A green aluminum curtain wall design system and method based on digital modeling

Abstract

The present invention relates to the technical field of data processing, and discloses a green aluminum plate curtain wall design system and method based on digital modeling. First, a point cloud image of the surface of a target building is collected, matching points are screened, a three-dimensional model point cloud data set is obtained, and an initial three-dimensional model of the target building is constructed; secondly, the outer surface of the initial three-dimensional model of the target building is preliminarily identified, and then the discrete point cloud data is simplified to extract the surface of the green aluminum plate curtain wall; then, a green aluminum plate curtain wall objective function is constructed based on the minimum construction cost and the shortest installation time, and a plant root growth optimization algorithm is used to solve and obtain a global optimal solution; finally, a green aluminum plate curtain wall design scheme is generated according to the global optimal solution, and the green aluminum plate curtain wall design of the target building is completed. The present invention achieves the purpose of green aluminum plate curtain wall design by digitally modeling the target building, and the method is accurate and objective.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a green aluminum plate curtain wall design system and method based on digital modeling. Background Art

[0002] Chinese patent CN115510543B discloses a BIM-based modular curtain wall design method and system, which specifically includes: collecting the design data of the target building, using the design data to build an initial BIM model; obtaining the curtain wall demand information of the user, dividing the initial BIM model according to the curtain wall demand information, and obtaining the module division result; then using the measurement equipment to collect the measured dimension data of the target building, using the measured dimension data to compensate the module division result, and obtaining the compensated module division result; using the image acquisition device to collect the target building image, and perform feature recognition to obtain the feature recognition result; combining the feature recognition result and the compensation module division result to generate the curtain wall design assembly plan of the target building. This invention does not optimize the curtain wall design plan, and has problems such as high design cost and environmental protection.

[0003] Traditional curtain wall design methods are usually used as external maintenance structures due to their advantages such as light weight and rich variety. However, due to the lack of the use of composite materials such as green aluminum plates, problems such as unreasonable design and high pollution exist. At the same time, the traditional green aluminum plate curtain wall design method does not use intelligent design methods, which not only results in high cost waste, but also cannot guarantee completion within the optimal construction period. Summary of the invention

[0004] In view of the problems in the related art, the present invention provides a green aluminum plate curtain wall design system and method based on digital modeling to overcome the above-mentioned technical problems existing in the existing related art.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a green aluminum plate curtain wall design method based on digital modeling, comprising the following steps:

[0007] S1, collecting the surface point cloud image of the target building, obtaining the target building surface point cloud image set, screening the matching points in the target building surface point cloud image set, obtaining the 3D model point cloud data set, and then performing 3D modeling on the target building to obtain the initial target building 3D model;

[0008] S2, preliminarily identifying the outer surface of the initial target building three-dimensional model, simplifying the discrete point cloud data, obtaining the final target building three-dimensional model, and then extracting the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface;

[0009] S3, gridding the surface of the green aluminum curtain wall to generate a green aluminum curtain wall grid, and constructing a green aluminum curtain wall objective function based on minimum construction cost and shortest installation time, and using a plant rhizome growth optimization algorithm to solve the green aluminum curtain wall objective function to obtain a global optimal solution;

[0010] S4. Generate a green aluminum curtain wall design solution according to the global optimal solution, and complete the green aluminum curtain wall design for the target building.

[0011] The invention collects point cloud images of the surface of the target building, obtains three-dimensional model point cloud data after matching point screening, and constructs an initial three-dimensional model of the target building; this method can ensure that the point cloud data is matched quickly and effectively, improve the precision and accuracy of the model after screening, reduce the void rate of the model, and facilitate subsequent processing; secondly, the outer surface of the initial target building three-dimensional model is preliminarily identified, and then the discrete point cloud data is simplified, and the outer surface of the model is extracted to obtain the green aluminum curtain wall surface; this method identifies the outer surface, eliminates errors caused by erroneous measurements and calculations, and establishes an accurate model. Considering the first endpoint greatly reduces the calculation time, and extracts the target building. Curtain wall design is carried out on the outer surface, which is convenient, fast and highly applicable; the green aluminum curtain wall grid is regenerated, and the green aluminum curtain wall objective function is constructed based on the minimum construction cost and the shortest installation time, and the plant rhizome growth optimization algorithm is used to solve and obtain the global optimal solution; the minimum green aluminum curtain wall installation cost and time are achieved by establishing the objective function. The plant rhizome growth optimization algorithm imitates the process of plant roots obtaining nutrients. The algorithm has good global search capabilities and fast convergence performance. Compared with other optimization algorithms, it has better optimization accuracy and effectively achieves the optimization purpose; finally, the green aluminum curtain wall design scheme is generated according to the global optimal solution, and the green aluminum curtain wall design of the target building is completed.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, collect a target building video, and combine it with the depth image of the target building to obtain several surface point cloud images of the target building to form a target building surface point cloud image set, select any target building surface point cloud image from the target building surface point cloud image set, record it as a point cloud image to be processed, select any matching point from the point cloud image to be processed, record it as a matching point b, set a neighborhood search radius, take the matching point b as the center, search for matching points within the neighborhood search radius, and obtain a neighborhood matching point set; traverse the neighborhood matching point set, and calculate the weights of the matching points in the neighborhood matching point set, and the calculation formula is as follows:

[0014] ;

[0015] in, represents the jth matching point in the neighborhood matching point set, Represents the weight of the jth matching point;

[0016] According to the weight of the matching point, the covariance matrix and the eigenvalues ​​of the covariance matrix of the matching point in the neighborhood matching point set are calculated, the eigenvalues ​​of the covariance matrix are arranged in descending order, and the top three eigenvalues ​​are selected and recorded as the first eigenvalues. , the second eigenvalue and the third eigenvalue , set a first relationship threshold and a second relationship threshold; establish the relationship between the matching points in the target building surface point cloud image set, when is less than or equal to the first relationship threshold and When the value is less than or equal to the second relationship threshold, the corresponding matching point is recorded as the three-dimensional model point cloud data; until all the target building surface point cloud images in the target building surface point cloud image set are screened, and the three-dimensional model point cloud data set is obtained;

[0017] S12, deleting and matching duplicate point cloud data of the three-dimensional model point cloud data in the three-dimensional model point cloud data set, realizing three-dimensional modeling of the target building, and obtaining an initial three-dimensional model of the target building, the specific steps are as follows:

[0018] S121, arbitrarily selecting three-dimensional model point cloud data corresponding to different target building surface point cloud images from the three-dimensional model point cloud data set, and recording them as three-dimensional model point cloud data subsets respectively. and 3D model point cloud data subset , connect the 3D model point cloud data subset and 3D model point cloud data subset 3D model point cloud data is obtained, and the distance of the 3D model point cloud data is calculated, a distance threshold is set, and the 3D model point cloud data corresponding to the distance of the 3D model point cloud data is greater than the distance threshold is deleted; the repeated 3D model point cloud data of the target building surface point cloud image are deleted in sequence to obtain a processed 3D model point cloud data set;

[0019] S122, establishing a spatial coordinate system for the processed 3D model point cloud data set, taking the 3D model point cloud data in the processed 3D model point cloud data set as the origin, obtaining a plurality of 3D model point cloud data coordinate systems, respectively calculating the translation relationship and rotation relationship of the 3D model point cloud data in the 3D model point cloud data coordinate systems, obtaining a point cloud data translation matrix and a point cloud data rotation matrix, converting the 3D model point cloud data in the 3D model point cloud data coordinate system to the 3D model coordinate system, obtaining converted 3D model point cloud data, and the calculation formula is as follows:

[0020] ;

[0021] in, Represents the converted 3D model point cloud data, Represents the 3D model point cloud data in the processed 3D model point cloud data set, Represents the point cloud data rotation matrix, Represents the point cloud data translation matrix;

[0022] All the three-dimensional model point cloud data in the processed three-dimensional model point cloud data set are converted, and then three-dimensional modeling of the target building is performed according to the converted three-dimensional model point cloud data to establish an initial three-dimensional model of the target building.

[0023] The invention collects point cloud images of the target building surface, obtains three-dimensional model point cloud data after matching point screening, ensures that the point cloud data is matched quickly and effectively, improves the precision and accuracy of the model after screening, reduces the void rate of the model, and constructs an initial three-dimensional model of the target building for subsequent processing.

[0024] Preferably, S2 comprises the following steps:

[0025] S21, preliminarily identifying the outer surface of the initial target building three-dimensional model, and then simplifying the discrete point cloud data to obtain the final target building three-dimensional model, the specific steps are as follows:

[0026] S211, set the rated number of converted three-dimensional model point cloud data included in the outer surface of the initial target building three-dimensional model, recorded as the outer surface point cloud data number threshold; select any three converted three-dimensional model point cloud data in the initial target building three-dimensional model, establish the initial three-dimensional model outer surface, calculate the distance from other converted three-dimensional model point cloud data to the initial three-dimensional model outer surface, and set the distance threshold, add the converted three-dimensional model point cloud data corresponding to the distance less than the distance threshold to the outer surface three-dimensional model point cloud data set; calculate the normal vector of the outer surface three-dimensional model point cloud data set and the normal vector of the initial three-dimensional model outer surface, set the normal vector Vector threshold, when the normal vector of the outer surface 3D model point cloud data set is greater than the normal vector of the initial 3D model outer surface, the corresponding converted 3D model point cloud data is removed from the outer surface 3D model point cloud data set to obtain a final 3D model point cloud data set; compare the final 3D model point cloud data set with the outer surface point cloud data number threshold, when the number of converted 3D model point cloud data in the final 3D model point cloud data set is less than the outer surface point cloud data number threshold, delete the final 3D model point cloud data set, re-perform outer surface recognition, complete preliminary recognition of the outer surface of the initial target building 3D model, and obtain the initial outer surface of the target building;

