustment instruction, reconfigure the installation queue of the precast component units, and dynamically adjust the component dependency relationship in the building information model data set.

[0142] Taking the installation of multiple precast component units of an assembled residential building as an example, the installation sequence adjustment instruction contains the sorting result of the installation priorities of each precast component unit. Assume that this residential building includes component units such as precast walls, precast floor slabs, and precast stairs. The installation sequence adjustment instruction sorts these component units according to factors such as the collision risk coefficient and construction convenience. The sorting result is that the precast wall has the highest priority, followed by the precast floor slab, and finally the precast stairs.

[0143] Reconfigure the installation queue of prefabricated component units. According to the priority sorting result, adjust the original installation queue. The original installation queue may be arranged according to the component production order or the preliminary design installation order, and now it needs to be adjusted to a queue that conforms to the priority sorting. For example, if the original installation queue order is precast floor slab - precast wall - precast staircase, according to the priority sorting result, reconfigure it to precast wall - precast floor slab - precast staircase.

[0144] Dynamically adjust the component dependency relationships in the building information model data set. There are set dependency relationships between different prefabricated component units. For example, the installation of the precast floor slab can only be carried out after the installation of the precast wall is completed, and the installation of the precast staircase can only be carried out after the installation of the precast floor slab is completed. After reconfiguring the installation queue, it is necessary to adjust these component dependency relationships accordingly.

[0145] Based on the 3D model in the building information model data set, redefine the connection relationships and installation sequences between components. For example, in the building information model, the installation of the precast floor slab originally depended on the installation of some precast walls, but due to the adjustment of the installation sequence, now the precast floor slab needs to depend on the completion of the installation of all precast walls. By modifying the connection constraints and installation conditions between components in the building information model, the dynamic adjustment of component dependency relationships is realized. At the same time, update the information related to the installation sequence and dependency relationships in the component attribute parameter set, such as the pre-installation conditions of each component, the post-installation components, etc. In this way, the adjusted installation sequence and component dependency relationships can be accurately reflected in the building information model, providing clear construction guidance for construction personnel.

[0146] Step S144: Package the updated expected arrival time parameter, load transfer path, and component dependency relationship into the component dynamic optimization instruction set, and push the component dynamic optimization instruction set to the construction terminal device through the BIM collaboration platform.

[0147] For each prefabricated component unit, integrate its updated expected arrival time parameter, load transfer path information, and component dependency relationship information. For example, for a precast beam, summarize its new expected arrival time (such as 10:30 am), updated load transfer path (such as detailed information that part of the load is transferred to the ground through temporary support points), and adjusted component dependency relationship (such as it needs to be installed after the installation of several precast columns is completed).

[0148] Package this information in a suitable data format, such as JSON format. The JSON format has good readability and scalability, and can conveniently store and transmit various types of data. The packaged component dynamic optimization instruction set contains detailed optimization information for each prefabricated component unit, forming a complete data set.

[0149] Push the component dynamic optimization instruction set to the construction terminal device through the BIM collaboration platform. The BIM collaboration platform is an integrated management platform that can realize the sharing of building information and collaborative work. The construction terminal device can be a mobile device such as a tablet computer or a smart phone used by construction workers. On the BIM collaboration platform, a dedicated message push module is set up to send the encapsulated component dynamic optimization instruction set to the corresponding construction terminal device.

[0150] Construction workers can receive and view the component dynamic optimization instruction set through the installed BIM application on the construction terminal device. In the application, information such as the expected arrival time, load transfer path, and component dependency relationship of each precast component unit is displayed in an intuitive interface. For example, the expected arrival time of each component is displayed in a list form, the load transfer path is displayed in a 3D model, and the component dependency relationship is displayed in a flow chart. Construction workers can adjust the construction plan and operation process in a timely manner based on this information to ensure the smooth progress of the construction process.

[0151] Step S150: Synchronize the component dynamic optimization instruction set to the building information model data set to trigger the component attribute iterative update operation.

[0152] By updating the latest information in the component dynamic optimization instruction set to the building information model data set, the building information model can always reflect the actual state of the components and the adjustment of the construction plan.

[0153] Step S151: Create a version control node in the BIM collaboration platform to record the initial state of the current building information model data set.

[0154] Create a version control node in the BIM collaboration platform. The version control node is a key identifier for recording different version states of the building information model data set. Taking this prefabricated residential building project as an example, before performing the component attribute iterative update operation, a new version control node is created in the BIM collaboration platform. This version control node will record the initial state of the current building information model data set, including the attribute parameters, geometric features, installation sequence, dependency relationship, etc. of all precast component units.

[0155] Specifically, the version control node will take a complete snapshot of the building information model data set, which can record the detailed information of each precast component unit, such as the material type, size, connection node coordinates of the precast wall, the thickness and reinforcement of the precast floor slab, the number of steps and handrail form of the precast stairs, etc. At the same time, the relationship between components, such as installation sequence and dependency conditions, will also be recorded. By creating a version control node, it is convenient to trace back to the initial state in case of problems in subsequent update operations, ensuring the security and traceability of the data.

