Method and system for detecting quality of building component

Abstract

The application relates to the technical field of building component detection and provides a building component quality detection method and system. The method comprises the following steps: constructing an M-layer assembled component model group based on construction information; after a first-layer building is formed according to a first-layer model entity ID construction, a real-time model is generated through point cloud scanning; P defects are positioned through space projection comparison of the first-layer model; after dynamic compensation, W defects are positioned through comparison of a second-layer model construction; and the assembled construction and quality detection compensation are recursively executed until the quality detection of the M-layer building component is completed. The application solves the technical problems that the traditional building component quality detection relies on manual measurement, real-time quality monitoring and dynamic compensation cannot be realized layer by layer, defects are accumulated and the detection efficiency is low, and the technical effects that the automatic identification of component quality defects is realized through real-time comparison of a three-dimensional point cloud model, quality defects are dynamically compensated through recursive iteration, and the accuracy and efficiency of building component quality detection are improved.

Description

Technical Field

[0001] The present application relates to the technical field of building component detection, and in particular to a method and system for detecting the quality of building components. Background Art

[0002] With the rapid industrialization of prefabricated buildings, component precision and construction quality are directly linked to overall building safety and project efficiency. Traditional cast-in-place construction methods are gradually being replaced by prefabrication. While this has increased construction speed, it has also introduced new quality control challenges: component production errors and on-site assembly deviations can easily accumulate and amplify during floor-by-floor construction. Existing quality inspections primarily rely on manual steel tape measurements and post-completion overall scanning, which present significant limitations. Manual inspections are inefficient and unable to capture millimeter-level deformations. While overall scanning can generate high-precision point cloud models, it only reveals problems after completion, leading to significantly increased rework costs. Furthermore, while BIM technology has been used to guide component modeling, it lacks real-time, closed-loop feedback with the construction process. This is particularly true for renovation projects, where existing building as-built drawings deviate significantly from actual on-site conditions, making accurate reverse modeling difficult using traditional methods. Summary of the Invention

[0003] This application provides a building component quality inspection method and system, aiming to solve the technical problem that traditional building component quality inspection relies on manual measurement and cannot achieve real-time quality monitoring and dynamic compensation layer by layer, resulting in defect accumulation and low inspection efficiency.

[0004] In a first aspect disclosed in the present application, a method for inspecting the quality of building components is provided, the method comprising: performing hierarchical modeling of a building structure based on information of a building to be constructed to obtain an M-layer assembled component model group; after invoking assembled component construction to obtain a first-layer assembled building based on the entity ID of the first-layer assembled component model group, performing point cloud scanning and splicing on the first-layer assembled building to obtain a first-layer real-time building model; spatially projecting the first-layer real-time building model onto the first-layer assembled component model group to perform component deviation inspection to identify and locate P component quality defect features; after dynamically compensating for the P component quality defect features, constructing a second-layer assembled building based on the second-layer assembled component model group on the first-layer assembled building; using the second-layer assembled component model group as a reference, performing component quality deviation inspection on the second-layer assembled building to identify and locate W component quality defect features; and recursively iterating assembled construction and component assembly quality inspection compensation based on the M-layer assembled component model group until the component construction quality inspection of the M-layer assembled building is completed.

[0005] Another aspect disclosed herein provides a building component quality inspection system, comprising: a hierarchical modeling module for performing hierarchical building structure modeling based on information of a building to be constructed to obtain an M-layer assembled component model group; a point cloud scanning and splicing module for invoking prefabricated component construction based on the entity ID of the first-layer assembled component model group to obtain a first-layer prefabricated building, and then performing point cloud scanning and splicing on the first-layer prefabricated building to obtain a first-layer real-time building model; a first deviation detection module for performing component deviation detection on the spatial projection of the first-layer real-time building model and overlapping it with the first-layer assembled component model group to identify and locate P component quality defect features; a component construction module for dynamically compensating for the P component quality defect features and then constructing a second-layer prefabricated building on the first-layer prefabricated building based on the second-layer assembled component model group; a second deviation detection module for performing component quality deviation detection on the second-layer prefabricated building based on the second-layer assembled component model group to identify and locate W component quality defect features; and a quality inspection module for performing recursive iterations of prefabricated construction and component assembly quality inspection compensation based on the M-layer assembled component model group until component construction quality inspection of the M-layer prefabricated building is completed.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The above-mentioned building component quality inspection method first performs structural modeling based on the design information of the building to be constructed, generating a building structural model comprising M layers of assembled component models. Subsequently, prefabricated component construction is performed based on the entity IDs of the first-layer assembled component models. Point cloud scanning and splicing of the first-layer building are performed to obtain a real-time building model of the first layer. The first-layer real-time building model is then spatially overlapped with the assembled component model to detect quality defects in P components and dynamically compensate for them. After compensation, the second layer is assembled based on the compensated first-layer building model and the second-layer assembled component model group. Quality deviation detection is then performed on the second layer, identifying and locating W defect features. This process is recursively iterated, continuously performing assembly construction and quality inspection based on the M-layer assembled component model group, ensuring that the component quality of each layer meets standards. Ultimately, quality inspection of the entire building is completed, effectively enabling real-time quality monitoring and precise compensation in prefabricated building construction.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 Schematic diagram of a flow chart of a building component quality inspection method in one embodiment.

[0011] Figure 2 2 is an architecture diagram of a building component quality inspection system in one embodiment.

