A prefabricated building construction quality tracing system

Abstract

The present invention relates to the field of construction management technology, and specifically to a prefabricated building construction quality tracing system, comprising a model comparison unit, a real-scene modeling unit, a progress tracing unit, a data acquisition unit, and a data management unit, and further comprising: a BIM modeling unit, wherein the BIM modeling unit is used to construct a static BIM three-dimensional model of the target prefabricated building using BIM technology based on the design drawings of the target prefabricated building, and transmit the static BIM three-dimensional model to a virtual segmentation unit. By comparing the static BIM three-dimensional model with the real-scene three-dimensional model, the present invention can detect differences and deviations in the assembly process, promptly identify problems and take corrective measures, thereby improving the accuracy and quality of building assembly. By combining the consumption of assembly components, the output volume, and the actual construction progress data, the construction progress can be accurately tracked and managed to ensure that the project proceeds as planned.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to a prefabricated building construction quality tracing system. Background Art

[0002] Prefabricated buildings, also known as prefabricated buildings, are a type of construction method that prefabricates building components in a factory and then transports them to the construction site for assembly and installation. Unlike traditional cast-in-place concrete buildings, most of the work on prefabricated buildings is done in a factory environment, while assembly and connection are mainly done on site.

[0003] Traditional systems are usually unable to efficiently integrate and manage data from multiple aspects such as supply chain, production, transactions, progress and environment, resulting in serious information silos and a lack of a comprehensive perspective for project management. In addition, traditional systems' data monitoring and feedback on the construction process are often delayed, making it difficult to promptly detect and address differences and deviations in the assembly process, leading to the accumulation of problems and affecting the quality and progress of the project. In addition, traditional systems rely on a large amount of manual operations, including data entry, inspection and comparison, which are prone to human errors and have low efficiency. In complex engineering projects, manual operations cannot guarantee the accuracy and completeness of data. In addition, traditional systems find it difficult to accurately track and manage construction progress by combining the consumption, outbound volume and actual construction progress data of assembly components, resulting in insufficiently detailed progress management and difficulty in promptly detecting and correcting progress deviations. Summary of the Invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and propose a prefabricated building construction quality tracing system.

[0005] The technical solution of the present invention is a prefabricated building construction quality tracing system, comprising a model comparison unit, a real-scene modeling unit, a progress tracing unit, a data acquisition unit, and a data management unit, and further comprising:

[0006] A BIM modeling unit, configured to construct a static BIM three-dimensional model of the target prefabricated building using BIM technology according to the design drawings of the target prefabricated building, and transmit the static BIM three-dimensional model to the virtual segmentation unit;

[0007] a virtual segmentation unit, which receives the static BIM three-dimensional model transmitted by the BIM modeling unit, and virtually segments the static BIM three-dimensional model based on the assembly component consumption to obtain a static BIM three-dimensional model corresponding to the assembly component consumption, and transmits the static BIM three-dimensional model corresponding to the assembly component consumption to a progress display unit;

[0008] a dispatch recording unit, configured to take out assembly components from the assembly component warehouse according to the required quantity of assembly components at each stage corresponding to the construction schedule, so as to obtain the dispatch quantity of assembly components at each stage, and transmit the dispatch quantity of assembly components at each stage to the progress display unit;

[0009] A progress display unit receives the static BIM three-dimensional models corresponding to the assembly component outbound quantity of each stage transmitted by the outbound record unit and the assembly component consumption quantity transmitted by the virtual segmentation unit, matches the assembly component outbound quantity of each stage with the static BIM three-dimensional models corresponding to the assembly component consumption quantity to obtain the static BIM three-dimensional models of each stage, and transmits the static BIM three-dimensional models of each stage to the model comparison unit.

[0010] Preferably, the real-scene modeling unit is used to perform real-scene three-dimensional modeling of the target prefabricated building through oblique photography technology to obtain a target prefabricated real-scene three-dimensional model, and transmit the target prefabricated real-scene three-dimensional model to the model comparison unit.