[0156] Step S152: Update the timestamp of the transportation stage in the component attribute parameter set according to the expected arrival time parameter in the component dynamic optimization instruction set, and recalculate the dependent time window for the subsequent construction stage.

[0157] Taking the precast column as an example, the updated expected arrival time in the component dynamic optimization instruction set is 2:00 pm. In the component attribute parameter set, update the transportation stage timestamp of this precast column to 2:00 pm. The transportation stage timestamp records the key time nodes during the transportation of the component, and updating this timestamp can accurately reflect the transportation progress of the component.

[0158] Recalculate the dependent time window for the subsequent construction stage. Since the expected arrival time of the precast column has changed, the time arrangements for the subsequent construction stages related to this precast column also need to be adjusted accordingly. For example, the installation of the beam can only start after the installation of the precast column is completed, so the start time of the beam installation depends on the arrival time and installation time of the precast column. Assuming that the installation of the precast column takes 1 hour, the earliest time for the beam installation can start at 3:00 pm. By recalculating these dependent time windows, the work for the subsequent construction stages can be reasonably arranged to avoid construction delays caused by unreasonable time arrangements.

[0159] Step S153: According to the update result of the load transfer path, mark the new stress concentration areas in the static mechanical distribution characteristics, and associate and generate an operation warning signal for the lifting equipment.

[0160] According to the update result of the load transfer path, mark the new stress concentration areas in the static mechanical distribution characteristics. As mentioned before, after inserting the temporary support points, the load transfer path of the precast floor slab has changed, and new stress concentration areas may appear. After obtaining the new stress distribution through finite element analysis, mark these new stress concentration areas in the static mechanical distribution characteristics.

[0161] For example, in the three-dimensional view of the building information model, mark the position and scope of the stress concentration areas in red. At the same time, record the detailed information of these stress concentration areas, such as the stress magnitude, stress change trend, etc. These information is very important for evaluating the safety of the components and guiding the subsequent construction operations.

[0162] Generate operation warning signals related to the lifting equipment. Since the emergence of stress concentration areas may affect the safe operation of the lifting equipment, corresponding operation warning signals are generated based on the information of the stress concentration areas. For example, if a stress concentration area is close to the lifting point, it may cause excessive stress on the lifting equipment. At this time, a warning signal is generated to prompt the lifting equipment operator to pay attention to adjusting the lifting parameters, such as reducing the lifting speed and decreasing the lifting weight. These operation warning signals are associated with the monitoring system of the lifting equipment. When the warning conditions are met, the system will automatically issue an alarm to remind the operator to take corresponding measures to ensure the safety of the lifting operation.

[0163] Step S154: According to the adjustment result of the component dependency relationship, reconstruct the installation logic network of the precast component unit to ensure that there are no circular dependency conflicts in the installation queue.

[0164] Taking the component installation of an assembled residential building as an example, due to the influence of the installation sequence adjustment instruction, the dependency relationship between components has changed. For example, the installation of the precast staircase originally depended on the installation of some precast floor slabs, and now it is adjusted to depend on the completion of the installation of all precast floor slabs.

[0165] To ensure that there are no circular dependency conflicts in the installation queue, it is necessary to reconstruct the installation logic network. First, all precast component units are used as nodes, and the dependency relationship between components is used as edges to construct a directed graph. For example, circular shapes are used to represent precast component units, and arrows are used to represent the dependency relationship, pointing from the dependent component to the component being depended on.

[0166] Then, topological sorting is performed on this directed graph. Topological sorting is an algorithm for sorting a directed acyclic graph (DAG), which can arrange the nodes in the graph in a set order so that for any directed edge (u, v), node u is arranged before node v. Through topological sorting, an installation order without circular dependencies can be obtained.

[0167] If circular dependencies are found in the graph during the topological sorting process, for example, precast component A depends on precast component B, and precast component B depends on precast component A, then it is necessary to re-examine the adjustment result of the component dependency relationship, find out the reasons for the circular dependencies and make corrections. It may be that the priority sorting in the installation sequence adjustment instruction is incorrect, or the definition of the connection relationship between components is inaccurate. By correcting these problems and performing topological sorting again until an installation logic network without circular dependencies is obtained.

[0168] Step S155: After completing all update operations, generate a difference comparison report, which includes the parameter change amounts before and after modification, the geometric feature change areas, and the evaluation results of the construction stage impacts.

[0169] After completing all update operations, a difference comparison report is generated. The difference comparison report is a summary and evaluation of the iterative update operations of component properties, which includes the parameter change amounts before and after modification, the geometric feature change regions, and the evaluation results of the impact on the construction stage.

