Intelligent detection and identification method and system for building construction

Abstract

The embodiment of the invention relates to the technical field of building detection, and discloses an intelligent detection method for building construction, and the method comprises the steps: calculating the space coordinate information of a corresponding indoor laser point cloud based on the collection position of a laser scanner and the indoor laser point cloud; determining a clustering range according to a certain coordinate on a randomly selected wall surface and a set width, and determining a to-be-detected interface; and determining a display reference interface according to the space coordinate information of the laser point clouds in the to-be-detected interface, and comparing the space coordinate information of all indoor laser point clouds at the corresponding to-be-detected interface with the display reference interface to determine color values of the corresponding indoor laser point clouds. According to the intelligent detection method for building construction in the embodiment of the invention, three-dimensional point cloud acquisition is carried out after each stage of building construction is completed to construct a corresponding point cloud model, and finally, the numerical value of each to-be-measured parameter is determined based on the point cloud parameter; the time consumed by repeated detection is reduced, and the overall detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building detection, and in particular to an intelligent detection method and device for building construction. Background Art

[0002] At present, traditional building measurement generally adopts manual measurement, which has the following pain points: First, the overall measurement is time-consuming and labor-intensive, and manual participation and recording are required at each stage; and because there are many people involved in the entire construction process, if the data quality cannot be guaranteed, multiple parties will have to repeatedly verify the same data, which will prolong the entire construction cycle; second, because the specific implementation is carried out in a local collection method, it is impossible to fully reflect the overall construction quality of the building; and in the subsequent sorting, a lot of data will be generated and unified and effective management cannot be achieved. Therefore, designing a solution that can efficiently complete the management and detection of the construction process has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0003] In response to the above-mentioned defects, an embodiment of the present invention discloses an intelligent detection method for building construction, which can efficiently realize construction detection at all stages of building construction, greatly improve the overall construction efficiency, and provide users with more dimensional data for reference.

[0004] The first aspect of the embodiment of the present invention discloses an intelligent detection method for building construction, comprising:

[0005] Use 3D laser scanners to collect 3D point clouds of the indoor environment after each stage of construction to obtain the corresponding indoor laser point clouds;

[0006] Calculating the spatial coordinate information of the corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determining the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information;

[0007] Determine a clustering range according to a coordinate on a randomly selected wall and a set width, perform a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, vertical coordinate or vertical coordinate of the interface to be detected is within the preset range;

[0008] Determine a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and compare the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud;

[0009] The corresponding indoor laser point cloud is displayed according to the color value and the spatial coordinate information to obtain a point cloud distribution map; the flatness data of the corresponding wall is determined according to the point cloud distribution map, and the numerical value of each measurement parameter is determined according to the parameter information of each interface to be detected.

[0010] As an optional implementation, in the first aspect of the embodiment of the present invention, determining the display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud, includes:

[0011] When the interface to be detected is an interface to be detected in a first direction, acquiring an azimuth coordinate set associated with the laser point cloud in the interface to be detected and the first direction according to the first direction;

[0012] Acquire the azimuth coordinate data with the same value and the largest number in the azimuth coordinate set, and construct a corresponding display reference interface based on the azimuth coordinate data;

[0013] Compare the spatial coordinate information of all indoor laser point clouds at the interface to be detected with the display reference interface, and if the spatial coordinate information of the corresponding indoor laser point cloud is higher than the display reference interface, configure the first color parameter for the corresponding laser point cloud; if the spatial coordinate information of the corresponding indoor laser point cloud is lower than the display reference interface, configure the second color parameter for the corresponding laser point cloud;

[0014] The color depth value is determined according to the difference between the spatial coordinate information of the laser point cloud and the display reference page, and the corresponding color parameter is obtained according to the color depth value, the first color parameter and the second color parameter.

[0015] As an optional implementation, in the first aspect of the embodiment of the present invention, the various stages in the construction include a concrete stage, a masonry engineering stage, a plastering engineering stage, and a putty application stage; the flatness data of the corresponding wall surface is determined according to the point cloud distribution map, and the numerical values ​​of various measurement parameters are determined by calculation according to the parameter information of each interface to be detected, including:

[0016] Determine the wall verticality, wall flatness, squareness, top plate horizontality, external window opening extreme difference and bay depth of the corresponding structure at the concrete stage according to the point cloud distribution map and parameter information of each interface to be detected;

[0017] Determine the verticality of the masonry, the flatness of the masonry surface and the extreme difference of the external window opening of the corresponding structure in the masonry engineering stage according to the point cloud distribution map and the parameter information of each interface to be detected;

[0018] According to the point cloud distribution map and the parameter information of each interface to be detected, the verticality of the plastered facade, the flatness of the plastered surface, the squareness of the plastered room, the straightness of the plastered corners, the extreme difference of the door opening width, the clear height of the plastered room, the horizontality of the plastered top plate, the size of the plastered window edge, the elevation difference of the bottom box in the same room and the depth of the bay are determined for the corresponding structure in the plastering engineering stage;

[0019] According to the point cloud distribution map and the parameter information of each interface to be detected, the verticality of the putty facade, the flatness of the putty surface, the horizontality / flatness of the ceiling, the three sides and two lines, the squareness of the putty corners, the straightness of the putty corners and the squareness of the putty room of the corresponding structure in the puttying stage are determined.