[0011] Preferably, the model comparison unit receives the static BIM three-dimensional model of each stage transmitted by the progress display unit and the target prefabricated real-scene three-dimensional model transmitted by the real-scene modeling unit, and compares the static BIM three-dimensional model of each stage with the target prefabricated real-scene three-dimensional model through a model comparison method to obtain a comparison degree between the two, and transmits the comparison degree to the progress tracing unit.

[0012] Preferably, the data collection unit is used to collect production data and transaction circulation data of each link in the supply chain, and collect progress data and environmental data of each construction link, combine the production data, the transaction circulation data, the progress data and the environmental data to obtain traceability data, and transmit the traceability data to the data management unit.

[0013] Preferably, the data management unit receives the traceability data transmitted by the data acquisition unit, and classifies the traceability data to obtain multiple different types of traceability data, and constructs a data mapping table for different types of traceability data, and transmits the data mapping table to the progress tracing unit.

[0014] Preferably, the progress tracing unit receives the comparison degree transmitted by the model comparison unit and the data mapping table transmitted by the data management unit, and compares the comparison degree with a preset contrast threshold. If it is greater than or equal to the preset contrast threshold, the progress of the target prefabricated building is normal; otherwise, the progress of the target prefabricated building is abnormal. The deviation level is matched based on the difference between the comparison degree and the preset contrast threshold, and the deviation level is matched with the data mapping table to obtain the traceability data corresponding to the deviation level, and the traceability data corresponding to the deviation level is sent to the corresponding administrator.

[0015] Preferably, the static BIM three-dimensional model is virtually segmented based on the assembly component consumption to obtain the static BIM three-dimensional model corresponding to the assembly component consumption, comprising the following steps:

[0016] A1. Based on the assembly component information, determining the position and spatial layout of each assembly component in the static BIM three-dimensional model;

[0017] A2. Based on the consumption data of the assembly components, the static BIM three-dimensional model is divided according to the position and spatial layout of each assembly component.

[0018] Preferably, the model comparison method comprises the following steps:

[0019] B1. Extract geometric attribute features of the static BIM 3D model and the target prefabricated real-scene 3D model. The geometric attribute features include volume, surface area, and principal moments of inertia. The volume calculation formula is as follows:

[0020]

[0021] Where V represents volume, s i Represents a symbolic variable, D i represents the determinant of the coordinates of the three vertices of each tetrahedron base, and P i1x Represents the x-axis coordinate of the first vertex in the base of the i-th tetrahedron;

[0022] The surface area calculation formula is as follows:

[0023]

[0024] Where A represents the surface area, P i1 Represents the first vertex of the i-th triangle;

[0025] The principal moment of inertia calculation formula is as follows:

[0026]

[0027] Where J represents the principal moment of inertia, V i represents the volume of the i-th tetrahedron, f( ) represents a homogeneous quadratic polynomial;

[0028] B2. Extracting view and internal distance map features of the static BIM 3D model and the target prefabricated real-scene 3D model;

[0029] B3. Construct a feature matrix based on the geometric attribute features and the view and internal distance map features, and calculate the similarity between the feature matrix corresponding to the static BIM three-dimensional model and the feature matrix corresponding to the target prefabricated real-life three-dimensional model using Canberra distance. The similarity is the comparison degree between the two.

[0030] Preferably, extracting the view and internal distance map features of the static BIM three-dimensional model and the target prefabricated real-scene three-dimensional model comprises the following steps:

[0031] C1. Construct a minimum value recording matrix, a maximum value recording matrix, and an update recording matrix. The minimum value recording matrix is ​​used to record the minimum z coordinate of the intersection of the ray emitted by each pixel point and all triangular faces. The maximum value recording matrix is ​​used to record the maximum z coordinate of the intersection of the ray emitted by each pixel point and all triangular faces. The update recording matrix is ​​used to record whether each pixel point has been updated.

[0032] C2. Traverse all triangular faces in the static BIM 3D model and the target prefabricated real-life 3D model, find the plane equation of each triangular face, and find its projected triangular face on the xOy plane. If the projected triangular face is perpendicular to the xOy plane, process the next triangular face.