[0170] For the parameter change amounts, the property parameters of each precast component unit are compared in detail before and after the update. For example, the change situations of parameters such as the dimensional error threshold and material strength grade of the precast wall are compared. Taking the length dimensional error threshold of the precast wall as an example, it was plus or minus 5 mm before the update and plus or minus 3 mm after the update, and the change amount of this parameter is recorded as 2 mm. Such comparisons and records are made for the important parameters of all components.

[0171] For the geometric feature change regions, by comparing the geometric features in the building information model data sets before and after the update, the changed regions are found. For example, after inserting temporary support points, the geometric features of the precast floor slab have changed, and the changed regions such as the position and scope of the support points are recorded. At the same time, the impacts of these geometric feature changes on the overall performance and construction of the components are described.

[0172] The evaluation result of the impact on the construction stage is to evaluate the impact of the iterative update operation of component properties on the entire construction process. Analyze the impacts of parameter changes and geometric feature changes on the transportation stage, hoisting stage, and installation stage. For example, the change in the expected arrival time of precast components may affect the personnel and equipment arrangements in the subsequent construction stages; geometric feature changes may affect the selection of hoisting equipment and installation techniques. Evaluate the degree and scope of these impacts, and propose corresponding countermeasures and suggestions. Organize this information into a difference comparison report to provide comprehensive decision-making basis for construction management personnel, so as to better control the construction progress and quality.

[0173] Step S210: Calculate the construction progress deviation index and the resource consumption increment based on the parameter change amounts in the difference comparison report.

[0174] The construction progress deviation index and the resource consumption increment are important indicators for evaluating the impacts of the iterative update operation of component properties on the construction progress and resource use.

[0175] First, calculate the construction progress deviation index. The construction progress deviation index reflects the degree of difference between the actual construction progress and the planned construction progress. According to the parameter change amounts in the difference comparison report, such as the change in the expected arrival time of precast components and the adjustment of the installation sequence, analyze the impacts of these changes on the construction progress.

[0176] For example, assume that the precast component A was originally scheduled to arrive at the construction site on the 5th day and start installation. Due to the change in the expected arrival time, it actually arrived on the 7th day. By analyzing the entire construction schedule, determine the impact of the delayed arrival of this component on the subsequent construction processes. Assume that the delay in the installation of this component caused a 2-day overall delay in the subsequent processes. According to the weights and time arrangements of each process in the construction schedule, calculate the construction schedule deviation index. For example, using the weighted average method, multiply the delay time of each affected process by its weight, then sum to obtain the total delay time, and then compare it with the planned total construction period to obtain the construction schedule deviation index. Assume that the planned total construction period is 30 days and the total delay time is 2 days, then the construction schedule deviation index is 2÷30≈0.067.

[0177] Next, calculate the resource consumption increment. The resource consumption increment refers to the increase in the amount of resources used due to the iterative update operation of the component attributes. Analyze the impact on resources such as manpower, material resources, and financial resources based on the parameter changes and geometric feature changes in the difference comparison report.

[0178] For example, due to the geometric feature changes of the precast component, it may be necessary to add additional lifting equipment or adjust the lifting plan, resulting in an increase in the rental cost of the lifting equipment. Or due to the adjustment of the installation sequence, it may be necessary to increase the working hours of the construction personnel, resulting in an increase in labor costs. Detailedly count the changes in these resource consumptions and calculate the resource consumption increment. Assume that the original cost of lifting the precast component was 5000 yuan. Due to the geometric feature changes, it is necessary to replace a more powerful lifting equipment, and the lifting cost increases to 6000 yuan. Then the resource consumption increment of the lifting equipment is 6000 - 5000 = 1000 yuan. Summarize the changes in all resource consumptions to obtain the total resource consumption increment.

[0179] Step S220: If it is detected that the construction schedule deviation index exceeds the seventh preset threshold, trigger the schedule compensation mechanism, and the schedule compensation mechanism includes adding parallel operation teams, optimizing the equipment scheduling plan, or adjusting the component production batches.

[0180] The seventh preset threshold is a critical value set according to the actual situation and management requirements of the project, and is used to determine whether compensation measures need to be taken for the construction schedule deviation. Assume that the seventh preset threshold is set to 0.05, and the previously calculated construction schedule deviation index is 0.067, which exceeds this threshold. At this time, it is necessary to trigger the schedule compensation mechanism.

[0181] The progress compensation mechanism includes various measures. First, increasing parallel work teams can be considered. For example, in the construction of prefabricated buildings, originally only one work team was responsible for the installation of precast walls. To speed up the construction progress, an additional parallel work team can be added. When two work teams install precast walls simultaneously, the installation time can be shortened by half. When adding parallel work teams, it is necessary to reasonably arrange personnel and equipment to ensure the coordinated work among teams and avoid conflicts and chaos.