[0020] As an optional implementation, in the first aspect of the embodiment of the present invention, after the three-dimensional point cloud acquisition of the indoor environment after each stage of the building construction is completed by the three-dimensional laser scanner to obtain the corresponding indoor laser point cloud, it also includes:

[0021] The indoor environment after each stage of the building construction is completed is imaged by a camera to obtain an indoor environment image, wherein the indoor environment image includes a wall image, a ceiling image and a ground image;

[0022] The indoor environment image is input into the classification and recognition model to identify foreign objects. When a foreign object is detected on the surface of the indoor environment image, a data clearing operation is performed on the point cloud position associated with the foreign object, and the wall image, ceiling image and ground image after the foreign object is cleared are saved;

[0023] After determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes:

[0024] The point cloud distribution map and the corresponding indoor environment image are displayed.

[0025] As an optional implementation, in the first aspect of the embodiment of the present invention, after determining the flatness data of the corresponding wall according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, it also includes:

[0026] The construction quality score is calculated based on a preset quality assessment formula to obtain a corresponding construction quality score; the quality assessment formula is:

[0027]

[0028] Among them, a i is the weight of the measurement indicator, w m is the unqualified point value of the corresponding measurement index; w k is the total measurement value of the corresponding measurement index, and X is the construction quality score.

[0029] As an optional implementation manner, in the first aspect of the embodiment of the present invention, after comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud, it also includes:

[0030] Based on the preset color grading logic, the color values ​​of the laser point cloud are graded to determine the color depth level of the corresponding indoor laser point cloud; the color depth level includes 7 levels of color depth; each level of color is associated with a corresponding value interval;

[0031] The image display operation is performed on the corresponding indoor laser point cloud according to the color value and the spatial coordinate information to obtain a point cloud distribution map, including:

[0032] An image display operation is performed on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map.

[0033] As an optional implementation manner, in the first aspect of the embodiment of the present invention, after performing an image display operation on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map, it also includes:

[0034] The point cloud distribution map obtained after the concrete stage is completed is input into the burst point prediction model to obtain the burst point distribution maps of the masonry engineering stage, the plastering engineering stage and the puttying stage; the burst point prediction model is constructed by the following steps:

[0035] Get images of various stages of building construction and corresponding color samples;

[0036] Based on the pre-built feature extraction module, feature extraction is performed on the color sample images at each stage of building construction to obtain the corresponding training feature information;

[0037] The various stages of building construction, the corresponding color sample images and training feature information are input into the pre-built prediction model for training to obtain the burst point prediction model.

[0038] A second aspect of an embodiment of the present invention discloses an intelligent detection system for building construction, comprising:

[0039] 3D acquisition module: used to acquire the 3D point cloud of the indoor environment after each stage of construction through a 3D laser scanner to obtain the corresponding indoor laser point cloud;

[0040] A calculation module: used to calculate the spatial coordinate information of the corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determine the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information;

[0041] Interface determination module: used to determine the clustering range according to a coordinate on a randomly selected wall and a set width, and to perform a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, vertical coordinate or vertical coordinate of the interface to be detected is within the preset range;

[0042] A reference determination module is used to determine a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and compare the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud;

[0043] Measurement and determination module: used to perform image display operation on the corresponding indoor laser point cloud according to the color value and spatial coordinate information to obtain a point cloud distribution map; and determine the flatness data of the corresponding wall according to the point cloud distribution map and calculate according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter.

[0044] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the intelligent detection method for construction disclosed in the first aspect of the embodiment of the present invention.

[0045] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the intelligent detection method for building construction disclosed in the first aspect of an embodiment of the present invention.

[0046] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0047] The intelligent detection method for building construction in the embodiment of the present invention constructs a corresponding point cloud model by collecting three-dimensional point clouds after each stage of building construction is completed, and finally determines the numerical size of each parameter to be measured based on the point cloud parameters, thereby reducing the time spent on repeated detection and improving the overall detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flow chart of the intelligent detection method for building construction disclosed in an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a flow chart for determining display color disclosed in an embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of a flow chart of measurement parameter calculation disclosed in an embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the process of image acquisition and recognition disclosed in an embodiment of the present invention;

[0053] Figure 5 is a schematic diagram showing the exterior facade of a building disclosed in an embodiment of the present invention;

[0054] Figure 6 It is a display schematic diagram of the indicator data calculation disclosed in the embodiment of the present invention;