[0033] C3. Calculate the y-coordinate range of the pixel points of the projected triangle on the xOy plane, compare the y-coordinate range with a preset range threshold, and if it meets the preset range threshold, abstract the y-coordinate range into a scan line and calculate the x-coordinate range of the pixel points of the projected triangle on the scan line;

[0034] C4. Combine the x-coordinate range and the y-coordinate range to obtain multiple pixel coordinates, calculate the z-coordinate of the intersection of the ray emitted by the pixel coordinate and the triangular face, and update the minimum value record matrix, the maximum value record matrix, and the update record matrix according to the z-coordinate. Repeat steps B2-B4 until all triangular faces are processed.

[0035] C5. Calculate the view and interior distance map features based on the updated minimum value record matrix and the maximum value record matrix. The view and interior distance map feature calculation formula is as follows:

[0036]

[0037] Among them, IDM represents the view and internal distance map feature, zBudder2 represents the maximum value record matrix, zBudder1 represents the minimum value record matrix, z max and z min Represents the maximum and minimum values ​​of the z coordinate respectively.

[0038] Preferably, classifying the traceability data to obtain a plurality of different types of traceability data comprises the following steps:

[0039] D1. Extract the data features of the traceability data to obtain a set of data feature vectors. The data feature vectors are as follows:

[0040] X={x1,x2,...,x i ,...,x n};

[0041] Among them, X represents the data feature vector set, x i represents the i-th data feature vector in the data feature vector set, and x i ={a1s1, a2s2, ..., a i s i ,...,a n s n}, a i Represents the weight coefficient of the i-th sample of the i-th data feature vector, s i Represents the characteristic parameter of the i-th sample of the i-th data feature vector;

[0042] D2. Selecting the optimal cutoff distance of the density peak clustering algorithm using the fruit fly algorithm based on the data feature vector set;

[0043] D3. Substitute the optimal cutoff distance into the density peak clustering algorithm to calculate the distance between each sample in the data feature vector set. The distance calculation formula is as follows:

[0044] H ij =dist(x i , x j );

[0045] Among them, H ij Represents the data feature vector set, x j represents the jth data feature vector in the data feature vector set, and dist() represents the distance function;

[0046] D4. Calculate the local density of the data feature vector based on the distance between each sample in the data feature vector set, and calculate the relative distance of the data feature vector based on the distance and the local density. The local density calculation formula is as follows:

[0047]

[0048] Among them, ρ i represents the local density of the i-th data feature vector in the data feature vector set, λ represents the density kernel function, d best represents the optimal cutoff distance obtained by the fruit fly algorithm;

[0049] The relative distance calculation formula is as follows:

[0050]

[0051] Among them, h i Represents the relative distance, ρ max represents the maximum local density, and min() represents the minimum function;

[0052] D5. Construct a decision diagram according to the local density and the relative distance, select cluster centers based on the decision diagram, and assign the remaining data feature vector sets to corresponding clusters to obtain multiple different types of traceability data.

[0053] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0054] By comparing the static BIM three-dimensional model with the real-life three-dimensional model, the present invention can detect differences and deviations in the assembly process, discover problems in a timely manner and take corrective measures, thereby improving the accuracy and quality of building assembly. By combining the consumption, outbound volume and actual construction progress data of assembly components, the construction progress can be accurately tracked and managed to ensure that the project proceeds as planned. Through the data acquisition unit and the data management unit, data from multiple links such as supply chain, production, transaction, progress and environment can be collected, integrated and managed. This traceability data can help trace the root cause of the problem, improve management efficiency and the scientific nature of decision-making, and can automatically process and compare large amounts of data, reducing human errors and information asymmetry, and improving management efficiency and transparency. Administrators can quickly respond to and solve problems based on deviation levels and traceability data, thereby reducing construction risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the overall system in one embodiment of the present invention.