[0182] Optimizing the equipment scheduling plan is also an effective progress compensation measure. For example, optimizing the scheduling of lifting equipment to reduce the idle time of the equipment. Originally, there may be a long waiting time between the lifting of different components by the lifting equipment. By optimizing the scheduling plan and reasonably arranging the lifting sequence and time, the lifting equipment can work continuously and efficiently. An intelligent scheduling system can be adopted to automatically plan the operation route and working time of the lifting equipment according to the installation sequence and location of the components, improving the utilization rate of the equipment.

[0183] Adjusting the production batches of components is also a feasible progress compensation measure. If it is found that the supply delay of some precast components hinders the construction progress, the production batches of the components can be adjusted. For example, originally the precast components were produced according to a set sequence and time interval. Now, the components that have a greater impact on the construction progress can be given priority in production to speed up their production speed. At the same time, communicate and coordinate with the component manufacturers to increase the investment in production equipment and personnel to ensure that the components can be supplied to the construction site in a timely manner.

[0184] When adjusting the production batches of components, a detailed analysis and planning of the entire component production process are required. First, it is necessary to clarify the criticality and dependency of each precast component in the construction progress. Taking a prefabricated residential project as an example, the supply of the foundation precast columns is crucial for the subsequent construction of the floors. If the supply of the foundation precast columns is delayed, it will directly affect the construction progress of the entire building. Therefore, when the construction progress deviation index exceeds the seventh preset threshold, the production of the foundation precast columns should be prioritized.

[0185] When specifically calculating the quantity and time nodes of the prefabricated components to be produced first, the construction progress plan and the actual progress deviation situation should be combined. Suppose the original plan was to complete the installation of all the foundation precast columns on the 10th day, but due to certain reasons, the actual progress was delayed by 3 days. According to the construction plan, the construction of the subsequent floors can only start after all the foundation precast columns are installed, and the construction time for each floor is 5 days. In order to make up for this 3-day progress deviation, it is necessary to speed up the production speed of the foundation precast columns. By analyzing the production process and equipment production capacity, it is determined that originally 5 foundation precast columns were produced per day, and now after increasing the equipment and personnel input, 8 can be produced per day. In this way, the production task of the foundation precast columns that originally needed 6 days to complete (30 columns) can now be completed in 4 days (32 columns, with the extra 2 columns produced as spares), thus reducing the progress deviation to 1 day.

[0186] When communicating and coordinating with the component production manufacturer, detailed production requirement information should be provided, including the type, quantity, size, quality requirements, and delivery time of the components. At the same time, the actual production capacity and resource status of the production manufacturer should be considered. For example, understand the existing raw material inventory, equipment operation conditions, and personnel allocation of the production manufacturer. If the raw material inventory of the production manufacturer is insufficient, it is necessary to assist it in communicating with the raw material supplier to ensure the timely supply of raw materials. For the situation of increasing equipment and personnel input, a specific implementation plan should be jointly formulated with the production manufacturer, including the procurement or lease of equipment, the recruitment and training of personnel, etc.

[0187] Suppose the production manufacturer originally had 3 sets of equipment for producing foundation precast columns, and the production capacity of each set of equipment per day was 5 columns. In order to meet the demand for accelerated production, it was decided to lease 2 more sets of the same equipment for a lease period of 10 days, and the lease cost of each set of equipment was 500 yuan per day. At the same time, 5 new production workers were recruited, with a daily wage of 300 yuan per person, and the training period was 2 days, and the training cost was 500 yuan per person. In this way, after increasing the equipment and personnel input, the total production capacity of the production manufacturer was increased to 25 foundation precast columns per day (5 sets of equipment × 5 columns / set), and the production task could be completed in a shorter time.

[0188] In addition to increasing parallel operation teams, optimizing the equipment scheduling plan, and adjusting the component production batches, the construction process can also be optimized. For example, adopting more advanced installation technologies to improve the installation efficiency of prefabricated components. For the installation of precast walls, the traditional hoisting and splicing methods were originally used, with a long installation time and difficult to guarantee accuracy. Now, automated installation equipment and technologies, such as robotic installation systems, can be introduced to quickly and accurately complete the wall installation work. By optimizing the construction process, not only can the construction progress be accelerated, but also the construction quality can be improved.

[0189] During the implementation of the progress compensation mechanism, it is necessary to monitor the changes in the construction progress in real time. Establish a perfect progress monitoring system, and obtain the construction progress data in real time through devices such as sensors and monitoring cameras at the construction site. Compare the actual construction progress with the adjusted progress plan, promptly discover new progress deviations, and adjust the progress compensation measures according to the deviation situation. For example, if it is found that after adding parallel operation teams, problems occur in the coordination between teams, resulting in a decrease rather than an increase in construction efficiency, it is necessary to promptly adjust the operation arrangement and strengthen the communication and cooperation between teams. At the same time, it is necessary to evaluate the implementation effect of the progress compensation mechanism, summarize experience and lessons, and provide reference for the construction management of subsequent projects.

