A point cloud integration and model library compliance quality inspection management method, device and equipment

Abstract

The present application relates to three-dimensional model quality inspection and data integration management technical field, disclose a kind of point cloud integration and model library compliance quality inspection management method, device and equipment, the present application is by obtaining to be registered point cloud data and target building model;With the coordinate system of target building model as reference coordinate system, to be registered point cloud data is registered to reference coordinate system, obtains registered point cloud data;Target building model and registered point cloud data are associated and stored in pre-constructed model library, and the compliance quality inspection of building model to be detected in model library is carried out, obtains multidimensional quality inspection result;According to multidimensional quality inspection result, generate quality inspection report, the present application realizes the integration of construction actual measurement review and model specification physical examination, automation management, improves quality inspection efficiency and alignment accuracy.

Description

Technical Field

[0001] This invention relates to the field of 3D model quality inspection and data integration management technology, specifically to a point cloud integration and model library compliance quality inspection management method, device and equipment. Background Technology

[0002] With the widespread application of 3D scanning technology in the construction and engineering fields, point cloud data has become an important basis for on-site construction quality verification and real-world alignment.

[0003] In existing solutions, the integration of point cloud data and building models relies heavily on tedious manual registration and position adjustment, resulting in low registration efficiency and difficulty in guaranteeing position alignment accuracy. At the same time, the lack of a standardized association management mechanism in the model library leads to chaotic storage of model assets. Furthermore, compliance quality inspection of building models mostly relies on manual visual inspection and manual verification, which is time-consuming and labor-intensive for checking dimensions such as model file format, component attributes, coding, and color scheme, and lacks efficient and automated comprehensive quality inspection management methods. Summary of the Invention

[0004] This invention provides a point cloud integration and model library compliance quality inspection management method, apparatus and equipment to solve the problems of inefficient and automatic registration of point cloud data and building models, and the difficulty in performing compliance quality inspection management of building models.

[0005] In a first aspect, the present invention provides a method for compliance quality inspection and management of point cloud integration and model library, including: Acquire point cloud data to be matched and target building model; Using the coordinate system of the target building model as the reference coordinate system, the point cloud data to be registered is registered to the reference coordinate system to obtain the registered point cloud data; The target building model and the registered point cloud data are associated and stored in a pre-built model library, and the building models to be tested in the model library are subjected to compliance quality inspection to obtain multi-dimensional quality inspection results. A quality inspection report is generated based on the multi-dimensional quality inspection results.

[0006] Beneficial effects: By acquiring the point cloud data to be registered and the target building model, and using the coordinate system of the target building model as the reference coordinate system, the point cloud data to be registered is registered to the reference coordinate system to obtain the registered point cloud data. The target building model and the registered point cloud data are then associated and stored in a pre-built model library. The building models to be inspected in the model library are subjected to compliance inspection to obtain multi-dimensional quality inspection results and generate quality inspection reports. This changes the inefficient traditional mode that relies on manual registration and visual inspection, significantly shortens the time for registering the point cloud data to be registered, and realizes the integrated and automated management of construction measurement verification and building model specification inspection, thereby improving quality inspection efficiency and delivery quality.

[0007] In one optional implementation, acquiring the point cloud data to be registered includes: Acquire source point cloud data, which includes header information and a 3D point set; Extract coordinate system reference and point cloud extent metadata from the header information; Based on the point cloud range metadata, the source point cloud data is divided into a three-dimensional voxel grid, and the three-dimensional point set is downsampled to obtain the downsampled point cloud data. Calculate the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, and remove points whose Euclidean distance is greater than a preset distance to obtain the point cloud data to be registered. Neighboring points refer to points whose distance from each point in the downsampled point cloud data is within a preset neighborhood range.

[0008] Beneficial effects: By acquiring source point cloud data containing header information and 3D point sets, coordinate system reference and point cloud range metadata are extracted from the header information. Based on the point cloud range metadata, the source point cloud data is divided into a 3D voxel mesh, and the 3D point set is downsampled using the voxel mesh. This significantly reduces the amount of data while preserving the geometric features of the building. Furthermore, by calculating the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, points with Euclidean distances greater than a preset distance are removed to obtain the point cloud data to be registered. This cleans up outlier noise interference generated by the on-site scanning environment, greatly improving computational efficiency and data quality, and providing a high-quality data foundation for subsequent high-precision registration.

[0009] In one optional implementation, the point cloud data to be registered is registered to the reference coordinate system, using the coordinate system of the target building model as the reference coordinate system, to obtain the registered point cloud data, including: Based on the point cloud data to be registered and the target building model, establish a set of corresponding point pairs; The corresponding point pair set is filtered out, and corresponding point pairs whose distance exceeds a preset distance threshold and / or whose normal vector angle exceeds a preset angle threshold are removed, resulting in a retained point pair set; Based on the set of retained point pairs, the transformation matrix that transforms the point cloud data to be registered to the reference coordinate system is solved by minimizing the sum of squares of the distance errors between point pairs in the set of retained point pairs. Update the position of the point cloud data to be registered according to the transformation matrix, and repeat the above filtering and solving steps until the preset convergence condition is met to obtain the registered point cloud data.

[0010] Beneficial effects: By establishing a set of corresponding point pairs based on the point cloud data to be registered and the target building model, and filtering the set of corresponding point pairs to remove those with a distance exceeding a preset distance threshold and / or an angle between normal vectors exceeding a preset angle threshold, a set of retained point pairs is obtained, which can eliminate mismatched point pairs. Furthermore, based on the retained point pair set, the transformation matrix is ​​solved by minimizing the sum of squares of the distance errors between point pairs in the retained point pair set, and the position of the point cloud data to be registered is updated according to the transformation matrix. This process is repeated until the preset convergence condition is met, achieving high-precision automatic initial registration of the point cloud data to be registered to the reference coordinate system. This greatly reduces the tediousness of manual adjustment and ensures the objectivity and accuracy of spatial alignment.

[0011] In one optional implementation, a set of corresponding point pairs is established based on the point cloud data to be registered and the target building model, including: The iterative nearest point algorithm is used, and a K-dimensional spatial tree is used to find the point in the target building model that is closest to each point in the point cloud data to be registered with Euclidean distance, and then pair them up to obtain the corresponding point pair set.

[0012] Beneficial effects: By utilizing the iterative nearest point algorithm and employing a K-dimensional spatial tree to find the point in the target building model that is closest to each point in the point cloud data to be registered using Euclidean distance, and then combining them pairwise to obtain the corresponding point pair set, the K-dimensional spatial tree data structure was successfully used to accelerate the search process of neighboring points, reduce the time complexity of the corresponding point pair search, greatly improve the computational alignment speed of large-scale 3D point cloud data, and ensure the smoothness of the registration response of the software system.

[0013] In one optional implementation, based on the set of retained point pairs, the transformation matrix for transforming the point cloud data to be registered to the reference coordinate system is solved by minimizing the sum of squares of the distance errors between point pairs in the set of retained point pairs, including: The covariance matrix is ​​constructed based on the set of retained point pairs. Singular value decomposition is performed on the covariance matrix to obtain the rotation matrix and translation vector. The transformation matrix is ​​composed of the rotation matrix and translation vector.

[0014] Beneficial effects: By constructing the covariance matrix based on the set of retained point pairs, and solving the rotation matrix and translation vector by singular value decomposition of the covariance matrix, and constructing the transformation matrix from the rotation matrix and translation vector, the coordinate translation and rotation deviations can be eliminated quickly and efficiently at the mathematical level, avoiding getting trapped in local optima in the solution process, and ensuring the stability and mathematical robustness of the transformation matrix calculation.

[0015] In one alternative implementation, the target building model and the registered point cloud data are associated and stored in a pre-built model library, including: The storage location of the target building model in the model library is determined according to the preset classification hierarchy; The target building model is stored in the storage location of the model library, and the registration point cloud data is stored in the model library as the associated data of the target building model.

[0016] Beneficial effects: By determining the storage location of the target building model in the model library according to the preset classification hierarchy, and storing the target building model in the storage location, the registration point cloud data is stored in the model library as the associated data of the target building model. This realizes the structured organization and persistent binding of the target building model file and the registration point cloud data, providing convenient data support for subsequent retrieval of on-site construction verification results and historical comparison.