[0056] Figure numerals: 1. BIM modeling unit; 2. Virtual segmentation unit; 3. Progress display unit; 4. Outbound record unit; 5. Model comparison unit; 6. Real-scene modeling unit; 7. Progress tracing unit; 8. Data acquisition unit; 9. Data management unit. DETAILED DESCRIPTION

[0057] Example 1, as Figure 1 As shown, the present invention proposes a prefabricated building construction quality tracing system, which includes a model comparison unit 5, a real scene modeling unit 6, a progress tracing unit 7, a data acquisition unit 8 and a data management unit 9, and also includes:

[0058] BIM modeling unit 1, BIM modeling unit 1 is used to construct a static BIM three-dimensional model of the target prefabricated building according to the design drawings of the target prefabricated building using BIM technology, and transmit the static BIM three-dimensional model to the virtual segmentation unit 2;

[0059] The virtual segmentation unit 2 receives the static BIM three-dimensional model transmitted by the BIM modeling unit 1, and virtually segments the static BIM three-dimensional model based on the assembly component consumption to obtain the static BIM three-dimensional model corresponding to the assembly component consumption, and transmits the static BIM three-dimensional model corresponding to the assembly component consumption to the progress display unit 3;

[0060] The outbound recording unit 4 is used to take out assembly components from the assembly component warehouse according to the required quantity of assembly components at each stage corresponding to the construction schedule, so as to obtain the outbound quantity of assembly components at each stage, and transmit the outbound quantity of assembly components at each stage to the progress display unit 3;

[0061] The progress display unit 3 receives the static BIM three-dimensional model corresponding to the assembly component outbound quantity of each stage transmitted by the outbound record unit 4 and the assembly component consumption quantity transmitted by the virtual segmentation unit 2, and matches the assembly component outbound quantity of each stage with the static BIM three-dimensional model corresponding to the assembly component consumption quantity to obtain the static BIM three-dimensional model of each stage, and transmits the static BIM three-dimensional model of each stage to the model comparison unit 5.

[0062] In this context, BIM technology is a building design, construction, and management technology based on three-dimensional digital models. It integrates various information about a building project to provide an information platform for the entire lifecycle, from design and construction to operation and maintenance, enabling information sharing and collaborative work. Prefabricated components refer to building components prefabricated in a factory or other location during construction. These components are assembled and installed on-site. Prefabricated components can significantly improve construction efficiency, quality, and safety, while reducing on-site workload and construction time.

[0063] In an optional embodiment, the real-scene modeling unit 6 is used to perform real-scene three-dimensional modeling of the target prefabricated building through oblique photography technology to obtain a target prefabricated real-scene three-dimensional model, and transmit the target prefabricated real-scene three-dimensional model to the model comparison unit 5.

[0064] It should be noted that oblique photography technology is a three-dimensional modeling technology widely used in geographic information systems (GIS), urban planning, architectural design and other fields. It takes photos of the target object from multiple angles and then uses these photos to generate high-precision three-dimensional models.

[0065] In an optional embodiment, the model comparison unit 5 receives the static BIM three-dimensional model of each stage transmitted by the progress display unit 3 and the target prefabricated real-scene three-dimensional model transmitted by the real-scene modeling unit 6, and compares the static BIM three-dimensional model of each stage and the target prefabricated real-scene three-dimensional model through a model comparison method to obtain a comparison degree between the two, and transmits the comparison degree to the progress tracing unit 7.

[0066] In an optional embodiment, the data collection unit 8 is used to collect production data and transaction circulation data of each link in the supply chain, and to collect progress data and environmental data of each construction link, combine the production data, transaction circulation data, progress data and environmental data to obtain traceability data, and transmit the traceability data to the data management unit 9.

[0067] It should be noted that production data covers information related to production activities in all links of the supply chain, such as production quantity, production date, production location, production equipment status, etc.; transaction circulation data records the transportation and transaction process of products in the supply chain, including transportation route, transportation time, transaction object, transaction time, transaction location, price and other information; progress data refers to the progress of work at different stages of the construction process, such as construction start time, construction end time, completion status of each stage, and staff dynamics; environmental data includes monitoring data of the construction site and surrounding environment, such as air quality, noise level, and soil conditions.

[0068] In an optional embodiment, the data management unit 9 receives the traceability data transmitted by the data acquisition unit 8, and classifies the traceability data to obtain multiple different types of traceability data, and constructs a data mapping table for different types of traceability data, and transmits the data mapping table to the progress tracing unit 7.