[0190] Step S230: If it is detected that the resource consumption increment exceeds the eighth preset threshold, trigger the resource reallocation mechanism, and the resource reallocation mechanism includes renegotiating the supplier delivery cycle, enabling the standby component inventory, or adjusting the machinery usage rate.

[0191] The eighth preset threshold is a critical value set according to the project budget and resource management objectives, and is used to determine whether resource reallocation measures need to be taken for the increase in resource consumption. Suppose the eighth preset threshold is set at 10%, and the previously calculated resource consumption increment reaches 15%, exceeding this threshold. At this time, it is necessary to start the resource reallocation mechanism.

[0192] Renegotiating the supplier delivery cycle is an important measure in the resource reallocation mechanism. In prefabricated building projects, many precast components and raw materials rely on the supply of suppliers. When the resource consumption increment is too large, it may be due to the long delivery cycle of the supplier, resulting in resource shortages during the construction process, and some additional measures have to be taken to ensure the construction progress, thus increasing the resource consumption.

[0193] For example, a certain supplier originally promised a delivery cycle of 15 days for a certain precast beam, but the actual delivery time was extended to 20 days, resulting in the construction unit increasing the rental time of lifting equipment and the labor overtime cost to make up for the project duration loss, thus significantly increasing the resource consumption. In this case, the construction unit needs to renegotiate the delivery cycle with the supplier. The construction unit can provide the supplier with a detailed construction progress plan and resource requirements, and explain the importance and urgency of shortening the delivery cycle. At the same time, incentive measures set for the supplier can also be considered, such as paying part of the payment in advance or increasing the subsequent order quantity. Suppose after negotiation, the supplier agrees to shorten the delivery cycle to 12 days, so that the construction unit can reduce the rental time of lifting equipment and the labor overtime cost, thus reducing the resource consumption.

[0194] Enabling the inventory of spare components is also an effective resource reallocation measure. During the project planning phase, a set quantity of spare component inventory is usually prepared to address possible component damage, supply delays, etc. When the incremental resource consumption exceeds the threshold, it can be considered to enable the inventory of spare components to meet the construction requirements and avoid resource waste caused by waiting for the production and supply of new components.

[0195] For example, during the construction process, it is found that a batch of precast floor slabs has quality problems and needs to be re-produced and supplied, which may lead to delays in the construction progress and increased resource consumption. At this time, if there is an inventory of spare precast floor slabs, these inventories can be immediately enabled to ensure the normal progress of the construction. When enabling the inventory of spare components, a detailed inspection and evaluation of the inventory should be carried out to ensure that its quality meets the requirements. At the same time, the inventory of spare components should be replenished in a timely manner to avoid similar problems occurring again during subsequent construction.

[0196] Adjusting the machinery usage rate is also part of the resource reallocation mechanism. When the incremental resource consumption is too large, it may be due to the too high machinery usage rate, resulting in an increase in construction costs. The machinery usage rate can be renegotiated with the machinery leasing company, or a more suitable machinery leasing supplier can be found.

[0197] For example, the original cost of renting a tower crane was 2,000 yuan per day. After market research and negotiation with the leasing company, it was found that a more preferential leasing company could be found, and the leasing cost of its tower crane was 1,800 yuan per day. By changing the leasing company, the machinery usage rate was reduced, thus reducing resource consumption. When adjusting the machinery usage rate, factors such as the performance, quality, and maintenance services of the machinery should be comprehensively considered to ensure that the normal progress of the construction is not affected while reducing costs.

[0198] During the implementation of the resource reallocation mechanism, the usage situation of resources should be monitored and analyzed in real time. A resource management database should be established to record information such as the procurement, usage, and inventory of resources, so as to promptly grasp the dynamic changes of resources. At the same time, the implementation effect of the resource reallocation mechanism should be evaluated to judge whether the goal of reducing resource consumption has been achieved. If it is found that the effect of certain measures is not good, the strategy should be adjusted in a timely manner and other more effective resource reallocation measures should be taken.

[0199] Step S240: Integrate the feedback instructions generated by the progress compensation mechanism and the resource reallocation mechanism into the component dynamic optimization instruction set to form a closed-loop control process.

[0200] The closed-loop control process is a management mode of continuous feedback and adjustment, which can enable the prefabricated building component management system to adjust the strategy in real time according to the actual situation and ensure the optimization of the construction progress and resource utilization.

[0201] First, organize and classify the feedback instructions generated by the progress compensation mechanism and the resource reallocation mechanism. The feedback instructions generated by the progress compensation mechanism may include specific arrangements for increasing parallel job teams, detailed plans for optimizing equipment scheduling schemes, time nodes for adjusting component production batches, etc. The feedback instructions generated by the resource reallocation mechanism may include the results of renegotiating the supplier delivery cycle, the quantity and time of enabling backup component inventories, specific terms for adjusting machinery usage rates, etc.