[0017] In one alternative implementation, the method further includes: When a deletion instruction is received for the target building model, check if there are any reference records to the target building model in the current project files; If a reference record exists, a reference list is generated, and deletion of the target building model is prevented. The reference list includes the name of the parent model, the reference location, and the reference type. If no reference record exists, deletion of the target building model is permitted.

[0018] Beneficial effects: By checking whether there are any reference records to the target building model in the current project file when a deletion instruction for the target building model is received, and generating a reference list containing the name of the parent model, the reference location and the reference type if a reference record exists, and preventing the deletion operation of the target building model, it is possible to prevent the deletion of the core target building model that is being referenced due to accidental operation in collaborative development, thus ensuring the integrity and security of the model library data and reducing the risk of project file crashes.

[0019] In one alternative implementation, the method further includes: Version management is performed on the target building models stored in the model library. Version management includes: version control of uploaded target building models based on the uniqueness of the target building model content, and restoration of historical versions to the current version in response to rollback commands for historical versions.

[0020] Beneficial effects: By managing the version of target building models stored in the model library, including version control of uploaded target building models based on the uniqueness of target building model content, and restoring historical versions to the current version in response to rollback commands, efficient traceability and management of the entire lifecycle of target building model asset changes are achieved. This ensures that the model update process is traceable, controllable and reversible, and improves the efficiency of model reuse and version order in team collaborative development.

[0021] In one optional implementation, version control is performed on the uploaded target building model based on the uniqueness of the target building model content, including: Detect the hash value of the target building model; If the hash value matches the hash value of a historical version of the target building model already stored in the model library, the upload will be rejected. If the hash value is inconsistent with the hash value of the target building model in the model library, the version number will be automatically incremented according to the preset version naming convention and the historical version will be retained to perform version control on the uploaded target building model.

[0022] Beneficial effects: By detecting the hash value of the target building model, the upload is rejected if the hash value matches the hash value of a historical version of the target building model already stored in the model library. The hash value is used to accurately determine whether the content of the target building model has changed, preventing redundant files from occupying storage space. If the hash value does not match the hash value of a historical version of the target building model already stored in the model library, the version number is automatically incremented according to the preset version naming convention and the historical version is retained, realizing the objectivity and automation of version control for uploaded target building models.

[0023] In one alternative implementation, in response to a rollback command for a historical version, restoring the historical version to the current version includes: Upon receiving a rollback command, the target building model of the previous version is copied, stored as the new version in the model library, and the new version is updated to the current version.

[0024] Beneficial effects: By responding to rollback commands for historical versions, the target building model of the historical version is copied when the rollback command is received. The copied target building model of the historical version is stored in the model library as the new version and the new version is updated to the current version. This can not only quickly restore the current model to the specified historical state to fix design defects, but also completely retain all previous historical version records, avoiding the hidden danger of losing historical versions caused by traditional direct overwrite.

[0025] In one optional implementation, compliance quality checks are performed on the building models to be inspected in the model library to obtain multi-dimensional quality inspection results, including: Identify the model type of the building model to be inspected and the information of the components to be inspected in the building model; The compliance inspection of the building models to be inspected in the model library is carried out to obtain multi-dimensional quality inspection results. The compliance inspection includes: the compliance inspection of the building models to be inspected according to the model type to obtain the model quality inspection results, and the compliance inspection of the component information to be inspected to obtain the component quality inspection results. The multi-dimensional quality inspection results include model quality inspection results and component quality inspection results.

[0026] Beneficial effects: By identifying the model type of the building model to be inspected and the information of the components to be inspected in the building model to be inspected, compliance quality inspection is performed on the building model to be inspected according to the model type to obtain model quality inspection results, and compliance quality inspection is performed on the information of the components to be inspected to obtain component quality inspection results. The model quality inspection results and component quality inspection results are combined to obtain multi-dimensional quality inspection results, realizing multi-level quality inspection coverage from macro model files to micro component elements, and improving the comprehensiveness and standardization level of automated quality inspection.

[0027] In one optional implementation, the model quality inspection result is obtained by performing a compliance quality inspection on the building model to be inspected according to the model type, including: If the building model to be tested includes a power grid information model, then the file header, required fields, and data format corresponding to the power grid information model are verified to obtain the legality verification result. If the building model to be tested includes an engineering model, then check whether the engineering model contains a preset set of necessary attributes to obtain the attribute check results. If the building model to be inspected includes an equipment model, then the geometric integrity and parameter integrity of the components of the equipment model are checked to obtain the integrity check results; The results of the legality check and / or attribute check and / or integrity check are combined to obtain the model quality inspection results.

[0028] Beneficial effects: By performing compliance checks on the building models to be inspected according to the model type, if the building model to be inspected includes a power grid information model, the corresponding file header, required fields and data format are checked to obtain the legality check result. If it includes an engineering model, it is checked whether it contains a preset set of necessary attributes to obtain the attribute check result. If it includes an equipment model, the geometric integrity and parameter integrity of the components are checked to obtain the integrity check result. The corresponding generated check results are combined to prevent models lacking key attributes or missing components from entering the construction stage, and to ensure that professional drawings and equipment models comply with specific industry specifications.

[0029] In one optional implementation, the component quality inspection result is obtained by performing compliance quality inspection on the information of the component to be inspected, including: If the information of the component to be inspected includes geometric details, the level of detail of the building model to be inspected is evaluated based on the density of the triangular facets and the minimum detail size, and the detail inspection result is obtained. If the information of the component to be inspected includes the component material, the component material is compared with the preset color code library to obtain the color matching inspection result; If the component information to be detected includes a component code field, then the component code field is parsed and matched with a preset classification code standard using regular expressions to obtain the code inspection result; The results of the fineness inspection and / or color matching inspection and / or coding inspection are combined to obtain the component quality inspection results.

[0030] Beneficial effects: By performing compliance checks on the information of the components to be inspected, the level of detail is evaluated based on the density of triangular faces and the minimum detail size of the building model to be inspected when geometric details are included, and the result of the detail check is obtained by comparing the material of the components with the preset color code library when the material of the components is included, and the result of the color check is obtained by parsing the component coding field and performing regular expression matching with the preset classification coding standard when the component coding field is included. The corresponding generated inspection results are combined, and the fine inspection that previously relied on manual visual inspection is completely transformed into automated rule scanning, eliminating the problems of missed inspections and inconsistent standards, and greatly reducing coding and color matching compliance errors.

[0031] Secondly, this invention provides a point cloud integration and model library compliance quality inspection management device, comprising: The acquisition module is used to acquire the point cloud data to be aligned and the target building model; The registration module is used to register the point cloud data to be registered to the reference coordinate system, using the coordinate system where the target building model is located as the reference coordinate system, to obtain the registered point cloud data; The quality inspection module is used to associate the target building model and the registration point cloud data into a pre-built model library, and to perform compliance quality inspection on the building models to be inspected in the model library to obtain multi-dimensional quality inspection results. The generation module is used to generate quality inspection reports based on multi-dimensional quality inspection results.

[0032] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform a point cloud integration and model library compliance quality inspection management method according to the first aspect or any corresponding embodiment described above.

[0033] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute a point cloud integration and model library compliance quality inspection management method as described in the first aspect or any corresponding embodiment above.

[0034] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute a point cloud integration and model library compliance quality inspection management method described in the first aspect or any corresponding embodiment above. Attached Figure Description

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of the first process of a point cloud integration and model library compliance quality inspection management method according to an embodiment of the present invention; Figure 2 This is a point cloud integrated structure diagram according to an embodiment of the present invention; Figure 3 This is a structural diagram of a point cloud basic tool according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a quality inspection module according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the process of deleting a target building model according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the management target building model version according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the second process for version management of the target building model according to an embodiment of the present invention; Figure 8 This is a structural block diagram of a point cloud integration and model library compliance quality inspection management device according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0039] In response to the practical needs of modern building and engineering construction review scenarios, such as the large volume of point cloud data, the difficulty in aligning it with virtual models, the chaotic collaborative storage of model assets, and the excessive reliance on manual verification for model compliance checks, the integration of point clouds and building models in related technologies often relies on cumbersome manual registration, resulting in low registration efficiency and difficulty in guaranteeing accuracy. The lack of a standardized association management mechanism in the model library leads to asset redundancy and confusion. Furthermore, there is a lack of automated quality inspection tools covering multiple dimensions such as model file format, component attributes, coding, and color scheme, which cannot meet the requirements of high-efficiency and high-precision digital engineering quality inspection management.