[0069] It should be noted that the data mapping table is a structured table formed by classifying and organizing the traceability data, which is used to facilitate data query and analysis by the progress tracing unit 7; the data mapping table includes data type, data identifier, timestamp, data source, data content, related information, deviation level, processing status, responsible person and approval record.

[0070] In an optional embodiment, the progress tracing unit 7 receives the comparison degree transmitted by the model comparison unit 5 and the data mapping table transmitted by the data management unit 9, and compares the comparison degree with a preset contrast threshold. If it is greater than or equal to the preset contrast threshold, the progress of the target prefabricated building is normal; otherwise, the progress of the target prefabricated building is abnormal. The deviation level is matched based on the difference between the comparison degree and the preset contrast threshold, and the deviation level is matched with the data mapping table to obtain the traceability data corresponding to the deviation level, and the traceability data corresponding to the deviation level is sent to the corresponding administrator.

[0071] In a second embodiment, a prefabricated building construction quality tracing system proposed by the present invention is provided. Compared with the first embodiment, this embodiment further includes virtually segmenting the static BIM three-dimensional model based on the consumption of assembly components to obtain a static BIM three-dimensional model corresponding to the consumption of assembly components, including the following steps:

[0072] A1. Based on assembly component information, determine the position and spatial layout of each assembly component in the static BIM 3D model;

[0073] A2. Based on the consumption data of assembly components, the static BIM 3D model is divided according to the position and spatial layout of each assembly component.

[0074] In an optional embodiment, the model comparison method includes the following steps:

[0075] B1. Extract the geometric attribute features of the static BIM 3D model and the target prefabricated real-life 3D model. The geometric attribute features include volume, surface area, and principal moments of inertia. The volume calculation formula is as follows:

[0076]

[0077] Where V represents volume, s i Represents a symbolic variable, D i represents the determinant of the coordinates of the three vertices of each tetrahedron base, and P i1x Represents the x-axis coordinate of the first vertex in the base of the i-th tetrahedron;

[0078] The surface area is calculated as follows:

[0079]

[0080] Where A represents the surface area, P i1 Represents the first vertex of the i-th triangle;

[0081] The formula for calculating the principal moments of inertia is as follows:

[0082]

[0083] Where J represents the principal moment of inertia, V i represents the volume of the i-th tetrahedron, f( ) represents a homogeneous quadratic polynomial;

[0084] B2. Extract the view and internal distance map features of the static BIM 3D model and the target prefabricated real-life 3D model;

[0085] B3. Construct a feature matrix based on geometric attribute features and view and internal distance map features, and calculate the similarity between the feature matrix corresponding to the static BIM 3D model and the feature matrix corresponding to the target prefabricated real-life 3D model using the Canberra distance. The similarity is the comparison between the two.

[0086] It should be noted that the Canberra distance is a measurement method used to calculate the difference between two vectors. It is particularly suitable for situations where there are many zero values ​​in the data or when small value changes need to be emphasized.

[0087] In an optional embodiment, extracting view and internal distance map features of a static BIM 3D model and a target prefabricated real-life 3D model includes the following steps:

[0088] C1. Construct a minimum record matrix, a maximum record matrix, and an update record matrix. The minimum record matrix is ​​used to record the minimum z coordinate of the intersection of the ray emitted by each pixel and all triangles. The maximum record matrix is ​​used to record the maximum z coordinate of the intersection of the ray emitted by each pixel and all triangles. The update record matrix is ​​used to record whether each pixel has been updated.

[0089] C2. Traverse all triangular faces in the static BIM 3D model and the target prefabricated real-life 3D model, find the plane equation of each triangular face, and find its projected triangular face on the xOy plane. If the projected triangular face is perpendicular to the xOy plane, process the next triangular face.

[0090] C3. Calculate the y-coordinate range of the pixel points of the projected triangle on the xOy plane and compare the y-coordinate range with the preset range threshold. If it meets the preset range threshold, abstract the y-coordinate range into a scan line and calculate the x-coordinate range of the pixel points of the projected triangle on the scan line.