[0202] Classify these feedback instructions according to the type of components and the construction stage. For example, for the construction of precast walls, classify the progress compensation and resource reallocation feedback instructions related to precast walls into one category; for the construction of precast floors, classify the relevant feedback instructions into another category. This can facilitate the subsequent integration of these instructions into the component dynamic optimization instruction set.

[0203] Then, integrate the classified feedback instructions into the component dynamic optimization instruction set. In the component dynamic optimization instruction set, each precast component unit has corresponding optimization information, including the expected arrival time, load transfer path, component dependency relationship, etc. Add the progress compensation and resource reallocation feedback instructions to the optimization information of the corresponding component unit.

[0204] For example, for a certain precast column, the original component dynamic optimization instruction set contains information such as its expected arrival time and installation sequence. Now add the feedback instructions such as the adjusted production batch time node in the progress compensation mechanism and the renegotiated supplier delivery cycle in the resource reallocation mechanism to the optimization information of this precast column. In this way, the component dynamic optimization instruction set contains more comprehensive and up-to-date optimization information, which can better guide the construction process.

[0205] The key to forming a closed-loop control process is to achieve a cycle of feedback and adjustment. During the construction process, continuously collect the actual data of the construction progress and resource usage, and compare it with the planned data in the component dynamic optimization instruction set. If a deviation is found between the actual situation and the plan, trigger the progress compensation mechanism and the resource reallocation mechanism again, generate new feedback instructions, and integrate them into the component dynamic optimization instruction set.

[0206] For example, after construction for a period of time, it is found that although progress compensation measures have been taken, the construction progress is still behind the plan. By analyzing the actual data, it is found that the delivery cycle of a certain supplier has been extended again, resulting in the untimely supply of related components. At this time, restart the resource reallocation mechanism, renegotiate the delivery cycle with the supplier, and integrate the new feedback instructions into the component dynamic optimization instruction set. Through such continuous feedback and adjustment, the prefabricated building component management system can adapt to various changes, ensure that the construction project is completed on time and according to quality requirements, and at the same time achieve the reasonable utilization of resources and the effective control of costs.

[0207] Figure 2 FIG. shows a schematic diagram of exemplary hardware and software components of a BIM-based prefabricated building component management system 100 that can implement the idea of the present application provided by some embodiments of the present application. For example, the processor 120 can be used on the BIM-based prefabricated building component management system 100 and is used to execute the functions in the present application.

[0208] The BIM-based prefabricated building component management system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the BIM-based prefabricated building component management method of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0209] For example, the BIM-based prefabricated building component management system 100 can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROMs, or RAMs, or any combination thereof. Exemplarily, the BIM-based prefabricated building component management system 100 can also include program instructions stored in a ROM, a RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The BIM-based prefabricated building component management system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0210] For ease of explanation, only one processor is described in the BIM-based prefabricated building component management system 100. However, it should be noted that the BIM-based prefabricated building component management system 100 in the present application can also include multiple processors. Therefore, the steps executed by one processor described in the present application can also be jointly executed or separately executed by multiple processors. For example, if the processor of the BIM-based prefabricated building component management system 100 executes steps A and B, it should be understood that steps A and B can also be jointly executed by two different processors or separately executed in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor jointly execute steps A and B.

[0211] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned BIM-based prefabricated building component management method is implemented.

[0212] It should be noted that, in order to simplify the description of the present invention disclosure and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, sometimes multiple features are incorporated into one embodiment, drawing or description thereof.

Claims

1. A method for managing prefabricated building components based on BIM, characterized in that: The method comprises: Acquire a building information model data set of a target prefabricated building project, wherein the building information model data set includes a component attribute parameter set and a component geometric feature set of a plurality of prefabricated component units, wherein the component attribute parameter set includes a component material type, a component size error threshold, and a component connection node positioning coordinate; Performing multi-level component feature analysis on the building information model data set to obtain a standardized geometric feature sequence and a dynamic construction state feature set of each prefabricated component unit, wherein the dynamic construction state feature set includes a positioning offset in a component transportation phase, a stress distribution gradient in a component hoisting phase, and a collision risk coefficient in a component installation phase; Based on a preset construction stage matching strategy, the standardized geometric feature sequence and the dynamic construction state feature set are subjected to cross-stage collaborative analysis and processing to generate a component matching strategy set of the prefabricated component unit, wherein the component matching strategy set is used to indicate a component installation sequence adjustment scheme and connection node accuracy compensation parameters; A component dynamic optimization instruction set is generated according to the component matching strategy set, and the component dynamic optimization instruction set is synchronized to the building information model data set to trigger a component attribute iterative update operation.