[0040] This invention acquires point cloud data to be registered and a target building model, using the coordinate system of the target building model as a unified reference, and automatically registers the point cloud data to be registered with high precision to the reference coordinate system, achieving rapid and accurate spatial alignment between the measured point cloud and the virtual model. Subsequently, the target building model and the registered point cloud data are associated and stored in a pre-built model library, realizing the structured binding and standardized management of the design model and the on-site measured data. Finally, the building models to be inspected in the model library are subjected to automated, multi-dimensional compliance quality inspection and a quality inspection report is generated, transforming traditional visual sampling into automated rule scanning. This solves the technical problems of cumbersome point cloud alignment, chaotic model storage, and time-consuming and labor-intensive quality inspection in one go, achieving integrated and efficient management of on-site measured verification and model asset compliance quality inspection.

[0041] According to an embodiment of the present invention, a method for compliance quality inspection management of point cloud integration and model library is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 1 This is a schematic diagram of the first process of a point cloud integration and model library compliance quality inspection management method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the point cloud data to be registered and the target building model.

[0043] Specifically, the point cloud data to be registered refers to the set of measured three-dimensional points containing spatial three-dimensional coordinate information collected by scanning the construction site or building structure with a three-dimensional laser scanning device; the target building model is a BIM model (Building Information Modeling), which refers to a digital building model file built based on building information modeling technology for spatial registration and model alignment with the point cloud data to be registered.

[0044] The system acquires the point cloud data to be registered collected at the construction site and the target building model built in the design phase through the data import interface. The point cloud data to be registered and the target building model are then loaded into the 3D rendering and data processing engine to complete the initial reading and loading of the data.

[0045] Step S102: Using the coordinate system of the target building model as the reference coordinate system, register the point cloud data to be registered to the reference coordinate system to obtain the registered point cloud data.

[0046] Specifically, the reference coordinate system refers to the world coordinate system of the target building model as a unified spatial reference coordinate system; the registration point cloud data refers to the three-dimensional point cloud data obtained by transforming the point cloud data to be registered, which was originally in the local coordinate system of the scanner, into the reference coordinate system through spatial rigid body transformation.

[0047] Using the coordinate system of the target building model as the reference coordinate system, the spatial rotation matrix and translation vector between the point cloud data to be registered and the target building model are calculated. The point cloud data to be registered in the local coordinate system is then mapped and transformed to the reference coordinate system, so that the point cloud data to be registered and the target building model coincide and align under the same spatial reference, thereby obtaining the registered point cloud data after eliminating coordinate deviation.

[0048] Step S103: Associate the target building model and the registered point cloud data and store them in the pre-built model library. Perform compliance quality inspection on the building model to be inspected in the model library to obtain multi-dimensional quality inspection results.

[0049] Specifically, the pre-built model library refers to a pre-built database system for organizing, managing, and storing architectural model assets and corresponding point cloud data; the architectural model to be tested refers to the architectural model stored in the model library that needs to undergo compliance checks and reviews in accordance with preset industry standards and norms; the multi-dimensional quality inspection results refer to the comprehensive inspection results record covering multiple technical indicators such as the legality of the model file architecture, the integrity of attributes, the geometric integrity of components, and the details and coding of components.

[0050] The target building model is stored in the model library, and the registration point cloud data is bound and stored with the target building model as associated data. Then, the automated quality inspection engine is called to retrieve the building model to be inspected from the model library, and scans and verifies it from multiple technical dimensions according to the preset compliance inspection rules. The verification data of each dimension is summarized to obtain multi-dimensional quality inspection results.

[0051] Step S104: Generate a quality inspection report based on the multi-dimensional quality inspection results.

[0052] Specifically, a quality inspection report is a document report that includes information on model violations and defects, along with rectification guidance and suggestions, generated after the multi-dimensional quality inspection results are structured and summarized.

[0053] The system analyzes the non-compliant items, erroneous statistical data, and screenshots of model violations extracted from multi-dimensional quality inspection results. It automatically fills in and formats the above information according to a preset report template, generating a structured quality inspection report. The report can then be pushed to relevant personnel via a communication interface to guide the subsequent rectification and optimization of the construction site and design model.

[0054] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By acquiring the point cloud data to be registered and the target building model, and using the coordinate system of the target building model as the reference coordinate system, the point cloud data to be registered is registered to the reference coordinate system to obtain registered point cloud data. The target building model and the registered point cloud data are then associated and stored in a pre-built model library. The building model to be inspected in the model library is subjected to compliance quality inspection to obtain multi-dimensional quality inspection results and generate a quality inspection report. This method changes the inefficient traditional mode that relies on manual registration and visual inspection, significantly shortens the registration time of the point cloud data to be registered, and realizes the integrated and automated management of construction measurement verification and building model specification inspection, thereby improving quality inspection efficiency and delivery quality.

[0055] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 2 This is a point cloud integrated structure diagram according to an embodiment of the present invention, such as... Figure 2 As shown: Step S201: Obtain source point cloud data, which includes header information and a 3D point set.

[0056] Specifically, source point cloud data refers to the raw laser scanning data collected by a 3D laser scanning device and imported into the system. Its format can be LAS (Laser Scan) or LAZ (Laser Scan Zip). Header information refers to the file header data containing point cloud file metadata information. 3D point set refers to the collection data composed of measurement points in space.

[0057] The system reads imported LAS or LAZ format point cloud files through a data interface, automatically parses and reads the header information in the source point cloud data file, and synchronously loads and reads the 3D point set stored in the file.

[0058] Step S202: Extract coordinate system reference and point cloud range metadata from the header information.

[0059] Specifically, coordinate system reference refers to the coordinate system reference information used to identify the reference datum of the spatial position of the point cloud; point cloud extent metadata refers to the spatial bounding box boundary parameters used to define the spatial distribution boundary of the three-dimensional point set.

[0060] Parse the read header information, extract the coordinate system reference information recorded therein, and at the same time extract the point cloud range metadata of the maximum and minimum coordinate boundaries of the recorded 3D point set in 3D space.

[0061] Step S203: Based on the point cloud range metadata, the source point cloud data is divided into a three-dimensional voxel grid, and the three-dimensional point set is downsampled to obtain downsampled point cloud data.

[0062] Specifically, a three-dimensional voxel mesh refers to a grid of cubic voxel units with equal side lengths established in space based on the metadata of the point cloud range; downsampled point cloud data refers to downsampled point cloud data obtained by replacing all points in the original grid with representative points of geometric features within each voxel mesh.

[0063] Based on the point cloud range metadata, the three-dimensional space occupied by the source point cloud data is divided into several three-dimensional voxel grids. The centroid coordinates of all points contained in each voxel grid are calculated and extracted. These centroid coordinates are used to replace all measurement points in the voxel grid, thereby performing voxel grid downsampling on the three-dimensional point set to obtain downsampled point cloud data that retains geometric features and significantly reduces the amount of data.

[0064] Step S204: Calculate the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, and remove points whose Euclidean distance is greater than a preset distance to obtain the point cloud data to be registered. Neighboring points refer to points whose distance from each point in the downsampled point cloud data is within a preset neighborhood range.

[0065] Specifically, the preset distance refers to a distance threshold determined by calculating the sum of the mean of the average distances between all points in the downsampled point cloud data and their neighboring points, and a preset multiple of the standard deviation. As shown in formula (1): (1) in, This indicates a preset distance threshold. This represents the mean of the average Euclidean distances between all points in the downsampled point cloud data and their neighboring points. This indicates the preset multiplier, with a default value of 3. It represents the standard deviation of the average Euclidean distance between all points in the downsampled point cloud data and their neighboring points; a neighboring point refers to a point within a spherical neighborhood with a preset spatial radius centered on the target point.

[0066] By using a statistical filter to iterate through each point in the downsampled point cloud data, the average Euclidean distance between that point and its neighbors within a preset neighborhood is calculated, and the mean of the average Euclidean distances for all points in the entire image is calculated. and standard deviation Determine the preset distance threshold Remove those with an average Euclidean distance greater than a preset distance. The cleaned point cloud data is then loaded into the 3D scene as the point cloud data to be registered.