[0027] S212, arbitrarily select the point cloud data on the initial outer surface of the target building, record it as suspicious discrete point cloud data, set the neighborhood distance, take the suspicious discrete point cloud data as the center, calculate the density of the suspicious discrete point cloud data within the neighborhood distance range, and obtain the neighborhood density; set the density threshold and the distance threshold, when the neighborhood density is greater than the density threshold, calculate the mean of all the point cloud data within the neighborhood distance range, retain the point cloud data and the point cloud data corresponding to the mean difference less than the distance threshold; when the neighborhood density is less than or equal to the density threshold, delete all the point cloud data within the neighborhood distance range, complete the simplification of the discrete point cloud data, obtain the final outer surface of the target building, and construct the final target building three-dimensional model according to the final outer surface of the target building;

[0028] S22, select any outer surface on the final target building three-dimensional model, record it as the outer surface to be extracted, select three consecutive point cloud data on the contour of the outer surface to be extracted , and , forming the external surface point cloud data set , fit the outer surface point cloud data set to obtain a point cloud data straight line, calculate the distances from three point cloud data to the point cloud data straight line, and obtain a point cloud data distance threshold; extend the point cloud data straight line, encounter new point cloud data during the extension process, compare the distance from the new point cloud data to the point cloud data straight line and the point cloud data distance threshold, when the distance from the new point cloud data to the point cloud data straight line is less than the point cloud data distance threshold, add the new point cloud data to the outer surface point cloud data set, otherwise reselect three consecutive point cloud data; fit the outer surface point cloud data set again to obtain a new point cloud data straight line, set a straight line length threshold, and stop extending until the new point cloud data straight line is greater than the straight line length threshold, and obtain the final outer surface point cloud data set, retain the point cloud data at both ends of the final outer surface point cloud data set, connect the point cloud data at both ends to form the contour of the outer surface to be extracted, extract the contours of all outer surfaces on the final target building three-dimensional model, obtain the outer surface of the target building, and record the outer surface of the target building as the green aluminum plate curtain wall surface.

[0029] The invention establishes an accurate model by preliminarily identifying the outer surface of the initial target building's three-dimensional model, eliminating errors caused by erroneous measurements and calculations, and then simplifies the discrete point cloud data and extracts the model's outer surface. Considering the first endpoint greatly reduces the time spent on calculations, and extracting the outer surface of the target building for curtain wall design is convenient, fast and highly applicable.

[0030] Preferably, S3 comprises the following steps:

[0031] S31, establish a two-dimensional coordinate system on the surface of the green aluminum curtain wall, set the rated length and rated width, and grid the surface of the green aluminum curtain wall according to the rated length and rated width to obtain a green aluminum curtain wall grid; obtain the thickness of the green aluminum curtain wall, and calculate the material cost of the green aluminum curtain wall according to the rated length, rated width and thickness; set a set of green aluminum curtain wall installation procedures ,in Indicates the mth green aluminum curtain wall installation process. Construction is performed in the green aluminum curtain wall grid according to the green aluminum curtain wall installation process set. The labor cost of the green aluminum curtain wall installation process is , based on the minimum construction cost, the green aluminum curtain wall installation cost function is established as follows:

[0032] ;

[0033] in, represents the installation cost function of the green aluminum curtain wall, and c represents the material cost of the green aluminum curtain wall;

[0034] Set The construction time of the green aluminum curtain wall installation process is The shortest construction time and the longest construction time are and , the green aluminum curtain wall installation time function is constructed based on the shortest installation time as follows:

[0035] , ;

[0036] in, Represents the installation time function of the green aluminum curtain wall;

[0037] Combining the green aluminum curtain wall installation cost function and the green aluminum curtain wall installation time function, a green aluminum curtain wall objective function is obtained;

[0038] S32, taking the green aluminum curtain wall objective function as the fitness function, using the plant rhizome growth optimization algorithm to solve the green aluminum curtain wall objective function, and obtaining a global optimal solution, the specific steps are as follows:

[0039] S321, the process of solving the objective function of the green aluminum curtain wall is regarded as a search space, and a root system is randomly generated, each of which represents the thickness of the green aluminum curtain wall and the installation process of the green aluminum curtain wall; in the fiber root growth stage, the current number of iterations is set to d, and the position of the i-th fiber root in the d-th iteration is , the dth iteration The fiber root position is , the optimal fiber root position for the dth iteration is , represents a random number between (-0.5, 1.5), and updates the position of the ith fiber root in the dth iteration to obtain ; In the lateral root growth stage, calculate the average value of the d-th iteration fiber root position, denoted as , Represents a random number between the interval (-1, 1), the dth iteration The fiber root position is , then the position of the i-th fiber root in the d+1th iteration is ; In the main root growth stage, set the growth parameters to , the search space is , at this time, the position of the i-th fiber root in the d+1th iteration is updated to obtain ;

[0040] S322. In the root diffusion stage, randomly select fiber root positions, record them as the first fiber root position and the second fiber root position respectively, calculate the average value of the ith fiber root position, the first fiber root position and the second fiber root position in the d-th iteration, and then update the ith fiber root position in the d+1-th iteration to obtain the final position of the ith fiber root in the d+1-th iteration, calculate the fitness function value, eliminate the roots, and randomly add roots to form a new root system for iterating again; set the maximum number of iterations, stop iterating when the current number of iterations reaches the maximum number of iterations, and obtain the global optimal solution.

[0041] This invention constructs a green aluminum curtain wall objective function based on the minimum construction cost and the shortest installation time, which facilitates the realization of the minimum green aluminum curtain wall installation cost and time, and then uses the plant rhizome growth optimization algorithm to solve it. By imitating the process of plant roots obtaining nutrients, the algorithm has good global search capabilities and fast convergence performance. Compared with other optimization algorithms, it has better optimization accuracy and effectively achieves the optimization purpose.

[0042] Preferably, S4 comprises the following steps:

[0043] S41. Obtain the optimal thickness of the green aluminum curtain wall and the optimal installation process of the green aluminum curtain wall according to the global optimal solution, place the green aluminum curtain wall in the green aluminum curtain wall grid, install it using the optimal thickness of the green aluminum curtain wall, and install it according to the optimal installation process of the green aluminum curtain wall to achieve the minimum construction cost and the shortest installation time, generate a green aluminum curtain wall design plan, and complete the green aluminum curtain wall design for the target building.

[0044] The present invention also discloses a system of a green aluminum plate curtain wall design method based on digital modeling, which specifically includes: a model initial establishment module, a curtain wall surface extraction module, a curtain wall installation optimization module and a design scheme generation module;

[0045] The model initialization module is used to obtain three-dimensional model point cloud data to perform three-dimensional modeling on the target building;

[0046] The curtain wall surface extraction module is used to extract the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface;

[0047] The curtain wall installation optimization module is used to construct the green aluminum curtain wall objective function and use the optimization algorithm to solve and obtain the global optimal solution;

[0048] The design scheme generating module is used to generate a green aluminum plate curtain wall design scheme according to a global optimal solution.

[0049] The present invention has the following beneficial effects:

[0050] 1. The invention collects point cloud images of the surface of the target building, obtains three-dimensional model point cloud data after matching point screening, ensures that the point cloud data is matched quickly and effectively, improves the precision and accuracy of the model after screening, reduces the void rate of the model, and constructs an initial three-dimensional model of the target building for subsequent processing.

[0051] 2. The invention establishes an accurate model by preliminarily identifying the outer surface of the initial target building's three-dimensional model, eliminating errors caused by erroneous measurements and calculations, and then simplifies the discrete point cloud data and extracts the outer surface of the model. Considering the first endpoint greatly reduces the time consumption of calculation, and extracting the outer surface of the target building for curtain wall design is convenient, fast and highly applicable.

[0052] 3. The invention constructs a green aluminum curtain wall objective function based on the minimum construction cost and the shortest installation time, which facilitates the realization of the minimum green aluminum curtain wall installation cost and time, and then uses the plant rhizome growth optimization algorithm to solve it. By imitating the process of plant roots obtaining nutrients, the algorithm has good global search capabilities and fast convergence performance. Compared with other optimization algorithms, it has better optimization accuracy and effectively achieves the optimization purpose.

[0053] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of the process of designing a green aluminum curtain wall by a green aluminum curtain wall design system based on digital modeling provided by the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0058] Example 1

[0059] Please refer to Figure 1 , a green aluminum curtain wall design method based on digital modeling, comprising the following steps:

[0060] S1, collecting the surface point cloud image of the target building, obtaining the target building surface point cloud image set, screening the matching points in the target building surface point cloud image set, obtaining the 3D model point cloud data set, and then performing 3D modeling on the target building to obtain the initial target building 3D model;

[0061] The S1 comprises the following steps:

[0062] S11, collect a target building video, and combine it with the depth image of the target building to obtain several surface point cloud images of the target building to form a target building surface point cloud image set, select any target building surface point cloud image from the target building surface point cloud image set, record it as a point cloud image to be processed, select any matching point from the point cloud image to be processed, record it as a matching point b, set a neighborhood search radius, take the matching point b as the center, search for matching points within the neighborhood search radius, and obtain a neighborhood matching point set; traverse the neighborhood matching point set, and calculate the weights of the matching points in the neighborhood matching point set, and the calculation formula is as follows:

[0063] ;

[0064] in, represents the jth matching point in the neighborhood matching point set, Represents the weight of the jth matching point;

[0065] According to the weight of the matching point, the covariance matrix and the eigenvalues ​​of the covariance matrix of the matching point in the neighborhood matching point set are calculated, the eigenvalues ​​of the covariance matrix are arranged in descending order, and the top three eigenvalues ​​are selected and recorded as the first eigenvalues. , the second eigenvalue and the third eigenvalue , set a first relationship threshold and a second relationship threshold; establish the relationship between the matching points in the target building surface point cloud image set, when is less than or equal to the first relationship threshold and When the value is less than or equal to the second relationship threshold, the corresponding matching point is recorded as the three-dimensional model point cloud data; until all the target building surface point cloud images in the target building surface point cloud image set are screened, and the three-dimensional model point cloud data set is obtained;

[0066] S12, deleting and matching duplicate point cloud data of the three-dimensional model point cloud data in the three-dimensional model point cloud data set, realizing three-dimensional modeling of the target building, and obtaining an initial three-dimensional model of the target building, the specific steps are as follows:

[0067] S121, arbitrarily selecting three-dimensional model point cloud data corresponding to different target building surface point cloud images from the three-dimensional model point cloud data set, and recording them as three-dimensional model point cloud data subsets respectively. and 3D model point cloud data subset , connect the 3D model point cloud data subset and 3D model point cloud data subset 3D model point cloud data is obtained, and the distance of the 3D model point cloud data is calculated, a distance threshold is set, and the 3D model point cloud data corresponding to the distance of the 3D model point cloud data is greater than the distance threshold is deleted; the repeated 3D model point cloud data of the target building surface point cloud image are deleted in sequence to obtain a processed 3D model point cloud data set;

[0068] S122, establishing a spatial coordinate system for the processed 3D model point cloud data set, taking the 3D model point cloud data in the processed 3D model point cloud data set as the origin, obtaining a plurality of 3D model point cloud data coordinate systems, respectively calculating the translation relationship and rotation relationship of the 3D model point cloud data in the 3D model point cloud data coordinate systems, obtaining a point cloud data translation matrix and a point cloud data rotation matrix, converting the 3D model point cloud data in the 3D model point cloud data coordinate system to the 3D model coordinate system, obtaining converted 3D model point cloud data, and the calculation formula is as follows:

[0069] ;

[0070] in, Represents the converted 3D model point cloud data, Represents the 3D model point cloud data in the processed 3D model point cloud data set, Represents the point cloud data rotation matrix, Represents the point cloud data translation matrix;

[0071] Converting all the three-dimensional model point cloud data in the processed three-dimensional model point cloud data set, and then performing three-dimensional modeling on the target building according to the converted three-dimensional model point cloud data to establish an initial three-dimensional model of the target building;

[0072] S2, preliminarily identifying the outer surface of the initial target building three-dimensional model, simplifying the discrete point cloud data, obtaining the final target building three-dimensional model, and then extracting the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface;

[0073] The S2 comprises the following steps:

[0074] S21, preliminarily identifying the outer surface of the initial target building three-dimensional model, and then simplifying the discrete point cloud data to obtain the final target building three-dimensional model, the specific steps are as follows:

[0075] S211, set the rated number of converted three-dimensional model point cloud data included in the outer surface of the initial target building three-dimensional model, recorded as the outer surface point cloud data number threshold; select any three converted three-dimensional model point cloud data in the initial target building three-dimensional model, establish the initial three-dimensional model outer surface, calculate the distance from other converted three-dimensional model point cloud data to the initial three-dimensional model outer surface, and set the distance threshold, add the converted three-dimensional model point cloud data corresponding to the distance less than the distance threshold to the outer surface three-dimensional model point cloud data set; calculate the normal vector of the outer surface three-dimensional model point cloud data set and the normal vector of the initial three-dimensional model outer surface, set the normal vector Vector threshold, when the normal vector of the outer surface 3D model point cloud data set is greater than the normal vector of the initial 3D model outer surface, the corresponding converted 3D model point cloud data is removed from the outer surface 3D model point cloud data set to obtain a final 3D model point cloud data set; compare the final 3D model point cloud data set with the outer surface point cloud data number threshold, when the number of converted 3D model point cloud data in the final 3D model point cloud data set is less than the outer surface point cloud data number threshold, delete the final 3D model point cloud data set, re-perform outer surface recognition, complete preliminary recognition of the outer surface of the initial target building 3D model, and obtain the initial outer surface of the target building;