[0012] Explanation of the accompanying drawings: hierarchical modeling module 11, point cloud scanning and stitching module 12, first deviation detection module 13, component construction module 14, second deviation detection module 15, quality detection module 16. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a building component quality inspection method and system to solve the technical problem that traditional building component quality inspection relies on manual measurement and cannot achieve real-time quality monitoring and dynamic compensation layer by layer, resulting in defect accumulation and low inspection efficiency.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides a method for detecting the quality of building components, the method comprising:

[0017] The building structure hierarchical modeling is performed based on the information of the building to be constructed, and an M-layer assembled component model group is obtained.

[0018] In an embodiment of the present application, the structural code and construction parameters of the building to be constructed are first used as dual search conditions to accurately retrieve the specific information of the building to be constructed from the local database. Subsequently, based on the defect transmission and amplification characteristics, a decimal structural coding system is used to hierarchically decompose the prefabricated building model, wherein the last three digits of the code respectively identify the component type, floor affiliation and spatial partition information. For example, in the code "302-05-F12", "302" represents prefabricated wall panel components, "05" represents the fifth floor, and "F12" corresponds to the twelfth span spatial position in the east district. Through this coding rule, the overall building model can be disassembled into M independent assembly layers, each layer containing several standardized component models. Since there is no structural contact or mechanical interaction between the components of each layer in space, multi-floor synchronous operation can be achieved during the construction process. Afterwards, in the BIM platform, the building topology framework is automatically constructed according to the hierarchical relationship of the component coding, and the standardized component models are mapped to the corresponding positions according to the spatial coordinates, and finally an M-layer assembly component model group with installation coordinate identification is generated. This hierarchical modeling method not only improves the assembly accuracy of the model, but also realizes parallel optimization of construction processes through spatial decoupling, thereby improving overall construction efficiency.

[0019] Furthermore, the present application provides a method for performing building structure hierarchical modeling based on information of a building to be constructed, obtaining an M-layer assembled component model group, the method comprising:

[0020] The structural code and construction parameters of the building to be constructed are used as dual search conditions to locally retrieve and call the information of the building to be constructed; the prefabricated building model is modeled and restored based on the information of the building to be constructed; based on the defect transmission and amplification characteristics, the prefabricated building model is hierarchically decomposed to obtain an M-layer assembled component model group, wherein each prefabricated component model has an installation space coordinate identifier.

[0021] Preferably, at the beginning of a construction project, the structural code and construction parameters of the building to be constructed are first obtained. The structural code is a unique identifier for each component in the building. It is encoded according to a certain hierarchical structure to ensure that the position, function, and relationship of each component in the entire building system are clearly identified. The construction parameters include information such as component size, material, and location requirements. Subsequently, the obtained structural code and construction parameters are used as dual search criteria to search the local database, quickly locating various data related to the building, such as the overall layout of the building structure, the positional relationship of components on each floor, material selection, dimensional accuracy requirements, etc. By summarizing the retrieved information, the information of the building to be constructed is generated. This information includes the overall structural design of the building and the detailed construction requirements of each component, providing a foundation for subsequent modeling work. After the information retrieval is completed, the modeling of the prefabricated building is carried out based on this retrieved data. Specifically, the retrieved information is matched to a standardized component model library, and the matched model is then mapped to the corresponding space to restore a complete prefabricated building model. During the modeling process, each component is assigned accurate spatial coordinates, which can ensure that all components match the overall building design and ensure the accuracy and consistency of the prefabricated building model. Afterwards, the restored prefabricated building model is hierarchically decomposed according to the defect transmission amplification characteristic. This defect transmission amplification characteristic means that if a component has a quality defect, it may affect the quality and stability of the entire building. Therefore, based on the structural hierarchy of the building, the prefabricated building model will be decomposed into multiple levels. For example, the building model will be divided according to floors or functional areas, and then the components of each floor of the building will be divided. For the divided assembled component models, the spatial coordinate identification of each component will be marked, and the assembly component models with annotations will be summarized to form an M-layer assembly component model group. The assembly component models of each layer will have a clear spatial positioning and identification. These models will be used for assembly construction to ensure that the installation of each component meets the design requirements and meets the overall stability and quality requirements of the building.

[0022] Furthermore, the present application provides a method for restoring an assembled building model based on the information modeling of the building to be constructed, the method comprising:

[0023] A decimal structure code set is extracted from the information of the building to be constructed, wherein the decimal structure code set is constrained by a component code hierarchical relationship; a building topology structure framework is constructed on a BIM platform according to the component code hierarchical relationship; after matching a plurality of standardized component models based on the decimal structure code set, the prefabricated building model is assembled and restored by mapping the plurality of standardized component models to the building topology structure framework.