[0067] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By acquiring source point cloud data containing header information and 3D point sets, coordinate system reference and point cloud range metadata are extracted from the header information. Based on the point cloud range metadata, the source point cloud data is divided into a 3D voxel mesh, and the 3D point set is downsampled using the voxel mesh. This significantly reduces the amount of data while preserving the geometric features of the building. Furthermore, by calculating the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, points with Euclidean distances greater than a preset distance are removed to obtain the point cloud data to be registered. This cleans up outlier noise interference generated by the on-site scanning environment, greatly improving computational efficiency and data quality, and providing a high-quality data foundation for subsequent high-precision registration.

[0068] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 3 This is a structural diagram of a point cloud basic tool according to an embodiment of the present invention, such as... Figure 3 As shown: Step S301: Establish a set of corresponding point pairs based on the point cloud data to be registered and the target building model.

[0069] Specifically, the set of corresponding point pairs refers to the set of points located in the source point cloud. Points in With the target point cloud Corresponding points in The set of matching point pairs formed, where This refers to the source point cloud, i.e., the point cloud data to be registered. This represents the target point cloud, i.e., the target building model. Represents source point cloud The first in One point, Represents the target point cloud Zhongyu The corresponding number One point.

[0070] Traversing the source point cloud Each point in the point cloud data to be registered In the target building model, i.e., the target point cloud Search for the point with the closest Euclidean distance. Establish a set of corresponding point pairs consisting of the nearest point pairs.

[0071] Step S302: Filter the corresponding point pair set and remove the corresponding point pairs whose distance exceeds a preset distance threshold and / or whose normal vector angle exceeds a preset angle threshold, to obtain the retained point pair set.

[0072] Specifically, the preset distance threshold is a distance limit used to filter mismatched point pairs with excessive spatial Euclidean distance, with a default value of 0.05 meters; the preset angle threshold is an angle limit used to filter point pairs with excessive normal orientation deviation, with a default value of 30 degrees; the retained point pair set refers to the set of high-quality point pairs retained after filtering.

[0073] A filtering strategy is used to filter the corresponding set of point pairs. If the Euclidean distance between the point pairs is greater than a preset distance threshold, the point pair is discarded. If the point cloud contains normal vector information and the angle between the normal vector directions of the point pairs is greater than a preset angle threshold, the point pair is discarded. Point pairs that meet the filtering requirements at the same time are retained to obtain the set of retained point pairs.

[0074] Step S303: Based on the set of retained point pairs, solve the transformation matrix to transform the point cloud data to be registered to the reference coordinate system by minimizing the sum of squares of the distance errors between point pairs in the set of retained point pairs.

[0075] Specifically, the transformation matrix refers to the matrix derived from the rotation matrix. Translation vector The homogeneous coordinate transformation matrix is ​​formed; the sum of squared distance errors refers to the objective function used to measure the registration and alignment accuracy. As shown in formula (2): (2) in, Let represent the objective function for registration error. Represents the rotation matrix. Represents source point cloud The first in One point, Represents the translation vector. Represents the target point cloud Zhongyu The corresponding number One point.

[0076] Construct the above objective function based on the set of retained points. By minimizing the objective function Solving the problem yields the source point cloud. With target point cloud The optimal rotation matrix that minimizes registration error Translation vector This forms the transformation matrix.

[0077] Step S304: Update the position of the point cloud data to be registered according to the transformation matrix, and repeat the above filtering and solving steps until the preset convergence condition is met to obtain the registered point cloud data.

[0078] Specifically, the preset convergence conditions refer to the convergence judgment rules, including any one of the following three conditions: the change in mean square error is less than the preset relative fitness threshold of 0.000001, the preset maximum number of iterations of 200 is reached, and the change in transformation parameters is sufficiently small, that is, the change in translation is less than 0.000001 meters and the change in rotation is less than 0.000001 radians.

[0079] The transformation matrix obtained by solving is applied to the source point cloud. The process involves updating the spatial location of the points and repeatedly performing the steps of establishing corresponding point pairs, filtering corresponding point pairs, and solving the transformation matrix for iterative registration until any of the preset convergence conditions are met. Then, the iteration stops and the registered point cloud data is output.

[0080] This embodiment provides a point cloud integration and model library compliance quality inspection management method. It establishes a set of corresponding point pairs based on the point cloud data to be registered and the target building model, and filters these sets, removing point pairs whose distance exceeds a preset distance threshold and / or whose normal vector angle exceeds a preset angle threshold, thus obtaining a set of retained point pairs. This method can eliminate mismatched point pairs. Furthermore, it solves for the transformation matrix by minimizing the sum of squares of the distance errors between point pairs in the retained set, and updates the position of the point cloud data to be registered based on the transformation matrix. This process is repeated until a preset convergence condition is met, achieving high-precision automatic initial registration of the point cloud data to be registered to the reference coordinate system. This greatly reduces the tediousness of manual adjustments and ensures the objectivity and accuracy of spatial alignment.

[0081] In one optional implementation, step S301 includes: Step S3011: Using the iterative nearest point algorithm and a K-dimensional spatial tree, find the point in the target building model that is closest to each point in the point cloud data to be registered with Euclidean distance, and combine them pairwise to obtain the corresponding point pair set.

[0082] Specifically, a K-dimensional tree refers to a tree used for analyzing target point clouds. The data structure for hierarchical segmentation and indexing of three-dimensional spatial data; the iterative closest point algorithm refers to the ICP (Iterative Closest Point) registration algorithm, which is an iterative optimization method for aligning two sets of point cloud data.

[0083] During the execution of the iterative nearest point algorithm, the target building model is pre-constructed as a K-dimensional spatial tree structure, which is then used to quickly accelerate the search for points located in the source point cloud. Each point in the point cloud data to be registered The closest point in the Euclidean distance of the target building model By combining them in pairs, we obtain a set of corresponding point pairs.

[0084] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By utilizing the iterative nearest point algorithm and employing a K-dimensional spatial tree to find the point in the target building model that is closest to each point in the point cloud data to be registered in terms of Euclidean distance, and combining them pairwise to obtain the corresponding point pair set, the K-dimensional spatial tree data structure is successfully used to accelerate the search process of neighboring points, reduce the time complexity of corresponding point pair search, greatly improve the calculation alignment speed of large-scale 3D point cloud data, and ensure the smoothness of the software system's registration response.

[0085] In one optional implementation, step S303 includes: Step S3031: Construct a covariance matrix based on the set of retained point pairs, perform singular value decomposition on the covariance matrix to obtain a rotation matrix and a translation vector, and construct a transformation matrix from the rotation matrix and the translation vector.

[0086] Specifically, the covariance matrix is ​​a matrix used to characterize the spatial distribution correlation between the source point cloud and the target point cloud. Singular Value Decomposition (SVD) is a matrix factorization algorithm that decomposes any matrix into three matrices. It is used for applications such as dimensionality reduction, data compression, and feature extraction.

[0087] Computational source point cloud various points in the middle center of mass With target point cloud various points in the middle center of mass The source and target point clouds are transformed to their respective centroid coordinate systems to eliminate the translation effect, and a covariance matrix is ​​constructed based on the retained point pairs in the centroid coordinate system. For the covariance matrix The result is obtained by performing singular value decomposition, as shown in formula (3): (3) The rotation matrix is ​​calculated based on the decomposition results. Translation vector As shown in formulas (4) and (5): (4) (5) in, Represents the covariance matrix. This represents a left orthogonal matrix in singular value decomposition. This represents a right orthogonal matrix in singular value decomposition. Represents a right orthogonal matrix The transpose of the matrix, Represents a singular value diagonal matrix. Represents the rotation matrix. Represents the translation vector. Represents the target point cloud various points in the middle The center of mass, Represents source point cloud various points in the middle The centroid; derived from the rotation matrix Translation vector This forms the final transformation matrix.

[0088] It should be noted that this embodiment also provides a manual fine-tuning tool for further adjustments: Operation granularity includes: (a) Translation adjustment: Set the step size (default 0.01 meters) along the X / Y / Z axes respectively. The user can control the translation amount through sliders or numerical input boxes; (b) Rotation adjustment: Set the angle step size (default 0.01 meters) around the X / Y / Z axes respectively. (c) Scaling adjustment: Set the scaling step size (default 0.001) to handle inconsistent scales. The real-time feedback mechanism is as follows: After each adjustment operation is triggered, the system completes scene redrawing and updates the point cloud position within 200ms. At the same time, the interface synchronously displays the current transformation matrix parameters (rotation angle, translation amount, scaling ratio) and registration error value (average distance deviation between the point cloud and the BIM model), realizing an operation-feedback closed loop.