[0076] S212, arbitrarily select the point cloud data on the initial outer surface of the target building, record it as suspicious discrete point cloud data, set the neighborhood distance, take the suspicious discrete point cloud data as the center, calculate the density of the suspicious discrete point cloud data within the neighborhood distance range, and obtain the neighborhood density; set the density threshold and the distance threshold, when the neighborhood density is greater than the density threshold, calculate the mean of all the point cloud data within the neighborhood distance range, retain the point cloud data and the point cloud data corresponding to the mean difference less than the distance threshold; when the neighborhood density is less than or equal to the density threshold, delete all the point cloud data within the neighborhood distance range, complete the simplification of the discrete point cloud data, obtain the final outer surface of the target building, and construct the final target building three-dimensional model according to the final outer surface of the target building;

[0077] S22, select any outer surface on the final target building three-dimensional model, record it as the outer surface to be extracted, select three consecutive point cloud data on the contour of the outer surface to be extracted , and , forming the external surface point cloud data set , fit the outer surface point cloud data set to obtain a point cloud data straight line, calculate the distance from three point cloud data to the point cloud data straight line, and obtain the point cloud data distance threshold; extend the point cloud data straight line, encounter new point cloud data during the extension process, compare the distance from the new point cloud data to the point cloud data straight line and the point cloud data distance threshold, when the distance from the new point cloud data to the point cloud data straight line is less than the point cloud data distance threshold, add the new point cloud data to the outer surface point cloud data set, otherwise reselect three consecutive point cloud data; fit the outer surface point cloud data set again to obtain a new point cloud data straight line, set a straight line length threshold, and stop extending until the new point cloud data straight line is greater than the straight line length threshold, and obtain the final outer surface point cloud data set, retain the point cloud data at both ends of the final outer surface point cloud data set, connect the point cloud data at both ends to form the contour of the outer surface to be extracted, extract the contours of all outer surfaces on the final target building three-dimensional model, obtain the outer surface of the target building, and record the outer surface of the target building as the green aluminum plate curtain wall surface;

[0078] S3, gridding the surface of the green aluminum curtain wall to generate a green aluminum curtain wall grid, and constructing a green aluminum curtain wall objective function based on minimum construction cost and shortest installation time, and using a plant rhizome growth optimization algorithm to solve the green aluminum curtain wall objective function to obtain a global optimal solution;

[0079] The S3 comprises the following steps:

[0080] S31, establish a two-dimensional coordinate system on the surface of the green aluminum curtain wall, set the rated length and rated width, and grid the surface of the green aluminum curtain wall according to the rated length and rated width to obtain a green aluminum curtain wall grid; obtain the thickness of the green aluminum curtain wall, and calculate the material cost of the green aluminum curtain wall according to the rated length, rated width and thickness; set a set of green aluminum curtain wall installation procedures ,in Indicates the mth green aluminum curtain wall installation process. Construction is performed in the green aluminum curtain wall grid according to the green aluminum curtain wall installation process set. The labor cost of the green aluminum curtain wall installation process is , based on the minimum construction cost, the green aluminum curtain wall installation cost function is established as follows:

[0081] ;

[0082] in, represents the installation cost function of the green aluminum curtain wall, and c represents the material cost of the green aluminum curtain wall;

[0083] Set The construction time of the green aluminum curtain wall installation process is The shortest construction time and the longest construction time are and , the green aluminum curtain wall installation time function is constructed based on the shortest installation time as follows:

[0084] , ;

[0085] in, Represents the installation time function of the green aluminum curtain wall;

[0086] Combining the green aluminum curtain wall installation cost function and the green aluminum curtain wall installation time function, a green aluminum curtain wall objective function is obtained;

[0087] S32, taking the green aluminum curtain wall objective function as the fitness function, using the plant rhizome growth optimization algorithm to solve the green aluminum curtain wall objective function, and obtaining a global optimal solution, the specific steps are as follows:

[0088] S321, the process of solving the objective function of the green aluminum curtain wall is regarded as a search space, and a root system is randomly generated, each of which represents the thickness of the green aluminum curtain wall and the installation process of the green aluminum curtain wall; in the fiber root growth stage, the current number of iterations is set to d, and the position of the i-th fiber root in the d-th iteration is , the dth iteration The fiber root position is , the optimal fiber root position for the dth iteration is , represents a random number between (-0.5, 1.5), and updates the position of the ith fiber root in the dth iteration to obtain ; In the lateral root growth stage, calculate the average value of the d-th iteration fiber root position, denoted as , Represents a random number between the interval (-1, 1), the dth iteration The fiber root position is , then the position of the i-th fiber root in the d+1th iteration is ; In the main root growth stage, set the growth parameters to , the search space is , at this time, the position of the i-th fiber root in the d+1th iteration is updated to obtain ;