[0024] Optionally, first, the structural codes of all components are extracted from the information of the building to be constructed to form a decimal structural code set, where the structural code is usually expressed in decimal, and this coding method is strictly designed according to the hierarchical relationship of the building's components and is unique. For example, each component in the building (such as walls, beams, columns, etc.) will be coded according to its location, function and type. Subsequently, on the BIM platform, a digital building structure framework is constructed based on the hierarchical relationship of the component codes. Each component is assigned a corresponding spatial position and connection attributes on the platform to form the overall topological structure of the building. This building topological structure framework not only includes the geometric form of the components, but also involves the relative position, connection method and construction requirements between the components. Through the BIM platform, the construction team can intuitively view the overall structure of the building, and can also clearly understand the relationship between the various components. Next, the corresponding standardized component models are matched from a standardized component model library based on the decimal structural code set. This library, pre-established according to building design and construction standards, typically includes standardized 3D models of common building components such as walls, columns, doors, and windows. These standardized 3D models include information such as component geometry, material properties, and dimensional requirements. The resulting multiple standardized component models are then mapped into the constructed building topology framework. During the mapping process, the standardized component models are automatically mapped to designated locations based on the spatial positioning requirements of the topology framework, ensuring that they are correctly aligned in space and that relative positions between components are not misaligned. Once mapped, these standardized component models form a complete prefabricated building model. In this model, the connection relationships, assembly sequence, and construction details between all components are clearly labeled. This model allows the construction team to efficiently carry out construction and conduct quality inspections and defect compensation during subsequent construction, ensuring the efficiency, accuracy, and quality of the prefabricated building.

[0025] Furthermore, the present application provides a method for assembling and restoring the prefabricated building model by mapping the plurality of standardized component models to the building topology framework after matching. The method includes:

[0026] Based on the last three digits of the decimal structure code set, multiple type identifiers, multiple floor affiliation codes, and multiple space partition codes of multiple building components are decomposed and obtained; based on the multiple type identifiers, the multiple standardized component models are called in a standardized component model library; the multiple floor affiliation codes and multiple space partition code features are mapped into multiple installation space coordinates, and the multiple standardized component models are automatically assembled in the building topology structure framework to restore the prefabricated building model.

[0027] Optionally, the last three digits of each decimal structure code are extracted from the decimal structure code set of building components. Each part of the decimal structure code represents the component information at different levels in the building. The last three digits of the identifier usually contain information such as the type, floor affiliation, and spatial location of the component. By decomposing the decimal structure codes of multiple building components, multiple type identifiers, multiple floor affiliation codes, and multiple space partition codes can be obtained. Among them, the type identifier is used to distinguish different types of components to ensure that the function and properties of each component can be clearly identified; the floor affiliation code specifies the floor where the component is located, which can ensure that each component in a multi-story building can be correctly mapped to the corresponding floor; the space partition code refers to the specific location of the component in the building space, which determines the actual installation location of the component on each floor. Subsequently, based on the extracted type identifier, a query is performed in the standardized component model library to find the standardized component models corresponding to these type identifiers, such as wall models, window models, etc. These models already have preset sizes, shapes, material properties, etc., and can be directly used to construct the digital model of the prefabricated building. Afterwards, the specific location of the component in the building is defined based on the extracted floor code and space partition code. For example, the floor code ensures that the component is located on the correct floor, while the space partition code further determines the specific location of the component in the floor, such as the coordinates of a room or an area. By combining these two types of information, the installation space coordinates of each component are generated. The installation space coordinates usually include x, y, and z coordinates to represent the precise position of the component in three-dimensional space. Finally, on the BIM platform, the standardized component model will be automatically assembled to the spatial coordinate position specified in the building topology framework. After the assembly is completed, the complete prefabricated building model will be restored. This prefabricated building model can improve the design and construction efficiency of prefabricated buildings and ensure the accurate installation and quality control of components.

[0028] Table 1: Example table of the last three digits of identification features

[0029]

[0030] As shown above, Table 1 is an example table of the last three digits of identification features. The table shows different types of prefabricated building components, floor numbers, and spatial location codes, which facilitates accurate positioning and management of the construction and quality control of various components.

[0031] According to the entity ID of the first-layer assembled component model group, after invoking the assembled component construction to obtain the first-layer assembled building, point cloud scanning and splicing are performed on the first-layer assembled building to obtain the first-layer real-time building model.

[0032] In one embodiment, the relevant component information is first retrieved from a database based on the entity ID of the first-layer assembled component model group within the M-layer assembled component model group. Each assembled component model has a unique entity ID that corresponds one-to-one with the actual component being assembled during construction. These entity IDs enable accurate access to the construction plan and parameters for the assembled components, providing the necessary data support for the assembly and construction of the first-layer building. Subsequently, the construction team conducts the actual construction of the first-layer prefabricated building based on the information from these component models. Prefabricated building components are typically prefabricated and can be quickly assembled according to design drawings. During the assembly process, construction workers install each prefabricated component one by one according to the parameters provided in the component model, such as size, position, and connection method, ensuring that each component is accurately positioned. Once the first-layer prefabricated building is installed, laser scanning technology (such as LiDAR) is used to collect three-dimensional data of the building's surface and space, generating high-precision point cloud data. This point cloud data reflects the actual state of the first-layer prefabricated building, including information such as the actual position, size, and geometry of the components. Afterwards, the scanned point cloud data is spliced ​​and processed using 3D modeling software to synthesize a complete 3D point cloud model, which is used as the first-layer real-time building model. This first-layer real-time building model is constructed based on real-time data from the actual construction site and can accurately reflect the current status of the building. It is used to verify whether the construction of the first layer of the building meets the design requirements and provide data basis for subsequent quality inspections.

[0033] The spatial projection of the first-layer real-time building model is overlapped onto the first-layer assembled component model group to perform component deviation detection and identify and locate P component quality defect features.