[0089] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By constructing a covariance matrix based on the retained point pair set, the method performs singular value decomposition on the covariance matrix to solve for the rotation matrix and translation vector, and constructs a transformation matrix from the rotation matrix and translation vector. This method can quickly and efficiently eliminate coordinate translation and rotation deviations at the mathematical level, avoid getting trapped in local optima during the solution process, and ensure the stability and mathematical robustness of the transformation matrix calculation.

[0090] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 4 This is a schematic diagram of a quality inspection module according to an embodiment of the present invention. The workflow of the quality inspection module is as follows: Figure 4 As shown: Step S401: Determine the storage location of the target building model in the model library according to the preset classification hierarchy.

[0091] Specifically, the preset classification hierarchy refers to a classification tree structure with a four-level hierarchy. The first level is the major professional category, including structure, electromechanical, and site. The second level is the subsystem (such as HVAC, water supply and drainage, and electrical under electromechanical). The third level is the equipment component type (such as fans and air conditioning units under HVAC). The fourth level is the specific model and specification.

[0092] Based on the preset four-level classification tree structure, the target building model is analyzed for attributes and classified for matching. The path of the four-level node corresponding to the target building model in the classification tree is determined, thereby determining the storage location of the target building model in the model library.

[0093] It should be noted that this embodiment also provides an access control method, specifically as follows: The access control method is Role-Based Access Control (RBAC). The system defaults to the following role levels: (a) System Administrator: Has full permissions, including creating / deleting categories, assigning user permissions, system configuration, etc.; (b) Category Owner: Has permissions to upload, approve, and delete models for the parent node and child nodes of the category tree under their jurisdiction; (c) Regular User: Only has permissions to view and download models, without upload / delete permissions. Permissions are inherited along the category tree hierarchy: parent node permissions are automatically applied to all child nodes unless explicitly overridden in the child nodes.

[0094] Step S402: Store the target building model in the storage location of the model library, and store the registration point cloud data as the associated data of the target building model in the model library.

[0095] Specifically, associated data refers to the auxiliary measurement data records that are attached to and bound to the main model asset.

[0096] Based on the determined storage location node, the target building model is written into the model library, and the registered point cloud data that has completed coordinate transformation is attached and bound to the target building model as associated data to form an association mapping relationship.

[0097] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By determining the storage location of the target building model in the model library according to a preset classification hierarchy, the target building model is stored in the storage location. At the same time, the registered point cloud data is stored in the model library as the associated data of the target building model. This realizes the structured organization and persistent binding of the target building model file and the registered point cloud data, providing convenient data support for subsequent retrieval of on-site construction verification results and historical comparison.

[0098] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 5 This is a schematic diagram of the process of deleting a target building model according to an embodiment of the present invention, such as... Figure 5 As shown: Step S501: When a deletion instruction for the target building model is received, check whether there is a reference record for the target building model in the current project file.

[0099] Specifically, a deletion command refers to an operation request that triggers the removal of a model; a reference record refers to a nested reference, external reference link, or material and family instance reference record of the target model in the BIM (Building Information Modeling) model file, construction simulation scene, or quality inspection task configuration of the current project file.

[0100] When a deletion command is received for a specific model asset, the system iterates through all BIM model files, construction simulation scenarios, and quality inspection task configurations in the current project files to check for any reference records pointing to the target building model.

[0101] In step S502, if a reference record exists, a reference list is generated, and deletion of the target building model is prevented. The reference list includes the name of the parent model, the reference location, and the reference type.

[0102] Specifically, the reference list refers to a detailed table that displays all references to the model.

[0103] If a reference record is found during the traversal and retrieval, information such as the name of the parent model that references the model, the specific reference location, and the reference type are extracted and summarized, and a visual reference list is generated and displayed to the user. At the same time, deletion operations on the target building model are prevented, and a prompt dialog box pops up requesting the removal of the reference.

[0104] Step S503: If no reference record exists, the deletion operation of the target building model is allowed.

[0105] Specifically, allowing deletion means removing the block on deletion operations and clearing the model library files.

[0106] If the search confirms that there are no references to the target building model in the current project files, the block is lifted, allowing the deletion of the target building model, and all historical versions and file records of the model are cleaned up simultaneously.

[0107] This embodiment provides a point cloud integration and model library compliance quality inspection management method. When a deletion instruction for a target building model is received, the method searches the current project file for any reference records of the target building model. If a reference record exists, a reference list containing the name of the parent model, the reference location, and the reference type is generated, and the deletion operation of the target building model is prevented. This method can prevent the deletion of the core target building model that is being referenced due to accidental operation during collaborative development, ensuring the integrity and security of the model library data and reducing the risk of project file crashes.

[0108] This embodiment provides a point cloud integration and model library compliance quality inspection management method, which can be used in the aforementioned electronic devices. Figure 6 This is a schematic diagram of the management target building model version according to an embodiment of the present invention, such as... Figure 6 As shown: Step S601: Perform version management on the target building model stored in the model library. Version management includes: controlling the uploaded target building model based on the uniqueness of the target building model content, and restoring the historical version to the current version in response to the rollback command of the historical version.

[0109] Specifically, version management refers to a management mechanism that traces the evolution history of model library assets; version control refers to the control process of incrementing version numbers and retaining history based on the unique fingerprint of files; and rollback instructions refer to instructions that reassign a specified historical version as the current effective version.

[0110] Establish a model version management mechanism. When a model is uploaded, it will automatically determine and execute version increment control and retain historical versions based on the unique fingerprint of the model content. At the same time, it will listen to the system version management interface and restore the specified historical model version to the current version of the system when it receives a rollback instruction for a historical version.

[0111] For example: Model versions follow the naming convention of "{Model Name}-{Major Version Number}.{Minor Version Number}.{Revision Number}-{Date}". For example, "Structural Column-2.1.3-20260615" represents the 2nd major version (major change), 1st version (new features), and 3rd revision (defect fixes) of the structural column model, released on June 15, 2026. Each time a model is uploaded, the system automatically checks the model file hash value. If the content remains unchanged, it rejects the duplicate upload; if there are changes, it automatically increments the version number and retains the historical version. Version rollback mechanism: Users can select any historical version from the version history list and click "Rollback". The system copies the model file of that historical version and appends it as a new version (the version number automatically increments, e.g., rolling back from 2.1.3 to 1.0.0 generates a new version 2.1.4) to the end of the version history, while simultaneously updating the "Current Version" pointer. The system retains the most recent 10 historical versions by default; any additional versions are automatically archived to cold storage.

[0112] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By managing the version of the target building models stored in the model library, including version control of the uploaded target building models based on the uniqueness of the target building model content, and restoring the historical version to the current version in response to the rollback command of the historical version, it realizes efficient traceability and management of the entire life cycle of target building model asset changes, ensures that the model update process is traceable, controllable and reversible, and improves the model reuse efficiency and version order in team collaborative development.

[0113] In one optional implementation, step S601 includes: Step S6011: Detect the hash value of the target building model.

[0114] Specifically, a hash value refers to a digital fingerprint calculated to characterize the uniqueness of the content of a model file, such as an MD5 (Message-Digest Algorithm 5) or SHA-256 (Secure Hash Algorithm 256) calculated value.

[0115] When uploading the model file corresponding to the target building model to the model library, a hash verification algorithm is called to calculate the hash value of the model file.

[0116] In step S6012, if the hash value matches the hash value of a historical version of the target building model already stored in the model library, the upload is rejected.

[0117] The hash value calculated this time is compared with the hash values ​​of all historical versions of the target building model with the same name that are already stored in the model library. If the hash values ​​are completely consistent, it is determined that the file content has not changed, and the duplicate upload is rejected and the user is notified.

[0118] Step S6013: If the hash value is inconsistent with the hash value of the historical version of the target building model already stored in the model library, the version number is automatically incremented according to the preset version naming convention and the historical version is retained to perform version control on the uploaded target building model.

[0119] Specifically, the default version naming convention refers to the rule of naming version numbers according to the format of model name plus major version number plus minor version number plus revision number plus date.

