Fabricated building construction quality tracing system

Abstract

The invention relates to the technical field of construction management, in particular to a fabricated building construction quality tracing system, which comprises a model comparison unit, a real scene modeling unit, a progress tracing unit, a data acquisition unit and a data management unit, and further comprises a BIM modeling unit, the BIM modeling unit is used for constructing a static BIM three-dimensional model of a target fabricated building through a BIM technology according to a design drawing of the target fabricated building, and transmitting the static BIM three-dimensional model to the virtual segmentation unit. Through comparison of the static BIM three-dimensional model and the live-action three-dimensional model, differences and deviations in the assembly process can be detected, problems can be found in time, correction measures are taken, and therefore the precision and quality of building assembly are improved, and through combination of the consumption amount, the warehouse-out amount and the actual construction progress data of the assembly components, the construction efficiency is improved. The construction progress can be accurately tracked and managed so as to ensure that a project is carried out according to plans.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to a system for tracing the construction quality of assembled buildings. 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, and the on-site work is mainly assembly and connection.

[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 islands and a lack of a comprehensive perspective for project management. In addition, traditional systems often lag behind in data monitoring and feedback on the construction process, making it difficult to promptly discover and deal with differences and deviations in the assembly process, leading to the accumulation of problems and affecting the quality and progress of the project. 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 are difficult to ensure the accuracy and completeness of data. In addition, traditional systems find it difficult to accurately track and manage construction progress in combination with the consumption, outbound volume and actual construction progress data of assembly components, resulting in insufficiently refined progress management and difficulty in promptly discovering and correcting progress deviations. Summary of the invention

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

[0005] The technical solution of the present invention is: a prefabricated building construction quality tracing system, including a model comparison unit, a real scene modeling unit, a progress tracing unit, a data collection unit and a data management unit, and also includes:

[0006] A BIM modeling unit, wherein the BIM modeling unit 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 by using BIM technology, 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 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;

[0008] A delivery recording unit, which 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 delivery quantity of assembly components at each stage, and transmit the delivery quantity of assembly components at each stage to the progress display unit;

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

[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 of the supply chain, and to collect progress data and environmental data of each link of the construction, and to 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 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.

[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 the comparison degree 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, determine the position and spatial layout of each assembly component in the static BIM three-dimensional model;

[0017] A2. According to 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 moment of inertia. The volume calculation formula is as follows:

[0020]

[0021] Where V represents volume, s i Denotes 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 the view and internal distance map features of the static BIM three-dimensional model and the target prefabricated real-scene three-dimensional 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 through Canberra distance, wherein 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 value of the z coordinate of the intersection of the ray emitted by each pixel point and all the triangular faces. The maximum value recording matrix is ​​used to record the maximum value of the z coordinate of the intersection of the ray emitted by each pixel point and all the 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-scene 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, finding the y coordinate range of the pixel points of the projected triangle on the xOy plane, comparing the y coordinate range with a preset range threshold, and if it meets the preset range threshold, abstracting the y coordinate range into a scan line, and finding the x coordinate range of the pixel points of the projected triangle on the scan line;

[0034] C4, combining the x-coordinate range and the y-coordinate range to obtain a plurality of pixel point coordinates, calculating the z-coordinate of the intersection of the ray emitted by the pixel point coordinates and the triangular face, and updating the minimum value record matrix, the maximum value record matrix and the update record matrix according to the z-coordinate, and repeating steps B2-B4 until all triangular faces are processed;

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

[0036]

[0037] Among them, IDM represents the view and internal distance map features, zBudder2 represents the maximum value record matrix, zBudder1 represents the minimum value record matrix, and z max and z min Represent 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, substituting 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, 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, and 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 center points based on the decision diagram, and assign and classify the remaining data feature vector sets into 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 3D model with the real-life 3D model, the present invention can detect differences and deviations in the assembly process, discover problems in time and take corrective measures, thereby improving the accuracy and quality of building assembly; by combining the consumption of assembly components, the quantity shipped out of the warehouse and the actual construction progress data, 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 problems, 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 A schematic diagram of the overall system flow in an 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 collection unit; 9. Data management unit. DETAILED DESCRIPTION

[0057] Embodiment 1, as Figure 1 As shown, a prefabricated building construction quality tracing system proposed by the present invention includes a model comparison unit 5, a real scene modeling unit 6, a progress tracing unit 7, a data collection 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 through BIM technology, and transmit the static BIM three-dimensional model to the virtual segmentation unit 2;

[0059] A 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 recording unit 4 and the assembly component consumption 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 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 the present invention, BIM technology is a building design, construction and management technology based on three-dimensional digital models. It integrates various information of construction projects to provide an information platform for the entire life cycle, from design, construction to operation and maintenance, to achieve information sharing and collaborative work; assembly components refer to building components that are pre-made in factories or other places in construction, and these components are assembled and installed on site. Assembly components can greatly improve construction efficiency, quality and safety, and reduce 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 3D 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 3D 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 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.