[0091] C4. Combine the x-coordinate range and the y-coordinate range to obtain multiple pixel coordinates, calculate the z-coordinate of the intersection of the ray emitted by the pixel coordinate and the triangle, and update the minimum value record matrix, maximum value record matrix, and update record matrix based on the z-coordinate. Repeat steps B2-B4 until all triangles are processed.

[0092] C5. Calculate the view and internal distance map features based on the updated minimum record matrix and maximum record matrix. The calculation formula for the view and internal distance map features is as follows:

[0093]

[0094] Among them, IDM represents the view and internal distance map feature, zBudder2 represents the maximum value record matrix, zBudder1 represents the minimum value record matrix, z max and z min Represents the maximum and minimum values ​​of the z coordinate respectively.

[0095] In an optional embodiment, classifying the traceability data to obtain multiple different types of traceability data includes the following steps:

[0096] D1. Extract the data features of the traceability data to obtain a set of data feature vectors. The data feature vectors are as follows:

[0097] X={x1,x2,...,x i ,...,x n};

[0098] Among them, X represents the data feature vector set, x i represents the i-th data feature vector in the data feature vector set, and x i ={a1s1, a2s2, ..., a i s i ,...,a n s n}, a i Represents the weight coefficient of the i-th sample of the i-th data feature vector, s i Represents the characteristic parameter of the i-th sample of the i-th data feature vector;

[0099] D2. Using the fruit fly algorithm to select the optimal cutoff distance of the density peak clustering algorithm based on the data feature vector set;

[0100] D3. Substitute the optimal cutoff distance into the density peak clustering algorithm to calculate the distance between each sample in the data feature vector set. The distance calculation formula is as follows:

[0101] H ij =dist(x i , x j );

[0102] Among them, H ij Represents the data feature vector set, x jrepresents the jth data feature vector in the data feature vector set, and dist() represents the distance function;

[0103] D4. Calculate the local density of the data feature vector based on the distance between each sample in the data feature vector set. Calculate the relative distance of the data feature vector based on the distance and local density. The local density calculation formula is as follows:

[0104]

[0105] Among them, ρ i represents the local density of the i-th data feature vector in the data feature vector set, λ represents the density kernel function, d best represents the optimal cutoff distance obtained by the fruit fly algorithm;

[0106] The relative distance calculation formula is as follows:

[0107]

[0108] Among them, h i Represents the relative distance, ρ max represents the maximum local density, and min() represents the minimum function;

[0109] D5. Construct a decision diagram based on local density and relative distance, select cluster centers based on the decision diagram, and assign the remaining data feature vector sets to corresponding clusters to obtain multiple different types of traceability data.

[0110] It should be noted that the fruit fly algorithm is a random optimization algorithm based on the foraging behavior of fruit flies. Fruit flies have strong olfactory and visual abilities and can find food through smell and visual clues. The fruit fly algorithm simulates this process and searches for the optimal solution by continuously updating the individual positions in the group. The density peak clustering algorithm is a clustering method based on the density and distance of data points. It assumes that the points in the high-density area are the cluster centers, and these center points have a large distance between each other. The optimal cutoff distance refers to the distance threshold used to determine the neighborhood of the data point in the density peak clustering algorithm. This distance determines which data points are included. Points are considered to be close to each other, which affects the calculation of local density; local density measures the number of data points near a data point and is an important indicator of the density peak clustering algorithm; relative distance refers to the distance from a data point to the nearest point with a higher density than it; the decision diagram is a tool used by the density peak clustering algorithm to select cluster centers. Each point in the diagram represents a data point, the horizontal axis is local density, and the vertical axis is relative distance; the cluster center point refers to the central data point selected as each cluster in cluster analysis. In the density peak clustering algorithm, these center points usually have higher local density and relative distance.