[0120] If the comparison finds that the hash value is inconsistent with the hash value of the historical version already stored in the model library, it is determined that the model content has changed. The version number is automatically incremented according to the preset version naming convention, and the original model file is retained as a historical version. The newly uploaded model file is then stored to achieve version control.

[0121] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By detecting the hash value of the target building model, the upload is rejected if the hash value is consistent with the hash value of a historical version of the target building model already stored in the model library. The hash value is used to accurately determine whether the content of the target building model has changed, preventing redundant files from occupying storage space. If the hash value is inconsistent with the hash value of a historical version of the target building model already stored in the model library, the version number is automatically incremented according to the preset version naming standard and the historical version is retained. This realizes the objectivity and automation of version control for uploaded target building models.

[0122] Step S6014: Upon receiving a rollback instruction, copy the target building model of the historical version, store the target building model of the historical version as the new version in the model library, and update the new version to the current version.

[0123] Specifically, the current version refers to the model version that is currently in effect by default in the model library and is called by external systems.

[0124] When a rollback command is received from a user for a target building model of a specific historical version in the version history list, the target building model file of that specified historical version is copied, appended as a new version with an automatically incrementing version number to the end of the version history of the model library, and the system's current version pointer is updated to point to the new version, thus completing the version restoration.

[0125] This embodiment provides a point cloud integration and model library compliance quality inspection management method. In response to the rollback command for a historical version, the method copies the target building model of the historical version when the rollback command is received. The copied target building model of the historical version is stored in the model library as the new version and the new version is updated to the current version. This method can quickly restore the current model to the specified historical state to fix design defects, and also completely retain all previous historical version records, avoiding the risk of losing historical versions caused by traditional direct overwriting.

[0126] In some optional implementations, step S103 above includes: Step S701: Identify the model type of the building model to be detected and the information of the components to be detected in the building model to be detected.

[0127] Specifically, model type refers to the classification attribute of the building model to be inspected, including GIM (Grid Information Model), engineering model and equipment model; the component information to be inspected refers to the geometric details, component material and component coding field contained in the internal components of the model.

[0128] The system parses the metadata and file structure of the building model to be detected, automatically identifies which model type it belongs to, such as GIM, engineering model, or equipment model, and simultaneously extracts the geometric details, material, and component code fields of each component inside the model to detect component information.

[0129] Step S702: Perform compliance quality checks on the building models to be tested in the model library to obtain multi-dimensional quality check results. The compliance quality checks include: performing compliance quality checks on the building models to be tested according to the model type to obtain model quality check results, and performing compliance quality checks on the component information to be tested to obtain component quality check results. The multi-dimensional quality check results include model quality check results and component quality check results.

[0130] Specifically, model quality inspection results refer to the inspection results generated for verifying the model file architecture and overall attributes; component quality inspection results refer to the inspection results generated for verifying component details, colors, and codes.

[0131] An automated quality inspection engine is invoked to perform file architecture and attribute verification on the building model to be inspected based on the identified model type, generating model quality inspection results. Specifically, when the building model to be inspected includes a power grid information model (GIM file), its file header, required fields, and data format are verified to conform to power grid specifications. When the building model to be inspected includes an engineering model, it is verified to include preset necessary attribute sets such as project name, coordinate system, and component parameters. When the building model to be inspected includes an equipment model, the geometric integrity and parameter integrity of its components are verified. At the same time, the extracted component information to be inspected is verified for details, materials, and coding rules, generating component quality inspection results. The model quality inspection results and component quality inspection results are then structurally summarized and combined to obtain multi-dimensional quality inspection results.

[0132] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By identifying the model type of the building model to be inspected and the information of the components to be inspected in the building model, compliance quality inspection is performed on the building model to be inspected according to the model type to obtain model quality inspection results, and compliance quality inspection is performed on the information of the components to be inspected to obtain component quality inspection results. The model quality inspection results and component quality inspection results are combined to obtain multi-dimensional quality inspection results, realizing multi-level quality inspection coverage from macro model files to micro component elements, and improving the comprehensiveness and standardization level of automated quality inspection.

[0133] In one optional implementation, step S701 includes: Step S7021: If the building model to be tested includes a power grid information model, then verify the file header, required fields, and data format corresponding to the power grid information model to obtain the legality verification result.

[0134] Specifically, the legality verification result refers to the verification report that checks whether the syntax and structure of the GIM file conform to the power grid standard.

[0135] If the building model to be detected is identified as a power grid information model (GIM file), its text and syntax structure are parsed, and its file header definition, the existence of required fields, and the standardization of data format are checked in turn to generate a legality verification result.

[0136] Step S7022: If the building model to be detected includes an engineering model, then verify whether the engineering model contains a preset set of necessary attributes and obtain the attribute check result.

[0137] Specifically, the preset necessary attribute set refers to the set of necessary attributes required by the specification, such as project name, coordinate system, component name, material and dimensions; the attribute check result refers to the attribute integrity check report.

[0138] If the building model to be detected is identified as an engineering model, its attribute nodes are traversed, and the model is compared to check whether it fully contains the preset necessary attribute set such as project name, coordinate system and component parameters, and the attribute check results are generated.

[0139] Step S7023: If the building model to be inspected includes an equipment model, then verify the geometric integrity and parameter integrity of the components of the equipment model to obtain the integrity check result.

[0140] Specifically, the equipment model refers to a model of electrical or building equipment; the integrity check result refers to an inspection report on whether equipment components are missing and whether parameters are complete.

[0141] If the building model to be inspected is identified as an equipment model, the system automatically identifies the key components of the equipment, verifies whether there are any missing geometric structures in each component, verifies whether the corresponding parameters are complete, and generates an integrity check result.

[0142] Step S7024: Combine the legality verification results and / or attribute check results and / or integrity check results to obtain the model quality inspection results.

[0143] Specifically, model quality inspection results refer to verification reports covering the overall dimensions of the model.

[0144] The legality verification results, attribute check results, and integrity check results generated during the above execution process are combined into structured data and summarized as model quality inspection results.

[0145] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By performing compliance quality inspection on the building model to be inspected according to the model type, if the building model to be inspected includes a power grid information model, the corresponding file header, required fields and data format are checked to obtain a legality check result. If it includes an engineering model, it is checked whether it contains a preset set of necessary attributes to obtain an attribute check result. If it includes an equipment model, the geometric integrity and parameter integrity of the components are checked to obtain an integrity check result. The corresponding generated check results are combined to prevent models lacking key attributes or missing components from entering the construction stage, and to ensure that professional drawings and equipment models comply with specific industry specifications.

[0146] Step S7025: If the information of the component to be inspected includes geometric details, the level of detail of the building model to be inspected is evaluated based on the density of the triangular facets and the minimum detail size, and the detail inspection result is obtained.

[0147] Specifically, the level of detail refers to the level of detail of the LOD (Level of Detail) model, such as LOD2.0 or LOD3.0; the detail check result is a report that determines whether the geometric details of the model meet the standards.

[0148] If the extracted information of the component to be inspected contains geometric details, the density of the triangular facets and the minimum detail size of the mesh of the building model to be inspected are extracted, the level of detail of the building model to be inspected is evaluated according to the LOD standard, and the detail check results are generated.

[0149] Step S7026: If the information of the component to be detected includes the component material, then the component material is compared with the preset color code library to obtain the color matching inspection result.

[0150] Specifically, the preset color code library refers to the built-in industry standard color code library; the color matching inspection result refers to the report that marks color violations.

[0151] If the extracted component information includes component material, iterate through the RGB (Red Green Blue) color values ​​of all component materials, compare them with the standard colors in the preset color standard library, mark non-compliant colors, and generate color matching inspection results.

[0152] Step S7027: If the component information to be detected includes a component code field, then parse the component code field and perform regular expression matching with the preset classification code standard to obtain the code check result.

[0153] Specifically, the preset classification coding standard refers to classification coding specifications such as OmniClass; the coding check result refers to the check report that conforms to the regular expression rules.

[0154] If the extracted component information contains a component code field, parse the code text, use regular expressions to match and verify it against the preset classification code standard format, and generate the code check result.

[0155] Step S7028: Combine the fineness inspection results and / or color matching inspection results and / or coding inspection results to obtain the component quality inspection results.

[0156] Specifically, component quality inspection results refer to the verification report for the microscopic dimensions of the component.