[0034] In one embodiment, after completing the construction and point cloud scanning of the first-level prefabricated building, the acquired real-time building model and the first-level assembled component model group are subjected to component deviation detection. During this process, quality issues that may have occurred during component installation are identified and located by comparing component normal vectors, plane distances, crack widths, and other features. After identification, the defect features in the results are retained and combined with the specific deviation data to form P component quality defect features, providing a basis for subsequent quality compensation.

[0035] Furthermore, the present application provides a method for overlaying the spatial projection of the first-layer real-time building model onto the first-layer assembled component model group to perform component deviation detection and identify and locate P component quality defect features. The method includes:

[0036] According to the N component entity IDs of the N standardized component models in the first-layer assembled component model group, N prefabricated components are called; using the N floor affiliation codes and the N space partition codes as assembly construction constraints, the N prefabricated components are physically assembled to obtain the first-layer prefabricated building; point cloud scanning and splicing processing are performed on the N prefabricated components in the assembled state in the first-layer prefabricated building to obtain the first-layer real-time building model, wherein the first-layer real-time building model includes N real-time component models; using the N floor affiliation codes and the N space partition codes as projection overlap constraints, after performing spatial projection of the N standardized component models and the N real-time component models, component deviation detection is performed to identify and locate the quality defect characteristics of the P components.

[0037] Preferably, N standardized component models are first obtained from the first-layer assembled component model group, and the component entity ID of each standardized component model is extracted. These component entity IDs are used to associate with actual components during the construction phase. By comparing these component entity IDs with the IDs in the component library, the corresponding N actual prefabricated components are retrieved. Subsequently, construction constraints are imposed on the prefabricated components based on N floor affiliation codes and N spatial partition codes. Each prefabricated component has a specific floor affiliation code and spatial partition code. This information determines the specific level and position of each component in the building, ensuring that the prefabricated components are installed according to the design requirements. Based on these constraints, construction workers will install these components one by one according to the component entity ID to the corresponding floor and spatial area, completing the construction of the first-layer prefabricated building. Afterwards, the point cloud data acquired by laser scanning technology is spliced ​​to form a complete point cloud dataset representing the real-time state of the building. This point cloud dataset is then input into 3D modeling software for simulation processing to generate the corresponding first-layer real-time building model. This first-layer real-time building model includes N real-time component models, which reflect the precise position and state of each prefabricated component after actual construction. Then, the N floor attribution codes and N space partition codes are used as projection overlap constraints, and the positions of the standardized component model and the real-time component model in space are matched according to the constraints, so that each standardized component model and its corresponding real-time component model are projected to the corresponding spatial position to ensure that their positions in three-dimensional space are consistent. Finally, the component deviation identification container is activated, and the component deviation identification container is used to compare the differences between the standardized component model and the real-time component model in four aspects: normal vector angle, plane distance, crack width, and hole size, to identify the deviation of each component. After the deviation detection is completed, the quality defect characteristics of P components will be located. These defects may include normal vector angle deviation defects, plane distance deviation defects, etc. In summary, through the above steps, the quality defects of components can be discovered and located in a timely manner, ensuring that the component quality of each floor of the prefabricated building meets the design requirements.

[0038] Furthermore, the present application provides a method for using the N floor attribution codes and N space partition codes as projection overlap constraints, performing spatial projection of the N standardized component models and the N real-time component models, performing component deviation detection, and identifying and locating the P component quality defect characteristics. The method includes:

[0039] Pre-activate N component deviation identification containers; load the spatial projections of the N standardized component models and the N real-time component models into the N component deviation identification containers to perform multi-threaded deviation defect identification, and output normal vector angle deviation defects, plane distance deviation defects, crack width defects, and hole size defects; retain the defect space characteristics of the normal vector angle deviation defects, plane distance deviation defects, crack width defects, and hole size defects, and output the P component quality defect features.

[0040] Optionally, before component quality inspection, N pre-built component deviation identification containers are activated. These containers consist of multiple computational models used to store and process component deviation data. Each container corresponds to a component and includes multiple deviation identification tasks. Subsequently, the spatial data of the projected N standardized component models and N real-time component models are loaded into these containers. Each container stores all the corresponding component data, including its spatial coordinates, geometric properties, and projected information. These containers then perform multi-threaded deviation defect detection based on their internal algorithms for multiple deviation identification tasks. This process includes identifying normal vector angle deviations (i.e., checking the angular deviation of the component surface normal vector); plane distance deviations (i.e., calculating the distance difference between the component surface and the design plane); crack width defects (i.e., checking for cracks and measuring their width); and hole size defects (i.e., detecting the presence of holes and calculating their size). These defect detection tasks are performed in parallel using multiple threads, ensuring that deviation identification tasks for multiple components can be processed simultaneously, improving inspection efficiency. After completing the deviation detection, the spatial characteristics of each defect will be retained, including the spatial location of the defect, the defect type (such as cracks, holes, angular deviation, etc.), and the deviation amount of the defect (such as angle difference, distance difference, width, size, etc.), thereby forming the quality defect characteristics of P components for subsequent quality control and repair work to ensure that all components can be adjusted according to design requirements and meet the expected quality standards.

[0041] Furthermore, the present application provides that the component deviation identification container is pre-configured with multi-threaded deviation identification tasks, and the multi-threaded deviation identification tasks include normal vector angle deviation identification tasks, plane distance deviation identification tasks, crack width identification tasks and hole size identification tasks.