[0055] Figure 7 is a display schematic diagram of a bedroom wall disclosed in an embodiment of the present invention;

[0056] Figure 8 is a schematic diagram of display results of measurement parameters disclosed in an embodiment of the present invention;

[0057] Fig. 9 It is a structural schematic diagram of an intelligent detection device for building construction provided by an embodiment of the present invention;

[0058] Fig.10 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] Traditional building measurement generally adopts manual measurement, which has the following pain points: First, the overall measurement is time-consuming and labor-intensive, and manual participation and manual recording are required at each stage; and because there are many people involved in the entire construction process, if the data quality cannot be guaranteed, multiple parties will have to repeatedly verify the same data, which prolongs the entire construction cycle; second, because the specific implementation adopts a local collection method, it is impossible to fully reflect the overall construction quality of the building; and a large amount of data will be generated during the subsequent sorting, and unified and effective management cannot be achieved. Based on this, the embodiment of the present invention discloses an intelligent detection method, device, electronic device and storage medium for building construction, which constructs a corresponding point cloud model by collecting three-dimensional point clouds after each stage of building construction is completed, and finally determines the numerical value of each parameter to be measured based on the point cloud parameters; reduces the time spent on repeated detection and improves the overall detection efficiency.

[0062] Embodiment 1

[0063] See also Figure 1 , Figure 1It is a flow chart of the intelligent detection method for construction disclosed in the embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the intelligent detection method based on building construction includes the following steps:

[0064] S101: using a 3D laser scanner to collect 3D point clouds of the indoor environment after each stage of the building construction is completed to obtain corresponding indoor laser point clouds;

[0065] In the embodiment of the present invention, the 3D point cloud is collected mainly by a 3D laser scanner. The 3D laser scanning setting can greatly improve the efficiency of data collection, making it possible for a single person to complete the operation that was previously impossible. And because objective data scanning is used, when data verification is required in other subsequent stages, the corresponding data can be directly called for data verification without on-site measurement, thereby improving the overall construction efficiency.

[0066] More preferably, Figure 4 is a schematic diagram of the process of image acquisition and recognition disclosed in an embodiment of the present invention; Figure 4 As shown, after the three-dimensional point cloud acquisition of the indoor environment after each stage of the building construction is completed by the three-dimensional laser scanner to obtain the corresponding indoor laser point cloud, it also includes:

[0067] S1011: acquiring images of the indoor environment after each stage of the building construction by a camera to obtain indoor environment images, wherein the indoor environment images include wall images, ceiling images, and ground images;

[0068] S1012: inputting the indoor environment image into a classification and recognition model to identify foreign objects. When a foreign object is detected on the surface of the indoor environment image, performing a data clearing operation on the point cloud position associated with the foreign object, and saving the wall image, ceiling image and ground image after the foreign object is cleared.

[0069] The above steps are mainly for clearing the corresponding foreign objects when they appear in the scene. For example, when a small stone is identified on the ground or a stone is forgotten to be cleared on the wall, the presence of the stone will reduce the overall measurement accuracy, which is also extremely detrimental to the accuracy of subsequent burst point prediction. Therefore, foreign object removal is required to clear the corresponding points.

[0070] After determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes:

[0071] S106: Displaying the point cloud distribution map and the corresponding indoor environment image.

[0072] In the specific implementation, not only a single display image can be provided for users to view as a reference, but also a comprehensive display can be combined with the corresponding indoor environment image, so that users can have a more intuitive viewing experience; rather than just a simple point cloud thermal distribution map, it can more effectively help users grasp the progress.

[0073] S102: Calculating spatial coordinate information of a corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determining the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information;

[0074] S103: determining a clustering range according to a coordinate on a randomly selected wall and a set width, and performing a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, the vertical coordinate, or the vertical coordinate of the interface to be detected is within the preset range;

[0075] S104: determining a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud;

[0076] More preferably, Figure 2 is a schematic diagram of a flow chart of display color determination disclosed in an embodiment of the present invention; Figure 2 As shown, the display reference interface is determined according to the spatial coordinate information of the laser point cloud in the interface to be detected, and the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected is compared with the display reference interface to determine the color value of the corresponding indoor laser point cloud, including:

[0077] S1041: When the interface to be detected is an interface to be detected in a first direction, a set of azimuth coordinates associated with the laser point cloud in the interface to be detected and the first direction is obtained according to the first direction; the first direction here can be horizontal or vertical, etc., which is intended to represent the laser point cloud coordinates at a side wall, floor or ceiling in the room. Only after knowing the coordinates of the corresponding interface can further depth judgment be made.