[0157] The detailed inspection results, color matching inspection results, and coding inspection results generated during the above execution process are combined into structured data and summarized as component quality inspection results.

[0158] This embodiment provides a point cloud integration and model library compliance quality inspection management method. By performing compliance quality inspection on the information of the component to be inspected, if geometric details are included, the detail level is evaluated based on the density of triangular faces and the minimum detail size of the building model to be inspected to obtain the detail inspection result. If the component material is included, it is compared with a preset color mark library to obtain the color matching inspection result. If the component coding field is included, it is parsed and matched with a preset classification coding standard using regular expressions to obtain the coding inspection result. The corresponding generated inspection results are combined, which completely transforms the previous fine inspection that relied on manual visual inspection into automated rule scanning, eliminating the problems of missed inspections and inconsistent standards, and greatly reducing coding and color matching compliance errors.

[0159] Figure 7 This is a schematic diagram of the second process of a point cloud integration and model library compliance quality inspection management method according to an embodiment of the present invention, as shown below. Figure 7 As shown: First, acquire the point cloud data to be registered and the target building model. Then, using the coordinate system of the target building model as the reference coordinate system, register the point cloud data to be registered to the reference coordinate system by calculating the spatial rigid body transformation parameters to obtain the registered point cloud data. Subsequently, associate the target building model and the registered point cloud data and store them in a pre-built model library, and call the automated quality inspection engine to perform compliance quality inspection on the building models to be inspected in the model library to obtain multi-dimensional quality inspection results. Finally, parse the multi-dimensional quality inspection results and automatically render and generate a quality inspection report based on the multi-dimensional quality inspection results.

[0160] This embodiment, through the aforementioned integrated processing flow, changes the inefficient traditional model that relies on manual registration and visual inspection, and realizes automated management of construction measurement verification and building model specification inspection, effectively improving quality inspection efficiency and delivery quality.

[0161] This embodiment also provides a point cloud integration and model library compliance quality inspection management device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated for details already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0162] This embodiment provides a point cloud integration and model library compliance quality inspection management device, such as... Figure 8 As shown, it includes: Module 801 is used to acquire the point cloud data to be matched and the target building model; The registration module 802 is used to register the point cloud data to be registered to the reference coordinate system, using the coordinate system where the target building model is located as the reference coordinate system, to obtain the registered point cloud data; The quality inspection module 803 is used to associate the target building model and the registration point cloud data into a pre-built model library, and to perform compliance quality inspection on the building model to be inspected in the model library to obtain multi-dimensional quality inspection results. The generation module 804 is used to generate a quality inspection report based on the multi-dimensional quality inspection results.

[0163] In some optional implementations, the acquisition module 801 includes: The source point cloud data acquisition unit is used to acquire source point cloud data, which includes header information and a 3D point set.

[0164] The header information extraction unit is used to extract coordinate system reference and point cloud range metadata from the header information.

[0165] The downsampled point cloud data acquisition unit is used to divide the source point cloud data into a three-dimensional voxel grid based on the point cloud range metadata, and to perform voxel grid downsampling on the three-dimensional point set to obtain downsampled point cloud data.

[0166] The point cloud data filtering unit is used to calculate the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, and to remove points whose Euclidean distance is greater than a preset distance, thus obtaining the point cloud data to be registered. Neighboring points refer to points whose distance from each point in the downsampled point cloud data is within a preset neighborhood range.

[0167] In some alternative implementations, the registration module 802 includes: The corresponding point pair set establishment unit is used to establish a corresponding point pair set based on the point cloud data to be registered and the target building model.

[0168] The point pair set filtering unit is used to filter the corresponding point pair set, and remove the corresponding point pairs whose distance exceeds a preset distance threshold and / or whose normal vector angle exceeds a preset angle threshold, thus obtaining the retained point pair set.

[0169] The coordinate system transformation unit is used to solve the transformation matrix that transforms the point cloud data to be registered to the reference coordinate system by minimizing the sum of squares of the distance errors between point pairs in the retained point pair set.

[0170] The registration point cloud data acquisition unit is used to update the position of the point cloud data to be registered according to the transformation matrix, and repeat the above filtering and solving steps until the preset convergence condition is met to obtain the registration point cloud data.

[0171] In some optional implementations, the corresponding point pair set establishment unit includes: The corresponding point pair set is combined into sub-units, which are used to use the iterative nearest point algorithm and a K-dimensional spatial tree to find the point in the target building model that is closest to each point in the point cloud data to be registered in Euclidean distance and combine them in pairs to obtain the corresponding point pair set.

[0172] In some optional implementations, the coordinate system transformation unit includes: The singular value decomposition subunit is used to construct the covariance matrix based on the set of retained point pairs, perform singular value decomposition on the covariance matrix to obtain the rotation matrix and translation vector, and form the transformation matrix by the rotation matrix and translation vector.

[0173] In some alternative implementations, the quality inspection module 803 includes: The storage location determination unit is used to determine the storage location of the target building model in the model library according to a preset classification hierarchy.

[0174] The associated data storage unit is used to store the target building model in the storage location of the model library, and to store the registered point cloud data as associated data of the target building model in the model library.

[0175] In some optional implementations, a point cloud integration and model library compliance quality inspection management device further includes: The reference record retrieval unit is used to retrieve whether there are any reference records for the target building model in the current project file when a deletion instruction for the target building model is received.

[0176] The deletion operation organization unit is used to generate a reference list if a reference record exists and to prevent deletion operations on the target building model. The reference list includes the name of the parent model, the reference location, and the reference type.

[0177] The delete operation permission unit allows deletion of the target building model if no reference record exists.

[0178] The target building model version management unit is used to manage the versions of target building models stored in the model library. Version management includes: controlling the version of uploaded target building models based on the uniqueness of the target building model content, and restoring the historical version to the current version in response to the rollback command of the historical version.

[0179] In some alternative implementations, the target building model version management unit includes: The hash value detection subunit is used to detect the hash value of the target building model.

[0180] The first sub-unit of hash value verification is used to reject the upload if the hash value matches the hash value of a historical version of the target building model already stored in the model library.

[0181] The second sub-unit of hash value verification is used to automatically increment the version number according to the preset version naming convention and retain the historical version if the hash value is inconsistent with the hash value of the target building model in the model library. This is to perform version control on the uploaded target building model.

[0182] The target building model copying unit is used to copy the historical version of the target building model when a rollback instruction is received, store the historical version of the target building model as the new version in the model library, and update the new version to the current version.

[0183] In some alternative implementations, the quality inspection module 803 includes: The model type identification unit is used to identify the model type of the building model to be detected and the information of the components to be detected in the building model to be detected; The multi-dimensional quality inspection result acquisition unit is used to perform compliance quality inspection on the building models to be inspected in the model library and obtain multi-dimensional quality inspection results. The compliance quality inspection includes: performing compliance quality inspection on the building models to be inspected according to the model type to obtain model quality inspection results, and performing compliance quality inspection on the information of the components to be inspected to obtain component quality inspection results. The multi-dimensional quality inspection results include model quality inspection results and component quality inspection results.

[0184] In some optional implementations, the unit for obtaining multi-dimensional quality inspection results includes: The legality verification result yields a sub-unit, which is used to verify the file header, required fields, and data format of the power grid information model if the building model to be tested includes a power grid information model, and to obtain the legality verification result.

[0185] The attribute check results yield sub-units, which are used to verify whether the engineering model contains a preset set of necessary attributes if the building model to be checked includes an engineering model, thus obtaining the attribute check results.

[0186] The integrity check results yield sub-units, which are used to verify the geometric integrity and parameter integrity of the components of the equipment model if the building model to be checked includes an equipment model, thus obtaining the integrity check results.

[0187] The model quality inspection results yield sub-units, which are used to combine the legality verification results and / or attribute check results and / or integrity check results to obtain the model quality inspection results.

[0188] The detail check results yield sub-units, which are used to evaluate the detail level of the building model under inspection based on the triangular facet density and minimum detail size if the information of the component to be inspected includes geometric details, thus obtaining the detail check results.

[0189] The color matching inspection result yields a sub-unit, which is used to compare the component material with a preset color code library if the component information to be inspected includes the component material, and thus obtain the color matching inspection result.