[0089] S322, in the root system diffusion stage, randomly select fiber root positions, record them as the first fiber root position and the second fiber root position, calculate the average value of the ith fiber root position, the first fiber root position and the second fiber root position in the dth iteration, and then update the ith fiber root position in the d+1th iteration to obtain the final position of the ith fiber root in the d+1th iteration, calculate the fitness function value, eliminate the root system, and randomly add the root system to form a new root system for iteration again; set the maximum number of iterations, stop iterating when the current number of iterations reaches the maximum number of iterations, and obtain the global optimal solution;

[0090] S4, generating a green aluminum curtain wall design scheme according to the global optimal solution, and completing the green aluminum curtain wall design for the target building;

[0091] The S4 comprises the following steps:

[0092] S41. Obtain the optimal thickness of the green aluminum curtain wall and the optimal installation process of the green aluminum curtain wall according to the global optimal solution, place the green aluminum curtain wall in the green aluminum curtain wall grid, install it using the optimal thickness of the green aluminum curtain wall, and install it according to the optimal installation process of the green aluminum curtain wall to achieve the minimum construction cost and the shortest installation time, generate a green aluminum curtain wall design plan, and complete the green aluminum curtain wall design for the target building.

[0093] Example 2

[0094] The present invention also discloses a system of a green aluminum plate curtain wall design method based on digital modeling, which specifically includes: a model initial establishment module, a curtain wall surface extraction module, a curtain wall installation optimization module and a design scheme generation module;

[0095] The model initialization module is used to obtain three-dimensional model point cloud data to perform three-dimensional modeling on the target building;

[0096] The curtain wall surface extraction module is used to extract the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface;

[0097] The curtain wall installation optimization module is used to construct the green aluminum curtain wall objective function and use the optimization algorithm to solve and obtain the global optimal solution;

[0098] The design scheme generating module is used to generate a green aluminum plate curtain wall design scheme according to a global optimal solution.

[0099] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0100] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A green aluminum curtain wall design method based on digital modeling, characterized in that: The steps include: S1, collecting the surface point cloud image of the target building, obtaining the target building surface point cloud image set, screening the matching points in the target building surface point cloud image set, obtaining the 3D model point cloud data set, and then performing 3D modeling on the target building to obtain the initial target building 3D model; S2, preliminarily identifying the outer surface of the initial target building three-dimensional model, simplifying the discrete point cloud data, obtaining the final target building three-dimensional model, and then extracting the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface; S3, gridding the surface of the green aluminum curtain wall to generate a green aluminum curtain wall grid, and constructing a green aluminum curtain wall objective function based on minimum construction cost and shortest installation time, solving the green aluminum curtain wall objective function, and obtaining a global optimal solution; S4. Generate a green aluminum curtain wall design solution according to the global optimal solution, and complete the green aluminum curtain wall design for the target building.

2. A green aluminum curtain wall design method based on digital modeling according to claim 1, characterized in that: The S1 comprises the following steps: S11, collecting surface point cloud images of the target building to obtain a target building surface point cloud image set, selecting any target building surface point cloud image, recording it as a point cloud image to be processed, screening matching points in the point cloud image to be processed, until all target building surface point cloud images in the target building surface point cloud image set are screened, and obtaining a three-dimensional model point cloud data set; S12, deleting duplicate point cloud data and matching the three-dimensional model point cloud data in the three-dimensional model point cloud data set to achieve three-dimensional modeling of the target building and obtain an initial three-dimensional model of the target building.

3. The green aluminum curtain wall design method based on digital modeling according to claim 2 is characterized in that: The S12 comprises the following steps: S121, calculating the distance of the three-dimensional model point cloud data in the three-dimensional model point cloud data set, setting a distance threshold, and obtaining a processed three-dimensional model point cloud data set by comparing the distance threshold and the distance of the three-dimensional model point cloud data; S122, establishing a spatial coordinate system for the processed three-dimensional model point cloud data set, performing spatial coordinate transformation to obtain transformed three-dimensional model point cloud data, and performing three-dimensional modeling on the target building to establish an initial three-dimensional model of the target building.

4. The green aluminum curtain wall design method based on digital modeling according to claim 3 is characterized in that: The S2 comprises the following steps: S21, preliminarily identifying the outer surface of the initial target building three-dimensional model, and then simplifying the discrete point cloud data to obtain the final target building three-dimensional model; S22. Select any outer surface on the three-dimensional model of the final target building and record it as the outer surface to be extracted. Select three consecutive point cloud data on the contour of the outer surface to be extracted and fit them to obtain a point cloud data straight line, calculate the point cloud data distance threshold, find the contour of the outer surface by comparing the distance from the new point cloud data to the point cloud data straight line and the point cloud data distance threshold, and obtain the outer surface of the target building. The outer surface of the target building is recorded as the green aluminum plate curtain wall surface.