[0078] S1042: Obtain the azimuth coordinate data with the same value and the largest number in the azimuth coordinate set, and construct a corresponding display reference interface based on the azimuth coordinate data; that is, when performing a specific implementation, it is necessary to first determine the reference plane, and the reference plane here can be a set reference plane, but a more convenient measurement method is to use coordinate data with the same value to determine the reference interface; for example, when it is necessary to judge the point cloud data on the ceiling, what needs to be judged is the state of the vertical coordinate of the point cloud data associated with the ceiling; extract the surface of the ceiling point cloud with the most identical vertical coordinates as the reference plane, and the reference plane can be used as the basis for subsequent data judgment. Here, the surface with the most identical vertical coordinates can also be directly used as the reference coordinate for subsequent comparison and judgment.

[0079] S1043: Compare the spatial coordinate information of all indoor laser point clouds at the interface to be detected with the display reference interface; if the spatial coordinate information of the corresponding indoor laser point cloud is higher than the display reference interface, configure the first color parameter for the corresponding laser point cloud; if the spatial coordinate information of the corresponding indoor laser point cloud is lower than the display reference interface, configure the second color parameter for the corresponding laser point cloud; S1044: Determine the color depth value according to the difference between the spatial coordinate information of the laser point cloud and the display reference page, and obtain the corresponding color parameter according to the color depth value, the first color parameter and the second color parameter.

[0080] After the corresponding reference plane is determined, it can be compared and judged, and then the corresponding color can be configured. When the corresponding coordinates are relatively concave, blue is configured, and when the corresponding coordinates are relatively convex, red is configured. And the color depth can be adjusted in combination with the numerical difference. When adjusting the color application, there are many ways to implement it, such as the color stepless setting method or the multi-level segmentation setting. The stepless color setting means that each depth value corresponds to a color, and different values ​​correspond to different color depths; the multi-level segmentation setting means that each value interval corresponds to a color, which means that although the depth values ​​are different, they may point to the same color. The stepless method can better reflect the difference; the hierarchical segmentation method can make the final rendering display faster.

[0081] S105: performing an image display operation on the corresponding indoor laser point cloud according to the color value and the spatial coordinate information to obtain a point cloud distribution map; determining the flatness data of the corresponding wall according to the point cloud distribution map, and performing calculations according to the parameter information of each interface to be detected to determine the numerical values ​​of various measurement parameters.

[0082] like Figure 5 As shown in the figure, it is to obtain and compare the corresponding parameters of the exterior wall. When it is implemented, it only takes 2 minutes to get the exterior wall flatness report, and can combine the point cloud distribution map and the measurement parameter results to assist in providing rectification suggestions to improve the overall building appearance. Figure 7 As shown, it is the measurement display result of the bedroom wall, where 1 refers to the specific measurement parameters, 2 represents the wall protrusion, the data in the box represents the protrusion number, and its value is set according to the actual situation. Although it is not clearly shown in the figure, it is known that the corresponding numerical display can be made. It is only a schematic result; 3 represents the wall depression, and the same box has a corresponding value to indicate the specific depression depth, and even different values ​​can be highlighted to make it more convenient for users to view.

[0083] More preferably, Figure 3 is a schematic diagram of the flow chart of the measurement parameter calculation disclosed in the embodiment of the present invention; Figure 3 As shown, the various stages in the construction include the concrete stage, the masonry engineering stage, the plastering engineering stage and the puttying stage; the flatness data of the corresponding wall surface is determined according to the point cloud distribution map, and the numerical values ​​of various measurement parameters are determined according to the parameter information of each interface to be detected, including:

[0084] S1051: Determine the wall verticality, wall flatness, squareness, top plate horizontality, external window opening extremes and bay depth of the corresponding structure in the concrete stage according to the point cloud distribution map and the parameter information of each interface to be detected; the multiple parameters here have their corresponding standard ranges, such as the standard range for wall verticality is verticality less than or equal to 8mm; the standard range for wall flatness is flatness less than or equal to 8mm; the standard range for squareness is squareness less than or equal to 10mm; the standard range for top plate horizontality is horizontality less than or equal to 10mm; the standard range for external window opening extremes is opening extremes between plus or minus 15mm; the standard range for bay depth is depth less than or equal to 10mm.

[0085] S1052: Determine the verticality of the masonry, the flatness of the masonry surface and the extreme value of the external window opening of the corresponding structure in the masonry engineering stage according to the point cloud distribution map and the parameter information of each interface to be detected; the multiple parameters here have their corresponding standard ranges, such as the standard range of the verticality of the masonry is that the verticality is less than or equal to 5mm; the standard range of the flatness of the masonry is that the flatness is less than or equal to 8mm; the standard range of the extreme value of the external window opening is that the extreme value of the opening is between plus or minus 15mm.