2. The method for managing prefabricated building components based on BIM according to claim 1, characterized in that: The step of obtaining a building information model data set of a target prefabricated building project includes: Performing data preprocessing operations on the original building information model, wherein the data preprocessing operations include component attribute denoising processing, geometric feature topology verification processing, and spatial coordinate system alignment processing; In the data preprocessing operation, when performing component attribute denoising, each prefabricated component unit in the original building information model is traversed to identify abnormal parameter items in the component attribute parameter set, wherein the abnormal parameter items include component material types exceeding a preset material strength range, dimension measurement values ​​exceeding a component dimension error threshold, and connection node positioning coordinates deviating from a design coordinate range; Based on a preset abnormal replacement rule, the abnormal parameter item is replaced with a standard parameter item in the component attribute parameter set to generate a denoised component attribute parameter set; When performing the geometric feature topology verification processing, the geometric constraint verification algorithm is called to verify the surface continuity error, edge and corner alignment and hole integrity in the component geometric feature set. If it is detected that the surface continuity error exceeds a first preset threshold, the edge and corner alignment is lower than a second preset threshold, or the hole integrity does not meet the preset closure condition, a geometric repair instruction is generated and the component geometric feature set is reconstructed; When performing the spatial coordinate system alignment process, the local coordinate system in the component geometric feature set is subjected to translation and rotation transformation according to the reference coordinate system of the global positioning system of the construction site, so that the spatial offset between the local coordinate system of all prefabricated component units and the reference coordinate system is less than a third preset threshold, thereby obtaining the building information model data set.

3. The BIM-based prefabricated building component management method according to claim 1, characterized in that: The multi-level component feature analysis of the building information model data set to obtain a standardized geometric feature sequence and a dynamic construction status feature set for each prefabricated component unit includes: Extracting surface mesh data, internal support structure data and connection node geometry data from the component geometry feature set, performing curvature homogenization processing on the surface mesh data, and generating standardized surface mesh features; Calculating the section inertia moment and analyzing the load transfer path on the internal support structure data to obtain the static mechanical distribution characteristics of the prefabricated component unit; Performing tolerance analysis on the geometric data of the connection nodes, calculating the difference between the theoretical gap and the actual gap between the connection nodes, and generating node matching error characteristics; The standardized surface mesh feature, the static mechanical distribution feature and the node matching error feature are integrated to generate the standardized geometric feature sequence; Real-time collection of sensor monitoring data sets at the construction site, including transport vehicle positioning data, hoisting equipment stress data, and installation phase point cloud scanning data; Performing Kalman filtering on the positioning data of the transport vehicle, extracting the real-time position offset of the prefabricated component unit during the transport phase, and calculating the cumulative offset distance from the designed path; Performing frequency domain analysis on the stress data of the hoisting equipment, extracting the amplitude peak and frequency distribution characteristics in the principal stress direction, and combining the weight distribution parameters of the prefabricated component unit to generate the stress distribution gradient of the component hoisting stage; Performing three-dimensional registration processing on the point cloud scanning data of the installation stage, comparing the overlap between the scanning point cloud and the design geometric model in the building information model data set, calculating the volume proportion and spatial distance of the non-overlapping area, and generating the collision risk coefficient of the component installation stage; The real-time position offset, the stress distribution gradient of the component hoisting stage and the collision risk coefficient of the component installation stage are combined into the dynamic construction state feature set.

4. The BIM-based prefabricated building component management method according to claim 3, characterized in that: The method of performing cross-stage collaborative analysis and processing on the standardized geometric feature sequence and the dynamic construction state feature set based on the preset construction stage matching strategy to generate a component matching strategy set for the prefabricated component unit includes: Constructing a construction phase matching strategy library, wherein the construction phase matching strategy library includes a transportation phase path optimization strategy, a hoisting phase stress equalization strategy, and an installation phase collision avoidance strategy; For the path optimization strategy in the transportation phase, dynamically adjust the driving path of the transport vehicle according to the real-time position offset and the accumulated offset distance, mark the risk offset area in the building information model data set, and generate a path replanning instruction; According to the stress equalization strategy in the hoisting stage, the non-uniformity of the stress distribution gradient of the component in the hoisting stage is analyzed. If it is detected that the gradient value of the stress concentration area exceeds the fourth preset threshold, the number and position of the temporary support points to be added are calculated, and a stress compensation instruction is generated; For the collision avoidance strategy in the installation phase, the non-overlapping areas are prioritized according to the size of the collision risk coefficient in the component installation phase. If the volume proportion of the non-overlapping areas exceeds the fifth preset threshold or the spatial distance exceeds the sixth preset threshold, an installation sequence adjustment instruction and a node precision compensation parameter are generated; The path replanning instructions, stress compensation instructions and installation sequence adjustment instructions are integrated into the component matching strategy set.