[0111] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A prefabricated building construction quality tracing system, comprising a model comparison unit (5), a real-scene modeling unit (6), a progress tracing unit (7), a data acquisition unit (8) and a data management unit (9), characterized in that: A BIM modeling unit (1), the BIM modeling unit (1) being used to construct a static BIM three-dimensional model of the target prefabricated building using BIM technology according to a design drawing of the target prefabricated building, and to transmit the static BIM three-dimensional model to a virtual segmentation unit (2); a virtual segmentation unit (2), the virtual segmentation unit (2) receiving the static BIM three-dimensional model transmitted by the BIM modeling unit (1), and performing virtual segmentation on the static BIM three-dimensional model based on the assembly component consumption to obtain a static BIM three-dimensional model corresponding to the assembly component consumption, and transmitting the static BIM three-dimensional model corresponding to the assembly component consumption to the progress display unit (3); a delivery recording unit (4), the delivery recording unit (4) being used to take out assembly components from the assembly component warehouse according to the required quantity of assembly components at each stage corresponding to the construction schedule, so as to obtain the delivery quantity of assembly components at each stage, and to transmit the delivery quantity of assembly components at each stage to the progress display unit (3); a progress display unit (3), the progress display unit (3) receiving the assembly component outbound quantity of each stage transmitted by the outbound recording unit (4) and the static BIM three-dimensional model corresponding to the assembly component consumption quantity transmitted by the virtual segmentation unit (2), matching the assembly component outbound quantity of each stage with the static BIM three-dimensional model corresponding to the assembly component consumption quantity to obtain the static BIM three-dimensional model of each stage, and transmitting the static BIM three-dimensional model of each stage to the model comparison unit (5); The real scene modeling unit (6) is used to perform real scene three-dimensional modeling of the target prefabricated building by using an oblique photography technique to obtain a target prefabricated real scene three-dimensional model, and transmit the target prefabricated real scene three-dimensional model to the model comparison unit (5); The model comparison unit (5) receives the static BIM three-dimensional model of each stage transmitted by the progress display unit (3) and the target prefabricated real-scene three-dimensional model transmitted by the real-scene modeling unit (6), and compares the static BIM three-dimensional model of each stage with the target prefabricated real-scene three-dimensional model through a model comparison method to obtain a comparison degree between the two, and transmits the comparison degree to the progress tracing unit (7); The data collection unit (8) is used to collect production data and transaction circulation data of each link of the supply chain, and collect progress data and environmental data of each construction link, combine the production data, the transaction circulation data, the progress data and the environmental data to obtain traceability data, and transmit the traceability data to the data management unit (9); The data management unit (9) receives the traceability data transmitted by the data acquisition unit (8), and classifies the traceability data to obtain a plurality of different types of traceability data, and constructs a data mapping table for the different types of traceability data, and transmits the data mapping table to the progress tracing unit (7).

2. The prefabricated building construction quality tracing system according to claim 1, characterized in that: The progress tracing unit (7) receives the comparison degree transmitted by the model comparison unit (5) and the data mapping table transmitted by the data management unit (9), and compares the comparison degree with a preset contrast threshold value. If the comparison degree is greater than or equal to the preset contrast threshold value, the progress of the target prefabricated building is normal; otherwise, the progress of the target prefabricated building is abnormal. The deviation level is matched based on the difference between the comparison degree and the preset contrast threshold value, and the deviation level is matched with the data mapping table to obtain the traceability data corresponding to the deviation level, and the traceability data corresponding to the deviation level is sent to the corresponding administrator.

3. The prefabricated building construction quality tracing system according to claim 1, characterized in that: Virtually segmenting the static BIM three-dimensional model based on the assembly component consumption to obtain the static BIM three-dimensional model corresponding to the assembly component consumption includes the following steps: A1. Based on the assembly component information, determining the position and spatial layout of each assembly component in the static BIM three-dimensional model; A2. Based on the consumption data of the assembly components, the static BIM three-dimensional model is divided according to the position and spatial layout of each assembly component.