[0066] In an optional embodiment, the data collection unit 8 is used to collect production data and transaction circulation data of each link of the supply chain, and to collect progress data and environmental data of each link of the construction, 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 the progress tracing unit 7 to perform data query and analysis; 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 the comparison degree 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] Embodiment 2, a prefabricated building construction quality tracing system proposed by the present invention, compared with embodiment 1, this embodiment further includes virtually segmenting the static BIM three-dimensional model based on the consumption of assembly components to obtain the static BIM three-dimensional model corresponding to the consumption of assembly components, including the following steps:

[0072] A1. Based on the 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 comprises the following steps:

[0075] B1. Extract the 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 moment of inertia. The volume calculation formula is as follows:

[0076]

[0077] Where V represents volume, s i Denotes 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 calculation formula is as follows:

[0079]

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

[0081] The principal moment of inertia is calculated 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-scene 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 through the Canberra distance. The similarity is the comparison degree 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 the view and internal distance map features of the static BIM 3D model and the target prefabricated real-scene 3D model includes the following steps:

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

[0089] C2. Traverse all triangular faces in the static BIM 3D model and the target prefabricated real-scene 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, the y coordinate range is abstracted as a scan line, and the x coordinate range of the pixel points of the projected triangle on the scan line is calculated;

[0091] C4, combining the x coordinate range and the y coordinate range to obtain multiple pixel point coordinates, calculating the z coordinate of the intersection of the ray emitted by the pixel point coordinates and the triangle surface, and updating the minimum value record matrix, the maximum value record matrix and the update record matrix according to the z coordinate, and repeating steps B2-B4 until all triangle surfaces are processed;

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

[0093]

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

[0095] In an optional embodiment, classifying the traceability data to obtain a plurality of 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, 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, 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:

[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, and 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 sense of smell and vision, and can find food through smell and visual clues. The fruit fly algorithm simulates this process and finds the optimal solution by continuously updating the individual positions in the group; the density peak clustering algorithm is a clustering method based on data point density and distance. 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 data points in the density peak clustering algorithm. This distance determines which data points are clustered. 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 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 are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose 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 collection 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 according to the design drawings of the target prefabricated building by using BIM technology, and to transmit the static BIM three-dimensional model to the virtual segmentation unit (2); A virtual segmentation unit (2), the virtual segmentation unit (2) receives the static BIM three-dimensional model transmitted by the BIM modeling unit (1), and performs 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 transmits the static BIM three-dimensional model corresponding to the assembly component consumption to a progress display unit (3); a warehouse-out recording unit (4), the warehouse-out 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 warehouse-out quantity of assembly components at each stage, and to transmit the warehouse-out quantity of assembly components at each stage to the progress display unit (3); A progress display unit (3) receives the static BIM three-dimensional model corresponding to the assembly component outgoing quantity at each stage transmitted by the outgoing record unit (4) and the assembly component consumption quantity transmitted by the virtual segmentation unit (2), matches the assembly component outgoing quantity at each stage with the static BIM three-dimensional model corresponding to the assembly component consumption quantity to obtain the static BIM three-dimensional model at each stage, and transmits the static BIM three-dimensional model at each stage to the model comparison unit (5).

2. The prefabricated building construction quality tracing system according to claim 1 is characterized in that: The real-scene modeling unit (6) is used to perform real-scene three-dimensional modeling on 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).

3. The assembly-type building construction quality tracing system according to claim 2 is characterized in that: 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 link of the construction, 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 collection 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).

4. The prefabricated building construction quality tracing system according to claim 3 is 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.

5. 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 consumption of assembly components to obtain the static BIM three-dimensional model corresponding to the consumption of assembly components includes the following steps: A1. Based on the assembly component information, determine the position and spatial layout of each assembly component in the static BIM three-dimensional model; A2. According to 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.

6. 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 moment of inertia. The volume calculation formula is as follows: Where V represents volume, s i Denotes 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; The surface area calculation formula is as follows: Where A represents the surface area, P i1 Represents the first vertex of the i-th triangle; The principal moment of inertia calculation formula is as follows: Where J represents the principal moment of inertia, V i represents the volume of the i-th tetrahedron, and f() represents a homogeneous quadratic polynomial; B2. Extracting the view and internal distance map features of the static BIM three-dimensional model and the target prefabricated real-scene three-dimensional 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 through Canberra distance, wherein the similarity is the comparison degree between the two.

7. The prefabricated building construction quality tracing system according to claim 1, characterized in that: Extracting the 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 value of the z coordinate of the intersection of the ray emitted by each pixel point and all the triangular faces. The maximum value recording matrix is ​​used to record the maximum value of the z coordinate of the intersection of the ray emitted by each pixel point and all the 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-scene 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, finding the y coordinate range of the pixel points of the projected triangle on the xOy plane, comparing the y coordinate range with a preset range threshold, and if it meets the preset range threshold, abstracting the y coordinate range into a scan line, and finding the x coordinate range of the pixel points of the projected triangle on the scan line; C4, combining the x-coordinate range and the y-coordinate range to obtain a plurality of pixel point coordinates, calculating the z-coordinate of the intersection of the ray emitted by the pixel point coordinates and the triangular face, and updating the minimum value record matrix, the maximum value record matrix and the update record matrix according to the z-coordinate, and repeating steps B2-B4 until all triangular faces are processed; C5. Calculate the view and internal distance map features based on the updated minimum value record matrix and the maximum value record matrix. The view and internal distance map feature calculation formula is as follows: Among them, IDM represents the view and internal distance map features, zBudder2 represents the maximum value record matrix, zBudder1 represents the minimum value record matrix, and z max and z min Represent the maximum and minimum values ​​of the z coordinate respectively.

8. The prefabricated building construction quality tracing system according to claim 1, characterized in that: Classifying the traceability data to obtain a plurality of 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: X={x1,x2,...,x i ,...,x n ): 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; 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, substituting 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: H ij =dist(x i ,x j ); 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; 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: 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, and d best Represents the optimal cutoff distance obtained by the fruit fly algorithm; The relative distance calculation formula is as follows: Among them, h i represents the relative distance, ρ max represents the maximum local density, and min() represents the minimum function; D5. Construct a decision diagram according to the local density and the relative distance, select cluster center points based on the decision diagram, and assign and classify the remaining data feature vector sets into corresponding clusters to obtain multiple different types of traceability data.

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