[0086] S1053: Determine the verticality of the plastering facade, the flatness of the plastering surface, the squareness of the plastering room, the straightness of the plastering corners, the extreme difference of the door opening width, the clear height of the plastering room, the horizontality of the plastering top plate, the big and small heads of the plastering window edges, the elevation difference of the bottom box in the same room and the depth of the bay according to the point cloud distribution map and the parameter information of each interface to be detected during the plastering engineering stage of the corresponding structure; the multiple parameters here have their corresponding standard ranges, such as the standard range of the verticality of the plastering is that the verticality is less than or equal to 4mm; the standard range of the flatness of the plastering is that the flatness is less than or equal to 4mm; the squareness of the plastering room (vertical plastering is measured by the reference plastering) is less than or equal to 4mm; The standard range is squareness less than or equal to 10mm; the standard range of straightness of plastered corners is straightness less than or equal to 4mm; the standard range of door opening width extreme difference is width extreme difference between plus or minus 5mm; the plastered indoor net height (based on the one-meter line of the building) is between plus or minus 20mm; the standard range of plastered ceiling horizontality is horizontality less than or equal to 10mm; the standard range of plastered window edges is less than or equal to 6mm; the height difference of the base box in the same room is less than or equal to 5mm; the standard range of bay depth is depth less than or equal to 10mm, and the standard range of door opening height extreme difference and design value deviation is between plus or minus 10mm.

[0087] S1054: Determine the verticality of the puttying facade, the flatness of the puttying surface, the horizontality / flatness of the ceiling, the three sides and two lines, the squareness of the puttying yin and yang corners, the straightness of the puttying yin and yang corners, and the squareness of the puttying room according to the point cloud distribution map and the parameter information of each interface to be detected. The standard interval of the verticality of the puttying facade is less than or equal to 3mm, the standard interval of the puttying surface flatness is less than or equal to 3mm, the standard interval of the ceiling horizontality / flatness is less than or equal to 10mm, the standard interval of the three sides and two lines (baseboards, door faces) is less than or equal to 2mm, the standard interval of the squareness of the puttying yin and yang corners is less than or equal to 3mm, the standard interval of the puttying yin and yang corner straightness is less than or equal to 3mm, and the standard interval of the puttying room squareness is less than or equal to 10mm. Perform a square-finding operation on the kitchen and bathroom to determine whether they are square, and use the Pythagorean theorem to calculate and determine whether they are square.

[0088] More preferably, after determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes:

[0089] The construction quality score is calculated based on a preset quality assessment formula to obtain a corresponding construction quality score; the quality assessment formula is:

[0090]

[0091] Among them, a i is the weight of the measurement indicator, w m is the unqualified point value of the corresponding measurement index; w k is the total measurement value of the corresponding measurement index, and X is the construction quality score. Here a1 is the weight coefficient of the wall flatness, a2 is the weight coefficient of the wall verticality, a3 is the weight coefficient of the opening size deviation, a4 is the weight coefficient of the door and window opening size extreme difference, a5 is the weight coefficient of the squareness, a6 is the weight coefficient of the top plate horizontality, a7 is the weight coefficient of the ground horizontality, a8 is the weight coefficient of the Yin-Yang angle, and a9 is the weight coefficient of the bay depth; the final score item is determined by comparing the qualified points with the unqualified points of the above indicator categories, and the weights of the above multiple parameters can be set to 25%, 25%, 5%, 10%, 5%, 5%, 10%, 20%, and 10%. The corresponding quality score can be calculated using the above parameters, and the specific display results are as follows: Figure 6 In addition to the above result presentation methods, you can also Figure 8 The presentation method shown is shown, and the value can be presented in real time at the customer's terminal for the user to view.

[0092] More preferably, after comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud, the method further includes:

[0093] Based on the preset color grading logic, the color values ​​of the laser point cloud are graded to determine the color depth level of the corresponding indoor laser point cloud; the color depth level includes 7 levels of color depth; each level of color is associated with a corresponding value interval;

[0094] The image display operation is performed on the corresponding indoor laser point cloud according to the color value and the spatial coordinate information to obtain a point cloud distribution map, including:

[0095] An image display operation is performed on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map.

[0096] A multi-level setting method is used here to match the colors of different intervals. This method can make the characteristics of the corresponding point cloud distribution map more obvious, and it is also convenient for subsequent burst point prediction.

[0097] More preferably, after performing an image display operation on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map, the method further includes:

[0098] The point cloud distribution map obtained after the concrete stage is completed is input into the burst point prediction model to obtain the burst point distribution maps of the masonry engineering stage, the plastering engineering stage and the puttying stage; the burst point prediction model is constructed by the following steps:

[0099] Get images of various stages of building construction and corresponding color samples;

[0100] Based on the pre-built feature extraction module, feature extraction is performed on the color sample images at each stage of building construction to obtain the corresponding training feature information;

[0101] The various stages of building construction, the corresponding color sample images and training feature information are input into the pre-built prediction model for training to obtain the burst point prediction model.

[0102] The obtained point cloud distribution map is input into the flash point prediction model to make the corresponding flash point prediction and then predict the flash points that may exist in the subsequent stage; it is convenient for the construction team to find the corresponding problems in time and make timely remedies, which can improve the overall construction efficiency and speed from a higher level, and greatly reduce the occurrence of unqualified situations.