4. The prefabricated building construction quality tracing system according to claim 1, characterized in that: The model comparison method comprises the following steps: B1. Extract geometric attribute features of the static BIM 3D model and the target prefabricated real-scene 3D model. The geometric attribute features include volume, surface area, and principal moments of inertia. The volume calculation formula is as follows: ； in, Indicates volume, represents a symbolic variable, represents the determinant of the coordinates of the three vertices of each tetrahedron base, and , Represents the x-axis coordinate of the first vertex in the base of the i-th tetrahedron; The surface area calculation formula is as follows: ； in, represents the surface area, Represents the first vertex of the i-th triangle; The principal moment of inertia calculation formula is as follows: ； in, represents the principal moments of inertia, represents the volume of the i-th tetrahedron, represents a homogeneous quadratic polynomial; B2. Extracting view and internal distance map features of the static BIM 3D model and the target prefabricated real-scene 3D model; B3. Construct a feature matrix based on the geometric attribute features and the view and internal distance map features, and calculate the similarity between the feature matrix corresponding to the static BIM three-dimensional model and the feature matrix corresponding to the target prefabricated real-life three-dimensional model using Canberra distance. The similarity is the comparison degree between the two.

5. The prefabricated building construction quality tracing system according to claim 1, characterized in that: Extracting view and internal distance map features of the static BIM three-dimensional model and the target prefabricated real-scene three-dimensional model includes the following steps: C1. Construct a minimum value recording matrix, a maximum value recording matrix, and an update recording matrix. The minimum value recording matrix is ​​used to record the minimum z coordinate of the intersection of the ray emitted by each pixel point and all triangular faces. The maximum value recording matrix is ​​used to record the maximum z coordinate of the intersection of the ray emitted by each pixel point and all triangular faces. The update recording matrix is ​​used to record whether each pixel point has been updated. C2. Traverse all triangular faces in the static BIM 3D model and the target prefabricated real-life 3D model, find the plane equation of each triangular face, and find its projected triangular face on the xOy plane. If the projected triangular face is perpendicular to the xOy plane, process the next triangular face. C3. Calculate the y-coordinate range of the pixel points of the projected triangle on the xOy plane, compare the y-coordinate range with a preset range threshold, and if it meets the preset range threshold, abstract the y-coordinate range into a scan line and calculate the x-coordinate range of the pixel points of the projected triangle on the scan line; C4. Combine the x-coordinate range and the y-coordinate range to obtain multiple pixel coordinates, calculate the z-coordinate of the intersection of the ray emitted by the pixel coordinate and the triangular face, and update the minimum value record matrix, the maximum value record matrix, and the update record matrix according to the z-coordinate. Repeat steps B2-B4 until all triangular faces are processed. C5. Calculate the view and interior distance map features based on the updated minimum value record matrix and the maximum value record matrix. The view and interior distance map feature calculation formula is as follows: ； in, Represents view and interior distance map features, represents the maximum value record matrix, represents the minimum value record matrix, and Represents the maximum and minimum values ​​of the z coordinate respectively.

6. The prefabricated building construction quality tracing system according to claim 1, characterized in that: Classifying the traceability data to obtain multiple different types of traceability data includes the following steps: D1. Extract the data features of the traceability data to obtain a set of data feature vectors. The data feature vectors are as follows: ； in, represents the set of data feature vectors, represents the i-th data feature vector in the data feature vector set, and , represents the weight coefficient of the i-th sample of the i-th data feature vector, Represents the characteristic parameter of the i-th sample of the i-th data feature vector; D2. Selecting the optimal cutoff distance of the density peak clustering algorithm using the fruit fly algorithm based on the data feature vector set; D3. Substitute the optimal cutoff distance into the density peak clustering algorithm to calculate the distance between each sample in the data feature vector set. The distance calculation formula is as follows: ； in, represents the set of data feature vectors, represents the jth data feature vector in the data feature vector set, represents the distance function; D4. Calculate the local density of the data feature vector based on the distance between each sample in the data feature vector set, and calculate the relative distance of the data feature vector based on the distance and the local density. The local density calculation formula is as follows: ； in, represents the local density of the i-th data feature vector in the data feature vector set, represents the density kernel function, represents the optimal cutoff distance obtained by the fruit fly algorithm; The relative distance calculation formula is as follows: ； in, Indicates relative distance, represents the maximum local density, represents the minimum function; D5. Construct a decision diagram according to the local density and the relative distance, select cluster centers based on the decision diagram, and assign the remaining data feature vector sets to corresponding clusters to obtain multiple different types of traceability data.

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