5. The BIM-based prefabricated building component management method according to claim 4, characterized in that: The step of generating a component dynamic optimization instruction set according to the component matching strategy set includes: Parsing the risk offset area coordinates in the path replanning instruction, associating and marking the modified key control points of the transportation path in the building information model data set, and updating the expected arrival time parameters in the component attribute parameter set; parsing the temporary support point position in the stress compensation instruction, inserting the support point geometry model into the component geometry feature set, and updating the load transfer path in the static mechanics distribution feature; Parsing the priority ranking result in the installation sequence adjustment instruction, reconfiguring the installation queue of the prefabricated component unit, and dynamically adjusting the component dependency in the building information model data set; The updated expected arrival time parameters, load transfer paths and component dependencies are encapsulated as the component dynamic optimization instruction set, and the component dynamic optimization instruction set is pushed to the construction terminal equipment through the BIM collaboration platform.

6. The BIM-based prefabricated building component management method according to claim 5, characterized in that: The step of synchronizing the component dynamic optimization instruction set to the building information model data set to trigger the component attribute iterative update operation includes: Creating a version control node in the BIM collaboration platform to record the initial state of the current building information model data set; According to the expected arrival time parameter in the component dynamic optimization instruction set, the transportation phase timestamp in the component attribute parameter set is updated, and the dependent time window of the subsequent construction phase is recalculated; According to the update result of the load transfer path, a new stress concentration area is marked in the static mechanical distribution feature, and an operation warning signal of the lifting equipment is generated in association; According to the adjustment result of the component dependency relationship, the installation logic network of the prefabricated component unit is reconstructed to ensure that there is no circular dependency conflict in the installation queue; After all update operations are completed, a difference comparison report is generated, which includes parameter changes before and after the modification, geometric feature change areas, and construction stage impact assessment results.

7. The BIM-based prefabricated building component management method according to claim 6, characterized in that: After generating the difference comparison report, the method further includes: Calculate the construction progress deviation index and resource consumption increment based on the parameter changes in the difference comparison report; If it is detected that the construction progress deviation index exceeds the seventh preset threshold, a progress compensation mechanism is triggered, and the progress compensation mechanism includes increasing parallel working teams, optimizing equipment scheduling plans, or adjusting component production batches; If it is detected that the resource consumption increment exceeds an eighth preset threshold, a resource reallocation mechanism is triggered, wherein the resource reallocation mechanism includes renegotiating supplier delivery cycles, activating spare component inventory, or adjusting machinery usage rates; The feedback instructions generated by the progress compensation mechanism and the resource reallocation mechanism are integrated into the component dynamic optimization instruction set to form a closed-loop control process.

8. The BIM-based prefabricated building component management method according to claim 3, characterized in that: The three-dimensional registration processing is performed on the point cloud scanning data of the installation stage, the overlap degree of the scanning point cloud is compared with the design geometric model in the building information model data set, the volume proportion and spatial distance of the non-overlapping area are calculated, and the collision risk coefficient of the component installation stage is generated, including: Extracting a feature point set in the design geometric model, wherein the feature point set includes connection node center points and component contour corner points; Downsampling the point cloud scanning data in the installation phase to generate a downsampled point cloud set, and calculating the curvature distribution parameter of each point in the downsampled point cloud set; Based on the curvature distribution parameters of the feature point set and the downsampled point cloud set, a point cloud rough registration operation is performed to generate an initial transformation matrix; Optimizing the rotation and translation parameters of the initial transformation matrix by an iterative closest point algorithm to generate a precise registration transformation matrix; Applying the fine registration transformation matrix to the downsampled point cloud set to generate a registration point cloud set aligned with the design geometric model space; Calculating the volume ratio of the non-overlapping area between the registration point cloud set and the designed geometric model to the total volume of the designed geometric model, and extracting the minimum Euclidean distance from each point in the non-overlapping area to the surface of the designed geometric model; The component installation phase collision risk coefficient is generated according to a weighted sum of the volume ratio and the minimum Euclidean distance.

9. The BIM-based prefabricated building component management method according to claim 4, characterized in that: The construction process of the matching strategy library in the construction phase includes: Collecting a construction data set of historical prefabricated building projects, wherein the construction data set includes optimized path parameters, stress equalization records, and collision avoidance solutions of successful cases; Perform cluster analysis on the optimized path parameters of the successful cases, extract the frequently occurring path correction patterns, and encode them into a rule set for the path optimization strategy in the transportation phase; Performing regression analysis on the stress equalization records, establishing a fitting relationship between the stress distribution gradient and the number of temporary support point configurations, and generating a decision tree for the stress equalization strategy in the hoisting stage; Performing association rule mining on the collision avoidance scheme, identifying the causal relationship chain between the adjustment of the installation sequence and the reduction of the collision risk coefficient, and constructing a reasoning graph of the collision avoidance strategy in the installation phase; The rule set, decision tree and reasoning graph are stored in the construction phase matching strategy library, and dynamic weight coefficients are configured to adapt to the priorities of different construction scenarios.

10. A BIM-based prefabricated building component management system, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the BIM-based prefabricated building component management method described in any one of claims 1 to 9.

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