[0042] Optionally, multi-threaded deviation recognition tasks are pre-configured in the component deviation recognition container, including normal vector angle deviation recognition tasks, plane distance deviation recognition tasks, crack width recognition tasks, and hole size recognition tasks. These tasks are highly parallelized and can be executed in parallel at the same time to improve detection efficiency and shorten the overall detection time. For the normal vector angle deviation recognition task, this task is used to detect the angular deviation between the component surface normal vector and the design standard. The normal vector describes the direction of the component surface. Any installation error or deformation will cause the angle of the normal vector to change. During the recognition process, geometric calculations will be used to calculate the corresponding normal vectors for the standardized component model and the real-time component model, and then the allowable error formula will be used. Calculate the error between the two normal vectors, where To allow error, is the radius of the fitted surface, The cosine value of the angular deviation of the surface normal vector is fitted. When the allowable error exceeds the set threshold, it indicates a large error. For the plane distance deviation identification task, this task is used to check the distance difference between the component surface and the design standard plane. It can identify surface unevenness caused by inaccurate construction or component deformation. During the identification process, the RANSAC algorithm is used to fit the point cloud plane of the real-time component model. The equation parameters of the corresponding plane are then obtained from the standardized component model. The plane distance deviation is measured by calculating the distance from the actual plane to the standard plane. If the plane distance exceeds the preset allowable error, it indicates a large error. For the crack width identification task, this task is used to identify cracks on the component surface and calculate the crack width. Cracks are a very important factor in building quality inspection. The presence of cracks may affect the structural strength of the component. During the identification process, the Canny algorithm is used to identify crack edges based on the point cloud density gradient. The Euclidean distance between the edge point sets is then calculated and compared with the crack standard to determine whether a crack defect exists. If so, the location is marked and the crack width at that location is recorded. For the hole size recognition task, this task is used to detect whether there are holes on the surface of the component and calculate the size of the hole. During the recognition process, the Hough circle detection method is used to identify the hole contour point cloud to determine whether there is a hole defect. The actual aperture size is then fitted using the minimum circumscribed circle algorithm, and the hole diameter is recorded. All deviation recognition tasks are executed in parallel in a multi-threaded environment. Each thread is responsible for processing the specific detection tasks of one or more components, including calculating normal vector angle deviation, plane distance deviation, crack width, and hole size. The use of multi-threading greatly improves the efficiency of task execution, especially when processing large amounts of component data. It can significantly shorten the total calculation time, thereby timely identifying and locating defect features.

[0043] After dynamically compensating for the P component quality defect characteristics, a second-layer prefabricated building is constructed on the first-layer prefabricated building based on the second-layer assembled component model group; based on the second-layer assembled component model group, component quality deviation detection is performed on the second-layer prefabricated building to identify and locate W component quality defect characteristics; based on the M-layer assembled component model group, recursive iterations of prefabricated construction and component assembly quality detection and compensation are performed until the component construction quality detection of the M-th layer prefabricated building is completed.

[0044] In one embodiment, after P component quality defect characteristics are identified, these P component quality defect characteristics are analyzed according to a compensation rule model to determine the required defect compensation strategy. The position, angle, and dimensions of these components are then adjusted based on these defect compensation strategies to ensure they are restored to a state that meets design requirements. After completing quality compensation for the first-story prefabricated building, construction is carried out based on the second-story prefabricated component model set, based on the compensated first-story building. During construction, the second-story prefabricated components are installed according to the specified positions, dimensions, and connection methods in the model, ensuring that the interfaces and connections with the first-story prefabricated building meet the requirements. Once the second-story prefabricated building is completed, component quality deviation detection is performed using the second-story prefabricated component model set as a benchmark. This step is similar to the first-story detection. By comparing the deviations between the actual second-story components installed and the standard model, W component quality defect characteristics can be identified and their specific deviation values ​​recorded for subsequent quality compensation or adjustments. As quality inspections proceed, a recursive and iterative process of prefabricated construction and component assembly quality inspection and compensation for the entire building will be executed based on the M-layer prefabricated component model group. That is, after each layer of prefabricated construction is completed, the quality defects of the components on that layer will be identified and located by comparing the standard model with the actual construction situation. Then, based on the results of the deviation inspection, dynamic compensation will be performed on each layer of components to ensure that the position, size, and shape of all components meet the design requirements. This process will be recursively iterated based on the quality inspection and compensation results of each layer of building until the component construction quality inspection of all M layers of prefabricated buildings is completed. This recursive and iterative quality inspection and compensation process ensures that each layer of components can receive precise quality control during the construction process, and through layer-by-layer corrections, ultimately achieves high-quality and precise construction of the entire building, thereby improving the construction accuracy of prefabricated buildings, reducing problems caused by quality defects, and thus ensuring the safety and durability of the building.

[0045] Furthermore, the method further comprises:

[0046] Interactively obtain multi-scale defect compensation strategies for multiple sample component defects, and complete the local configuration of the compensation rule model by associating and storing the multiple sample component defects and the multi-scale defect compensation strategies; input the P component quality defect characteristics into the compensation rule model to obtain P associated defect compensation strategies; perform priority merging of the P associated defect compensation strategies according to the P spatial positions of the P component quality defect characteristics to obtain M updated defect compensation strategies; and perform spatial construction dynamic compensation for the P component quality defect characteristics by executing the M updated defect compensation strategies.