[0190] The encoding check result yields a sub-unit, which is used to parse the component encoding field and perform regular expression matching with the preset classification encoding standard if the component information to be detected includes a component encoding field, thereby obtaining the encoding check result.

[0191] The component quality inspection results are used to obtain sub-units, which are used to combine the fineness inspection results and / or color matching inspection results and / or coding inspection results to obtain the component quality inspection results.

[0192] The point cloud integration and model library compliance quality inspection management device provided in this embodiment of the invention can execute the point cloud integration and model library compliance quality inspection management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0193] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0194] The following is a detailed reference. Figure 9 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 901, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 902 or a program loaded from memory 908 into random access memory (RAM) 903. The RAM 903 also stores various programs and data required for the operation of the electronic device. The processor 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0195] Typically, the following devices can be connected to I / O interface 905: input devices 906 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 907 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 908 including, for example, magnetic tapes, hard disks, etc.; and communication devices 909. Communication device 909 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 9 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0196] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 909, or installed from a memory 908, or installed from a ROM 902. When the computer program is executed by a processor 901, it performs the functions defined in the point cloud integration and model library compliance quality inspection management method of the embodiments of the present invention.

[0197] Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0198] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the point cloud integration and model library compliance quality inspection management method shown in the above embodiments.

[0199] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0200] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for compliance quality inspection and management of point cloud integration and model library, characterized in that, The method includes: Acquire point cloud data to be matched and target building model; Using the coordinate system where the target building model is located as the reference coordinate system, the point cloud data to be registered is registered to the reference coordinate system to obtain the registered point cloud data; The target building model and the registered point cloud data are associated and stored in a pre-built model library, and the building model to be tested in the model library is subjected to compliance quality inspection to obtain multi-dimensional quality inspection results. A quality inspection report is generated based on the multi-dimensional quality inspection results.

2. The method according to claim 1, characterized in that, The acquisition of the point cloud data to be registered includes: Acquire source point cloud data, which includes header information and a 3D point set; Extract coordinate system reference and point cloud extent metadata from the header information; Based on the point cloud range metadata, the source point cloud data is divided into a three-dimensional voxel grid, and the three-dimensional point set is downsampled to obtain downsampled point cloud data. Calculate the Euclidean distance between each point in the downsampled point cloud data and its neighboring points, and remove points whose Euclidean distance is greater than a preset distance to obtain the point cloud data to be registered. The neighboring points refer to points whose distance from each point in the downsampled point cloud data is within a preset neighborhood range.

3. The method according to claim 1, characterized in that, The step of registering the point cloud data to be registered to the reference coordinate system, using the coordinate system where the target building model is located, to obtain the registered point cloud data includes: Based on the point cloud data to be registered and the target building model, establish a set of corresponding point pairs; The set of corresponding point pairs is filtered out, and corresponding point pairs whose distance exceeds a preset distance threshold and / or whose angle between normal vectors exceeds a preset angle threshold are removed, thus obtaining a set of retained point pairs. Based on the set of retained point pairs, the transformation matrix for transforming the point cloud data to be registered to the reference coordinate system is solved by minimizing the sum of squares of the distance errors between point pairs in the set of retained point pairs. The position of the point cloud data to be registered is updated according to the transformation matrix, and the above filtering and solving steps are repeated until the preset convergence condition is met to obtain the registered point cloud data.

4. The method according to claim 3, characterized in that, The step of establishing a set of corresponding point pairs based on the point cloud data to be registered and the target building model includes: Using the iterative nearest point algorithm, and employing a K-dimensional spatial tree, the point in the target building model that is closest to each point in the point cloud data to be registered is found, and the points are combined in pairs to obtain the corresponding set of point pairs.

5. The method according to claim 3, characterized in that, The step of solving the transformation matrix for transforming the point cloud data to be registered to the reference coordinate system by minimizing the sum of squares of the distance errors between point pairs in the retained point pair set includes: A covariance matrix is ​​constructed based on the set of retained point pairs. Singular value decomposition is performed on the covariance matrix to obtain a rotation matrix and a translation vector. The transformation matrix is ​​formed by the rotation matrix and the translation vector.

6. The method according to claim 1, characterized in that, The step of associating the target building model and the registered point cloud data and storing them in a pre-built model library includes: The storage location of the target building model in the model library is determined according to the preset classification hierarchy structure; The target building model is stored in the storage location of the model library, and the registration point cloud data is stored in the model library as the associated data of the target building model.

7. The method according to claim 6, characterized in that, The method further includes: Upon receiving a deletion instruction for the target building model, the system searches the current project files for any reference records to the target building model. If the reference record exists, a reference list is generated, and deletion of the target building model is prevented. The reference list includes the name of the parent model, the reference location, and the reference type. If the reference record does not exist, the deletion operation of the target building model is permitted.

8. The method according to claim 1, characterized in that, The method further includes: Version management is performed on the target building models stored in the model library. The version management includes: version control of the uploaded target building models based on the uniqueness of the target building model content, and restoring the historical version to the current version in response to the rollback command of the historical version.

9. The method according to claim 8, characterized in that, The version control of uploaded target building models based on the uniqueness of the target building model content includes: Detect the hash value of the target building model; If the hash value matches the hash value of a historical version of the target building model already stored in the model library, the upload will be rejected. If the hash value is inconsistent with the hash value of the historical version of the target building model already stored in the model library, the version number will be automatically incremented according to the preset version naming convention and the historical version will be retained to perform version control on the uploaded target building model.

10. The method according to claim 8, characterized in that, The step of responding to a rollback command for a historical version and restoring the historical version to the current version includes: Upon receiving a rollback command, the target building model of the historical version is copied, the historical version of the target building model is stored in the model library as the new version, and the new version is updated to the current version.

11. The method according to claim 1, characterized in that, The compliance quality inspection of the building models to be inspected in the model library is performed to obtain multi-dimensional quality inspection results, including: Identify the model type of the building model to be detected and the information of the components to be detected in the building model to be detected; The building models to be tested in the model library are subjected to compliance quality checks to obtain multi-dimensional quality check results. The compliance quality checks include: performing compliance quality checks on the building models to be tested according to the model type to obtain model quality check results, and performing compliance quality checks on the component information to be tested to obtain component quality check results. The multi-dimensional quality check results include model quality check results and component quality check results.

12. The method according to claim 11, characterized in that, The process of obtaining model quality inspection results by performing compliance quality inspection on the building model to be inspected according to the model type includes: If the building model to be tested includes a power grid information model, then the file header, required fields, and data format corresponding to the power grid information model are verified to obtain the legality verification result; If the building model to be detected includes an engineering model, then verify whether the engineering model contains a preset set of necessary attributes to obtain the attribute check result; If the building model to be tested includes an equipment model, then the geometric integrity and parameter integrity of the components of the equipment model are verified to obtain the integrity check result; The legality verification results and / or the attribute check results and / or the integrity check results are combined to obtain the model quality inspection results.

13. The method according to claim 11, characterized in that, The process of obtaining the component quality inspection result by performing compliance quality inspection on the information of the component to be inspected includes: If the information of the component to be inspected includes geometric details, the level of detail of the building model to be inspected is evaluated based on the density of the triangular facets and the minimum detail size, and the detail inspection result is obtained. If the information of the component to be tested includes the component material, then the component material is compared with a preset color code library to obtain the color matching inspection result; If the component information to be detected includes a component code field, then the component code field is parsed and matched with a preset classification code standard using regular expressions to obtain the code check result; The fineness inspection results and / or the color matching inspection results and / or the coding inspection results are combined to obtain the component quality inspection results.

14. A point cloud integration and model library compliance quality inspection and management device, characterized in that, The device includes: The acquisition module is used to acquire the point cloud data to be aligned and the target building model; The registration module is used to register the point cloud data to be registered to the reference coordinate system, using the coordinate system where the target building model is located as the reference coordinate system, to obtain the registered point cloud data. The quality inspection module is used to associate the target building model and the registration point cloud data into a pre-built model library, and to perform compliance quality inspection on the building models to be inspected in the model library to obtain multi-dimensional quality inspection results. The generation module is used to generate a quality inspection report based on the multi-dimensional quality inspection results.

15. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform a point cloud integration and model library compliance quality inspection management method according to any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the point cloud integration and model library compliance quality inspection management method according to any one of claims 1 to 13.

17. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute a point cloud integration and model library compliance quality inspection management method according to any one of claims 1 to 13.

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