[0103] The intelligent detection method for building construction in the embodiment of the present invention constructs a corresponding point cloud model by collecting three-dimensional point clouds after each stage of building construction is completed, and finally determines the numerical size of each parameter to be measured based on the point cloud parameters, thereby reducing the time spent on repeated detection and improving the overall detection efficiency.

[0104] Embodiment 2

[0105] See also Fig. 9 , Fig. 9 Schematic diagram of the structure of the intelligent detection device for building construction disclosed in the embodiment of the present invention. Fig. 9 As shown, the intelligent detection device for building construction may include:

[0106] 3D acquisition module: used to acquire the 3D point cloud of the indoor environment after each stage of construction through a 3D laser scanner to obtain the corresponding indoor laser point cloud;

[0107] A calculation module: used to calculate the spatial coordinate information of the corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determine the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information;

[0108] Interface determination module: used to determine the clustering range according to a coordinate on a randomly selected wall and a set width, and to perform a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, vertical coordinate or vertical coordinate of the interface to be detected is within the preset range;

[0109] A reference determination module is used to determine a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and compare the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud;

[0110] Measurement and determination module: used to perform image display operation on the corresponding indoor laser point cloud according to the color value and spatial coordinate information to obtain a point cloud distribution map; and determine the flatness data of the corresponding wall according to the point cloud distribution map and calculate according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter.

[0111] More preferably, the step of determining the display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud includes:

[0112] A position acquisition module: when the interface to be detected is an interface to be detected in a first direction, acquires a position coordinate set of the laser point cloud in the interface to be detected associated with the first direction according to the first direction;

[0113] A reference acquisition module is used to acquire the azimuth coordinate data with the same value and the largest number in the azimuth coordinate set, and to construct a corresponding display reference interface based on the azimuth coordinate data;

[0114] Comparison module: used for comparing the spatial coordinate information of all indoor laser point clouds at the interface to be detected with the display reference interface, if the spatial coordinate information of the corresponding indoor laser point cloud is higher than the display reference interface, configuring the first color parameter for the corresponding laser point cloud, if the spatial coordinate information of the corresponding indoor laser point cloud is lower than the display reference interface, configuring the second color parameter for the corresponding laser point cloud;

[0115] Depth determination module: used to determine the color depth value according to the difference between the spatial coordinate information of the laser point cloud and the display reference page, and obtain the corresponding color parameter according to the color depth value, the first color parameter and the second color parameter.

[0116] More preferably, the various stages in the construction include a concrete stage, a masonry engineering stage, a plastering engineering stage and a puttying stage; the flatness data of the corresponding wall surface is determined according to the point cloud distribution map, and the numerical values ​​of various measurement parameters are determined by calculation according to the parameter information of each interface to be detected, including:

[0117] The first parameter calculation module is used to determine the wall verticality, wall flatness, squareness, top plate horizontality, external window opening extreme difference and bay depth of the corresponding structure in the concrete stage according to the point cloud distribution map and the parameter information of each interface to be detected;

[0118] The second parameter calculation module is used to determine the verticality of the masonry, the flatness of the masonry surface and the extreme difference of the external window opening of the corresponding structure in the masonry engineering stage according to the point cloud distribution map and the parameter information of each interface to be detected;

[0119] The third parameter calculation module is used to determine the verticality of the plastered facade, the flatness of the plastered surface, the squareness of the plastered room, the straightness of the plastered yin and yang angles, the extreme difference of the door opening width, the clear height of the plastered room, the horizontality of the plastered top plate, the size of the plastered window edge, the elevation difference of the bottom box in the same room and the depth of the bay according to the point cloud distribution map and the parameter information of each interface to be detected;

[0120] The fourth parameter calculation module is used to determine the verticality of the putty facade, the flatness of the putty surface, the horizontality / flatness of the ceiling, the three sides and two lines, the squareness of the putty corners, the straightness of the putty corners and the squareness of the putty room of the corresponding structure in the puttying stage according to the point cloud distribution map and the parameter information of each interface to be detected.

[0121] More preferably, after the three-dimensional point cloud acquisition of the indoor environment after each stage of the building construction is completed by the three-dimensional laser scanner to obtain the corresponding indoor laser point cloud, the method further includes:

[0122] Image acquisition module: used to acquire images of the indoor environment after each stage of building construction through a camera to obtain indoor environment images, wherein the indoor environment images include wall images, ceiling images and ground images;

[0123] Foreign object recognition module: used for inputting the indoor environment image into the classification recognition model to perform foreign object recognition. When a foreign object is detected on the surface of the indoor environment image, a data clearing operation is performed on the point cloud position associated with the foreign object, and the wall image, ceiling image and ground image after the foreign object is cleared are saved;

[0124] After determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes:

[0125] Display module: used to display the point cloud distribution map and the corresponding indoor environment image.