[0047] Preferably, during the component quality inspection process, the system first interacts with the historical database to collect defect data of various sample components and corresponding multi-scale defect compensation strategies. These sample components represent various types of quality defects that may occur during the construction process, such as dimensional deviations, holes, cracks, etc. Each multi-scale defect compensation strategy corresponds to a specific type of component defect and provides different correction methods for it. For example, for dimensional deviations of large structures, it may be necessary to adjust the installation position, while for crack problems, it may be necessary to repair or replace some components. Subsequently, the defects of these various sample components and their corresponding compensation strategies are associated and stored to establish multiple sets of training data. These training data are then input into a locally deployed model framework. This model framework can be deployed based on deep neural networks, support vector machines, decision trees, etc. After the model framework receives these data, it will train the model. Taking deep neural networks as an example, it iterates through steps such as forward propagation, loss calculation, back propagation, and parameter optimization until the loss converges or the maximum number of iterations is reached, thereby generating a compensation rule model. Afterwards, the quality defect characteristics of the P components identified in the first-floor prefabricated building are input into the compensation rule model for processing, and the compensation strategy that matches each defect characteristic is identified, thereby obtaining P associated defect compensation strategies, which provide specific operating steps and methods for subsequent quality compensation and correction. Then, based on the spatial positions of the P components, these associated defect compensation strategies are prioritized and merged. Specifically, the quality defect characteristics of each component have a specific spatial position. If multiple components have similar defects or are in the same spatial area, the relevant compensation strategies will be merged to determine the order of priority compensation. For example, multiple components in a certain layer may have the same type of deviation, and the part with the largest deviation will be processed first. The merged priority compensation strategy will be updated to M updated defect compensation strategies. These strategies are sorted according to the spatial position of the defect, the degree of deviation, and the priority to determine the final compensation plan. Finally, based on the M updated defect compensation strategies obtained, the quality defect characteristics of the P components will be dynamically compensated. For components with dimensional deviations, their installation positions will be automatically adjusted. For cracks or surface defects, repair operations will be performed or replacement components will be recommended. Dynamic compensation can be performed in real time during the construction process, and the compensation plan will be continuously adjusted according to the actual situation and test results to ensure that the component quality is repaired to the greatest extent possible.

[0048] Furthermore, the method further comprises:

[0049] A global prefabricated building is obtained through hierarchical component assembly detection; the prefabricated building model is used to perform global component quality verification on the global prefabricated building, and dynamic compensation of global deviation defects is performed based on the verification results.

[0050] Optionally, after completing the hierarchical component assembly inspection, that is, after the recursive iteration is completed, a global prefabricated building will be obtained. Subsequently. Perform point cloud scanning on the global prefabricated building to construct a global prefabricated building model, and then combine the global prefabricated building model with the prefabricated building model. In the same way as above, perform deviation identification in four aspects: normal vector angle, plane distance, crack width and hole size to obtain a global component quality verification result. Afterwards, based on the detected deviation defects, the detected defects will be input into the compensation rule model to match the defect compensation strategy, such as repositioning, replacing unqualified components, strengthening docking points, etc., and then perform dynamic compensation of global deviation defects according to the matched defect compensation strategy to ensure that all quality defects can be repaired in a timely manner, thereby ensuring the quality and structural stability of the entire prefabricated building.

[0051] In summary, the embodiments of the present application have at least the following technical effects:

[0052] The embodiment of the present application first performs building structure hierarchical modeling based on the information of the building to be constructed to obtain an M-layer assembled component model group; then, based on the entity ID of the first-layer assembled component model group, after invoking the assembled component construction to obtain the first-layer assembled building, point cloud scanning and splicing are performed on the first-layer assembled building to obtain a first-layer real-time building model; thereafter, the spatial projection of the first-layer real-time building model is overlapped onto the first-layer assembled component model group to perform component deviation detection, and P component quality defect features are identified and located; further, after dynamically compensating for the P component quality defect features, a second-layer assembled building is constructed on the first-layer assembled building based on the second-layer assembled component model group; then, based on the second-layer assembled component model group, component quality deviation detection is performed on the second-layer assembled building, and W component quality defect features are identified and located; finally, recursive iterations of assembled construction and component assembly quality detection compensation are performed based on the M-layer assembled component model group until the component construction quality detection of the M-th layer assembled building is completed. These technical effects jointly solve the technical problem that traditional building component quality inspection relies on manual measurement and cannot achieve real-time quality monitoring and dynamic compensation layer by layer, resulting in defect accumulation and low inspection efficiency. They achieve the technical effect of automatically identifying component quality defects through real-time comparison of three-dimensional point cloud models, and dynamically compensating quality defects through recursive iteration, thereby improving the accuracy and efficiency of building component quality inspection.

[0053] Example 2, based on the same inventive concept as the building component quality detection method in the above embodiment, Figure 2As shown, the present application provides a building component quality inspection system, the system comprising: a hierarchical modeling module 11: performing hierarchical modeling of the building structure according to the information of the building to be constructed, and obtaining an M-layer assembled component model group; a point cloud scanning and splicing module 12: calling the assembled component construction to obtain the first layer of assembled building according to the entity ID of the first layer of assembled component model group, performing point cloud scanning and splicing on the first layer of assembled building to obtain the first layer of real-time building model; a first deviation detection module 13: overlapping the spatial projection of the first layer of real-time building model to the first layer of assembled component model group to perform component deviation detection, and identifying the fixed P component quality defect characteristics are determined; a component construction module 14: after dynamically compensating for the P component quality defect characteristics, a second-layer prefabricated building is constructed on the first-layer prefabricated building based on the second-layer prefabricated component model group; a second deviation detection module 15: based on the second-layer prefabricated component model group, component quality deviation detection is performed on the second-layer prefabricated building to identify and locate W component quality defect characteristics; a quality inspection module 16: based on the M-layer prefabricated component model group, recursive iterations of prefabricated construction and component assembly quality inspection compensation are performed until the component construction quality inspection of the M-th layer prefabricated building is completed.