5. The green aluminum curtain wall design method based on digital modeling according to claim 4 is characterized in that: The S21 comprises the following steps: S211, according to the outer surface of the initial target building three-dimensional model, establish the initial three-dimensional model outer surface, and obtain the outer surface three-dimensional model point cloud data set; then calculate the normal vector of the outer surface three-dimensional model point cloud data set and the normal vector of the initial three-dimensional model outer surface, set the normal vector threshold, and obtain the final three-dimensional model point cloud data set by comparing the normal vector threshold and the normal vector, thereby completing the preliminary recognition of the outer surface of the initial target building three-dimensional model and generating the initial outer surface of the target building; S212. Arbitrarily select point cloud data on the initial outer surface of the target building, record it as suspicious discrete point cloud data, set a neighborhood distance, take the suspicious discrete point cloud data as the center, calculate the density of the suspicious discrete point cloud data within the neighborhood distance range, and obtain the neighborhood density. Simplify the discrete point cloud data according to the neighborhood density to obtain the final outer surface of the target building, and construct a final three-dimensional model of the target building based on the final outer surface of the target building.

6. The green aluminum curtain wall design method based on digital modeling according to claim 5 is characterized in that: The S3 comprises the following steps: S31, gridding the surface of the green aluminum curtain wall to obtain a green aluminum curtain wall grid; obtaining the thickness of the green aluminum curtain wall, and calculating the material cost of the green aluminum curtain wall; setting a set of green aluminum curtain wall installation procedures, establishing a green aluminum curtain wall installation cost function based on the minimum construction cost, and then constructing a green aluminum curtain wall installation time function based on the shortest installation time, and combining them to obtain a green aluminum curtain wall objective function; S32, taking the green aluminum plate curtain wall objective function as the fitness function, using the plant rhizome growth optimization algorithm to solve the green aluminum plate curtain wall objective function, and obtaining a global optimal solution.

7. The green aluminum curtain wall design method based on digital modeling according to claim 6 is characterized in that: The S32 comprises the following steps: S321, the process of solving the objective function of the green aluminum curtain wall is regarded as a search space, and a root system is randomly generated, each of which represents the thickness of the green aluminum curtain wall and the installation process of the green aluminum curtain wall; in the fiber root growth stage, the current number of iterations is set to d, and the position of the i-th fiber root in the d-th iteration is , the dth iteration The fiber root position is , the optimal fiber root position for the dth iteration is , represents a random number between (-0.5, 1.5), and updates the position of the ith fiber root in the dth iteration to obtain ; In the lateral root growth stage, calculate the average value of the d-th iteration fiber root position, denoted as , Represents a random number between the interval (-1, 1), the dth iteration The fiber root position is , then the position of the i-th fiber root in the d+1th iteration is ; In the main root growth stage, set the growth parameters to , the search space is , at this time, the position of the i-th fiber root in the d+1th iteration is updated to obtain ; S322, in the root system diffusion stage, randomly select fiber root positions, record them as the first fiber root position and the second fiber root position, calculate the average value of the ith fiber root position, the first fiber root position and the second fiber root position in the d-th iteration, then update the ith fiber root position in the d+1-th iteration, obtain the final position of the ith fiber root in the d+1-th iteration, calculate the fitness function value, eliminate the root system, and randomly add the root system to form a new root system for iteration again; Set the maximum number of iterations. When the current number of iterations reaches the maximum number of iterations, stop the iteration and get the global optimal solution.

8. The green aluminum curtain wall design method based on digital modeling according to claim 7 is characterized in that: The S4 comprises the following steps: S41. Obtain the optimal thickness of the green aluminum curtain wall and the optimal installation process of the green aluminum curtain wall according to the global optimal solution, place the green aluminum curtain wall in the green aluminum curtain wall grid, install it using the optimal thickness of the green aluminum curtain wall, and install it according to the optimal installation process of the green aluminum curtain wall to achieve the minimum construction cost and the shortest installation time, generate a green aluminum curtain wall design plan, and complete the green aluminum curtain wall design for the target building.

9. A system for implementing the green aluminum curtain wall design method based on digital modeling as described in any one of claims 1 to 8, characterized in that: Specifically include: Model initialization module, curtain wall surface extraction module, curtain wall installation optimization module and design solution generation module; The model initialization module is used to obtain three-dimensional model point cloud data to perform three-dimensional modeling on the target building; The curtain wall surface extraction module is used to extract the outer surface of the final target building three-dimensional model to obtain the green aluminum curtain wall surface; The curtain wall installation optimization module is used to construct the green aluminum curtain wall objective function and use the optimization algorithm to solve and obtain the global optimal solution; The design scheme generating module is used to generate a green aluminum plate curtain wall design scheme according to a global optimal solution.

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