[0126] More preferably, after determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes:

[0127] The construction quality score is calculated based on a preset quality assessment formula to obtain a corresponding construction quality score; the quality assessment formula is:

[0128]

[0129] Among them, a i is the weight of the measurement indicator, w m is the unqualified point value of the corresponding measurement index; w k is the total measurement value of the corresponding measurement index, and X is the construction quality score.

[0130] More preferably, after comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud, the method further includes:

[0131] Grading processing module: used to grade the color values ​​of the laser point cloud based on the preset color grading logic to determine the color depth level of the corresponding indoor laser point cloud; the color depth level includes 7 levels of color depth division; each level of color is associated with the corresponding value interval;

[0132] The image display operation is performed on the corresponding indoor laser point cloud according to the color value and the spatial coordinate information to obtain a point cloud distribution map, including:

[0133] An image display operation is performed on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map.

[0134] More preferably, after performing an image display operation on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map, the method further includes:

[0135] The burst point prediction module is used to input the point cloud distribution map obtained after the concrete stage is completed into the burst point prediction model to obtain the burst point distribution map of the masonry engineering stage, the plastering engineering stage and the puttying stage; the burst point prediction model is constructed by the following steps:

[0136] Sample acquisition module: used to obtain various stages of building construction and corresponding color sample images;

[0137] Feature extraction module: used to extract features from color sample images at various stages of building construction based on a pre-built feature extraction module to obtain corresponding training feature information;

[0138] Model building module: used to input various stages of building construction, corresponding color sample images and training feature information into a pre-built prediction model for training to obtain a flash point prediction model.

[0139] The intelligent detection method for building construction in the embodiment of the present invention constructs a corresponding point cloud model by collecting three-dimensional point clouds after each stage of building construction is completed, and finally determines the numerical size of each parameter to be measured based on the point cloud parameters, thereby reducing the time spent on repeated detection and improving the overall detection efficiency.

[0140] Embodiment 3

[0141] See also Fig.10 , Fig.10 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Fig.10 As shown, the electronic device may include:

[0142] A memory 510 storing executable program codes;

[0143] a processor 520 coupled to the memory 510;

[0144] The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the intelligent detection method for building construction in the first embodiment.

[0145] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the intelligent detection method for building construction in the first embodiment.

[0146] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the intelligent detection method for building construction in the first embodiment.

[0147] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the intelligent detection method for building construction in embodiment one.

[0148] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0152] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0153] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0154] The above is a detailed introduction to the intelligent detection method, device, electronic device and storage medium for construction disclosed in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent detection method for building construction, characterized in that: include: Use 3D laser scanners to collect 3D point clouds of the indoor environment after each stage of construction to obtain the corresponding indoor laser point clouds; Calculating the spatial coordinate information of the corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determining the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information; Determine a clustering range according to a coordinate on a randomly selected wall and a set width, perform a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, vertical coordinate or vertical coordinate of the interface to be detected is within the preset range; Determine a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and compare the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud; The corresponding indoor laser point cloud is displayed according to the color value and the spatial coordinate information to obtain a point cloud distribution map; the flatness data of the corresponding wall is determined according to the point cloud distribution map, and the numerical value of each measurement parameter is determined according to the parameter information of each interface to be detected.

2. The intelligent detection method for building construction according to claim 1, characterized in that: The step of determining a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud includes: When the interface to be detected is an interface to be detected in a first direction, acquiring an azimuth coordinate set associated with the laser point cloud in the interface to be detected and the first direction according to the first direction; Acquire the azimuth coordinate data with the same value and the largest number in the azimuth coordinate set, and construct a corresponding display reference interface based on the azimuth coordinate data; Compare the spatial coordinate information of all indoor laser point clouds at the interface to be detected with the display reference interface, and if the spatial coordinate information of the corresponding indoor laser point cloud is higher than the display reference interface, configure the first color parameter for the corresponding laser point cloud; if the spatial coordinate information of the corresponding indoor laser point cloud is lower than the display reference interface, configure the second color parameter for the corresponding laser point cloud; The color depth value is determined according to the difference between the spatial coordinate information of the laser point cloud and the display reference page, and the corresponding color parameter is obtained according to the color depth value, the first color parameter and the second color parameter.