[0054] Furthermore, the hierarchical modeling module 11 is further configured to execute the following method:

[0055] Using the structural code and construction parameters of the building to be constructed as dual search conditions, local retrieval and call of the building information to be constructed; modeling and restoring the prefabricated building model based on the building information to be constructed; 3 based on the defect propagation and amplification characteristics, hierarchically decomposing the prefabricated building model to obtain an M-layer assembled component model group, wherein each prefabricated component model has an installation space coordinate identifier.

[0056] Furthermore, the hierarchical modeling module 11 is further configured to execute the following method:

[0057] A decimal structure code set is extracted from the information of the building to be constructed, wherein the decimal structure code set is constrained by a component code hierarchical relationship; a building topology structure framework is constructed on a BIM platform according to the component code hierarchical relationship; after matching a plurality of standardized component models based on the decimal structure code set, the prefabricated building model is assembled and restored by mapping the plurality of standardized component models to the building topology structure framework.

[0058] Furthermore, the hierarchical modeling module 11 is further configured to execute the following method:

[0059] Based on the last three digits of the decimal structure code set, multiple type identifiers, multiple floor affiliation codes, and multiple space partition codes of multiple building components are decomposed and obtained; based on the multiple type identifiers, the multiple standardized component models are called in a standardized component model library; the multiple floor affiliation codes and multiple space partition code features are mapped into multiple installation space coordinates, and the multiple standardized component models are automatically assembled in the building topology structure framework to restore the prefabricated building model.

[0060] Furthermore, the first deviation detection module 13 is further configured to perform the following method:

[0061] According to the N component entity IDs of the N standardized component models in the first-layer assembled component model group, N prefabricated components are called; using the N floor affiliation codes and the N space partition codes as assembly construction constraints, the N prefabricated components are physically assembled to obtain the first-layer prefabricated building; point cloud scanning and splicing processing are performed on the N prefabricated components in the assembled state in the first-layer prefabricated building to obtain the first-layer real-time building model, wherein the first-layer real-time building model includes N real-time component models; using the N floor affiliation codes and the N space partition codes as projection overlap constraints, after performing spatial projection of the N standardized component models and the N real-time component models, component deviation detection is performed to identify and locate the quality defect characteristics of the P components.

[0062] Furthermore, the first deviation detection module 13 is further configured to perform the following method:

[0063] Pre-activate N component deviation identification containers; load the spatial projections of the N standardized component models and the N real-time component models into the N component deviation identification containers to perform multi-threaded deviation defect identification, and output normal vector angle deviation defects, plane distance deviation defects, crack width defects, and hole size defects; retain the defect space characteristics of the normal vector angle deviation defects, plane distance deviation defects, crack width defects, and hole size defects, and output the P component quality defect features.

[0064] Furthermore, the first deviation detection module 13 is further configured to perform the following method:

[0065] The component deviation identification container is pre-configured with multi-threaded deviation identification tasks, and the multi-threaded deviation identification tasks include a normal vector angle deviation identification task, a plane distance deviation identification task, a crack width identification task, and a hole size identification task.

[0066] Furthermore, the quality detection module 16 is further configured to perform the following method:

[0067] Interactively obtain multi-scale defect compensation strategies for multiple sample component defects, and complete the local configuration of the compensation rule model by associating and storing the multiple sample component defects and the multi-scale defect compensation strategies; input the P component quality defect characteristics into the compensation rule model to obtain P associated defect compensation strategies; perform priority merging of the P associated defect compensation strategies according to the P spatial positions of the P component quality defect characteristics to obtain M updated defect compensation strategies; and perform spatial construction dynamic compensation for the P component quality defect characteristics by executing the M updated defect compensation strategies.

[0068] Furthermore, the quality detection module 16 is further configured to perform the following method:

[0069] A global prefabricated building is obtained through hierarchical component assembly detection; the prefabricated building model is used to perform global component quality verification on the global prefabricated building, and dynamic compensation of global deviation defects is performed based on the verification results.

[0070] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0072] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for detecting the quality of building components, characterized in that: The method comprises: Based on the information of the building to be constructed, the building structure hierarchical modeling is carried out to obtain an M-layer assembly component model group; According to the entity ID of the first-layer assembled component model group, after invoking the assembled component construction to obtain the first-layer assembled building, perform point cloud scanning and splicing on the first-layer assembled building to obtain the first-layer real-time building model; Overlaying the spatial projection of the first-layer real-time building model onto the first-layer assembled component model group to perform component deviation detection and identify and locate P component quality defect features; After dynamically compensating for the quality defect characteristics of the P components, constructing the second-layer prefabricated building on the first-layer prefabricated building according to the second-layer prefabricated component model group; Based on the second-floor assembled component model group, we perform component quality deviation detection on the second-floor prefabricated building and identify and locate W component quality defect features. Perform recursive iterations of prefabricated construction and component assembly quality inspection and compensation based on the M-layer prefabricated component model group until the component construction quality inspection of the M-th layer of prefabricated buildings is completed; Among them, identifying and locating P component quality defect characteristics includes: Calling N prefabricated components according to N component entity IDs of N standardized component models in the first-layer prefabricated component model group; Using N floor affiliation codes and N space partition codes as assembly construction constraints, physically assembling the N prefabricated components to obtain the first-floor prefabricated building; Performing point cloud scanning and splicing processing on the N assembled components in the first-layer prefabricated building to obtain a first-layer real-time building model, wherein the first-layer real-time building model includes N real-time component models; Using the N floor attribution codes and N space partition codes as projection overlap constraints, after performing spatial projection of the N standardized component models and the N real-time component models, component deviation detection is performed to identify and locate the quality defect features of the P components; Wherein, identifying and locating the P component quality defect characteristics includes: Pre-activate N component deviation identification containers; Loading the spatial projections of the N standardized component models and the N real-time component models into the N component deviation identification containers to perform multi-threaded deviation defect identification, and outputting normal vector angle deviation defects, plane distance deviation defects, crack width defects, and hole size defects; retaining the defect space characteristics of the normal vector angle deviation defect, the plane distance deviation defect, the crack width defect, and the hole size defect, and outputting the P component quality defect characteristics; Among them, also include: Interactively obtaining multi-scale defect compensation strategies for multiple sample component defects, and completing local configuration of a compensation rule model by associating and storing the multiple sample component defects and the multi-scale defect compensation strategies; Inputting the P component quality defect characteristics into the compensation rule model to obtain P associated defect compensation strategies; Prioritizing the P associated defect compensation strategies according to the P spatial positions of the P component quality defect characteristics to obtain M updated defect compensation strategies; By executing the M updated defect compensation strategies, spatial construction dynamic compensation of the P component quality defect characteristics is performed.

2. The building component quality inspection method according to claim 1, wherein: Performing building structure hierarchical modeling based on the information of the building to be constructed to obtain an M-layer assembled component model group, the method comprising: Using the structural code and construction parameters of the building to be constructed as dual search conditions, locally searching and calling the information of the building to be constructed; Restoring the prefabricated building model based on the information model of the building to be constructed; Based on the defect transmission and amplification characteristics, the prefabricated building model is decomposed hierarchically to obtain an M-layer assembled component model group, wherein each prefabricated component model has an installation space coordinate identifier.

3. The building component quality inspection method according to claim 2, characterized in that: The method for restoring the prefabricated building model based on the information modeling of the building to be constructed comprises: Extracting a decimal structure code set from the information of the building to be constructed, wherein the decimal structure code set is constrained by a component code hierarchical relationship; According to the component coding hierarchical relationship, a building topology framework is constructed on the BIM platform; After matching and obtaining a plurality of standardized component models according to the decimal structure code set, the prefabricated building model is assembled and restored by mapping the plurality of standardized component models to the building topology structure framework.

4. The building component quality inspection method according to claim 3, wherein: After matching and obtaining a plurality of standardized component models according to the decimal structure code set, the plurality of standardized component models are mapped to the building topology structure framework to assemble and restore the prefabricated building model. The method includes: Decomposing the plurality of building components into a plurality of type identifiers, a plurality of floor attribution codes, and a plurality of space partition codes according to the last three digits of the decimal structure code set; calling the plurality of standardized component models in a standardized component model library according to the plurality of type identifiers; The multiple floor attribution codes and multiple space partition code features are mapped into multiple installation space coordinates, and the multiple standardized component models are automatically assembled in the building topology structure framework to restore the prefabricated building model.

5. The building component quality inspection method according to claim 1, wherein: The component deviation identification container is pre-configured with multi-threaded deviation identification tasks, and the multi-threaded deviation identification tasks include a normal vector angle deviation identification task, a plane distance deviation identification task, a crack width identification task, and a hole size identification task.

6. The building component quality inspection method according to claim 2, wherein: The method further comprises: Through hierarchical component assembly testing, a global prefabricated building is obtained; The prefabricated building model is used to perform global component quality verification on the global prefabricated building, and dynamic compensation of global deviation defects is performed based on the verification results.

7. Building component quality inspection system, characterized in that: The system is used to perform the building component quality detection method according to any one of claims 1 to 6, and the system comprises: Hierarchical modeling module: Performs hierarchical modeling of the building structure based on the information of the building to be constructed, and obtains an M-layer assembly component model group; Point cloud scanning and splicing module: Based on the entity ID of the first-layer assembled component model group, after invoking the prefabricated component construction to obtain the first-layer prefabricated building, point cloud scanning and splicing are performed on the first-layer prefabricated building to obtain the first-layer real-time building model; The first deviation detection module is used to overlay the spatial projection of the first-layer real-time building model onto the first-layer assembled component model group to perform component deviation detection and identify and locate P component quality defect features; Component construction module: after dynamically compensating for the quality defect characteristics of the P components, construct the second-layer prefabricated building on the first-layer prefabricated building based on the second-layer prefabricated component model group; Second deviation detection module: Based on the second-layer assembled component model group, it performs component quality deviation detection on the second-layer prefabricated building and identifies and locates W component quality defect features; Quality inspection module: Execute recursive iteration of prefabricated construction and component assembly quality inspection compensation based on the M-layer assembled component model group until the component construction quality inspection of the M-layer prefabricated building is completed.

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