3. The intelligent detection method for building construction as claimed in claim 2, characterized in that: The various stages of the construction include the concrete stage, the masonry stage, the plastering stage and the puttying stage; the flatness data of the corresponding wall surface is determined according to the point cloud distribution map, and the numerical values ​​of various measurement parameters are determined by calculation according to the parameter information of each interface to be detected, including: Determine the wall verticality, wall flatness, squareness, top plate horizontality, external window opening extreme difference and bay depth of the corresponding structure at the concrete stage according to the point cloud distribution map and parameter information of each interface to be detected; Determine the verticality of the masonry, the flatness of the masonry surface and the extreme difference of the external window opening of the corresponding structure in the masonry engineering stage according to the point cloud distribution map and the parameter information of each interface to be detected; According to the point cloud distribution map and the parameter information of each interface to be detected, the verticality of the plastered facade, the flatness of the plastered surface, the squareness of the plastered room, the straightness of the plastered corners, the extreme difference of the door opening width, the clear height of the plastered room, the horizontality of the plastered top plate, the size of the plastered window edge, the elevation difference of the bottom box in the same room and the depth of the bay are determined for the corresponding structure in the plastering engineering stage; According to the point cloud distribution map and the parameter information of each interface to be detected, the verticality of the putty facade, the flatness of the putty surface, the horizontality / flatness of the ceiling, the three sides and two lines, the squareness of the putty corners, the straightness of the putty corners and the squareness of the putty room of the corresponding structure in the puttying stage are determined.

4. The intelligent detection method for building construction according to claim 1, characterized in that: After the three-dimensional point cloud acquisition of the indoor environment after each stage of the building construction is completed by the three-dimensional laser scanner to obtain the corresponding indoor laser point cloud, it also includes: The indoor environment after each stage of the building construction is completed is imaged by a camera to obtain an indoor environment image, wherein the indoor environment image includes a wall image, a ceiling image and a ground image; The indoor environment image is input into the classification and recognition model to identify foreign objects. When a foreign object is detected on the surface of the indoor environment image, a data clearing operation is performed on the point cloud position associated with the foreign object, and the wall image, ceiling image and ground image after the foreign object is cleared are saved; After determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes: The point cloud distribution map and the corresponding indoor environment image are displayed.

5. The intelligent detection method for building construction according to claim 1, characterized in that: After determining the flatness data of the corresponding wall surface according to the point cloud distribution map and calculating according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter, the method further includes: The construction quality score is calculated based on a preset quality assessment formula to obtain a corresponding construction quality score; the quality assessment formula is: Among them, a i is the weight of the measurement indicator, w m is the unqualified point value of the corresponding measurement index; w k is the total measurement value of the corresponding measurement index, and X is the construction quality score.

6. The intelligent detection method for building construction according to claim 1, characterized in that: After comparing the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud, the method further includes: Based on the preset color grading logic, the color values ​​of the laser point cloud are graded to determine the color depth level of the corresponding indoor laser point cloud; the color depth level includes 7 levels of color depth; each level of color is associated with a corresponding value interval; The image display operation is performed on the corresponding indoor laser point cloud according to the color value and the spatial coordinate information to obtain a point cloud distribution map, including: An image display operation is performed on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map.

7. The intelligent detection method for building construction according to claim 6, characterized in that: After performing an image display operation on the corresponding indoor laser point cloud according to the color depth level and the spatial coordinate information to obtain a point cloud distribution map, the method further includes: The point cloud distribution map obtained after the concrete stage is completed is input into the burst point prediction model to obtain the burst point distribution maps of the masonry engineering stage, the plastering engineering stage and the puttying stage; the burst point prediction model is constructed by the following steps: Get images of various stages of building construction and corresponding color samples; Based on the pre-built feature extraction module, feature extraction is performed on the color sample images at each stage of building construction to obtain the corresponding training feature information; The various stages of building construction, the corresponding color sample images and training feature information are input into the pre-built prediction model for training to obtain the burst point prediction model.

8. An intelligent detection system for building construction, characterized in that: include: 3D acquisition module: used to acquire the 3D point cloud of the indoor environment after each stage of construction through a 3D laser scanner to obtain the corresponding indoor laser point cloud; A calculation module: used to calculate the spatial coordinate information of the corresponding indoor laser point cloud based on the acquisition position information of the three-dimensional laser scanner and the indoor laser point cloud; and determine the wall to which the corresponding indoor laser point cloud belongs based on the spatial coordinate information; Interface determination module: used to determine the clustering range according to a coordinate on a randomly selected wall and a set width, and to perform a clustering operation on the indoor laser point cloud according to the clustering range and the spatial coordinate information to obtain an interface to be detected, wherein the interface to be detected is composed of laser point clouds within a preset range, and any one of the horizontal coordinate, vertical coordinate or vertical coordinate of the interface to be detected is within the preset range; A reference determination module is used to determine a display reference interface according to the spatial coordinate information of the laser point cloud in the interface to be detected, and compare the spatial coordinate information of all indoor laser point clouds at the corresponding interface to be detected with the display reference interface to determine the color value of the corresponding indoor laser point cloud; Measurement and determination module: used to perform image display operation on the corresponding indoor laser point cloud according to the color value and spatial coordinate information to obtain a point cloud distribution map; and determine the flatness data of the corresponding wall according to the point cloud distribution map and calculate according to the parameter information of each interface to be detected to determine the numerical value of each measurement parameter.

9. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent detection method for building construction as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the intelligent detection method for building construction according to any one of claims 1 to 7.

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