Office building leasing intelligent management method and system based on BIM technology

Abstract

The application relates to the technical field of data processing, and discloses an office building leasing intelligent management method and system based on BIM technology. The method collects image data and position data of an office building needing leasing in real time, carries out three-dimensional visual modeling on the collected image data of the office building needing leasing, carries out data processing on the obtained three-dimensional model data of the office building needing leasing through a data processing mode, and determines the area of the office building needing leasing; meanwhile, through collection of house type data of the office building needing leasing, and based on the determined area of the office building needing leasing, the office building needing leasing is clustered through a clustering algorithm, and finally, the obtained clustering result is managed and analyzed according to the collected position data of the office building needing leasing, so that the intelligence of office building leasing intelligent management is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent management method and system for office building leasing based on BIM technology. Background Art

[0002] As market understanding deepens, the basic requirement of office leasing service systems is to provide information; however, current office leasing services fail to provide a unified system and lack data mining support.

[0003] The existing Chinese patent application CN113536440A conducts a comprehensive data analysis on the frequency of abnormal data occurring in a unit time and the fluctuation of the abnormal data frequency. It makes a preliminary judgment on the surrounding environment and the current BIM environmental situation based on the high frequency of occurrence in a unit time. At the same time, it judges whether the detection is accurate based on the fluctuation of the abnormal data frequency. Finally, it conducts a comprehensive analysis of the data and the environmental interference factors to obtain the final result forecast. However, due to the lack of data mining support and the lack of a unified service system, the data management capabilities are insufficient. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an intelligent management method and system for office building leasing based on BIM technology, which has the advantages of real-time, accuracy, and efficiency, and solves the problem that office building leasing services fail to provide a unified system and lack data mining support.

[0006] (2) Technical solution

[0007] To solve the technical problems of the above-mentioned office building leasing service failing to provide a unified system and lacking data mining support, the present invention provides the following technical solutions:

[0008] This embodiment discloses an intelligent management method for office building leasing based on BIM technology, which specifically includes the following steps:

[0009] S1. Collect image data and location data of the office building to be leased in real time, and perform three-dimensional visualization modeling on the collected image data of the office building to be leased to obtain three-dimensional model data of the office building to be leased;

[0010] S2. Processing the obtained three-dimensional model data of the office building to be leased by a data processing method to determine the area of ​​the office building to be leased;

[0011] S3. Collecting apartment type data of office buildings to be leased, and clustering the office buildings to be leased collected in real time based on the determined area of ​​the office buildings to be leased using a clustering algorithm to obtain clustering results. The clustering results include: leased office building type, cluster center, and clustered leased office building data;

[0012] S4. Conduct management analysis on the clustering results based on the collected location data of the leased office buildings.

[0013] The present invention collects image data and location data of office buildings to be leased in real time, performs three-dimensional visual modeling on the collected image data of office buildings to be leased, and processes the obtained three-dimensional model data of the office buildings to be leased through a data processing method to determine the area of ​​the office buildings to be leased; at the same time, by collecting apartment data of office buildings to be leased, and based on the determined area of ​​the office buildings to be leased, clustering the office buildings to be leased collected in real time is performed through a clustering algorithm, and finally management and analysis are performed on the obtained clustering results according to the collected location data of the leased office buildings, thereby improving the accuracy of intelligent management of office building leasing.

[0014] Preferably, the real-time collection of image data and location data of the office building to be leased and the three-dimensional visualization modeling of the collected image data of the office building to be leased include the following steps:

[0015] S11. Using a camera to capture multiple images of the leased office building from different angles to obtain point cloud data of the leased office building.

[0016] S12. Matching the point cloud data in the leased office building image to a unified coordinate system through a point cloud registration method to obtain registered point cloud data;

[0017] S13. Convert the registered point cloud data into a three-dimensional model using a three-dimensional reconstruction algorithm.

[0018] Preferably, the step of matching the point cloud data in the leased office building image to a unified coordinate system by point cloud registration to obtain the registered point cloud data comprises the following steps:

[0019] Assume that the point cloud dataset of the rental office building is P(u1,u2,...,u n );

[0020] Calculate the mean and covariance of a point cloud dataset of rental office buildings;

[0021] The formulas for calculating mean and covariance are as follows:

[0022]

[0023]

[0024] in, represents the mean of the point cloud dataset of the rental office building, cov represents the covariance of the point cloud dataset of the rental office building, u i Represents the i-th point cloud data in the point cloud dataset, n represents the number of point cloud datasets, and T represents the transpose calculation;

[0025] Based on the calculated mean and covariance of the point cloud dataset of the rental office building, the covariance of the calculated point cloud dataset of the rental office building is set as the coordinate axis of the coordinate system, and the calculated mean of the point cloud dataset of the rental office building is set as the origin of the coordinate system;

[0026] Based on the set coordinate system and origin, the point cloud data in the point cloud dataset of the leased office building is recalculated to obtain the registered point cloud data.

[0027] Preferably, converting the registered point cloud data into a three-dimensional model using a three-dimensional reconstruction algorithm comprises the following steps:

[0028] The registered point cloud data is converted into a 3D model based on the set coordinate system and origin. The relationship between the two sets of coordinates is as follows:

[0029] Set Q = (x Q ,y Q ,z Q ) is the reconstructed three-dimensional model, q=(x q ,y q ,w q ) is the point cloud data after registration;

[0030] Among them, x Q ,y Q ,z Q Respectively represent the horizontal, vertical and vertical coordinates of the reconstructed three-dimensional model; x q ,y q Respectively represent the horizontal and vertical coordinates in the registered point cloud data, w q Indicates the wheelbase parameter of the camera;

[0031]

[0032] Among them, f x Indicates the x-axis camera wheelbase, f y Indicates the y-axis camera wheelbase, e x Represents the horizontal coordinate of the point cloud data on the x-axis, e y Represents the vertical coordinate of the point cloud data on the y-axis; Represents the rotation vector matrix, r 11 Represents the rotation vector of the first row and first column in the rotation vector matrix, Represents the translation vector matrix, t1 represents the translation vector of the first row;

[0033] The converted 3D model is set as the 3D model data of the office building to be rented.

[0034] The present invention obtains point cloud data of the rental office building by using a camera to shoot multiple images of the rental office building at different angles, and matches the point cloud data in the rental office building images to a unified coordinate system through point cloud registration. After the coordinate system is unified, the three-dimensional model is reconstructed through a three-dimensional reconstruction algorithm, thereby improving the accuracy of three-dimensional modeling.

[0035] Preferably, the step of processing the obtained three-dimensional model data of the office building to be leased by a data processing method to determine the area of ​​the office building to be leased comprises the following steps:

[0036] In a real-world scenario where an office building needs to be rented, select any two points A(x1,y1) and B(x2,y2), and measure the distance between the two points to obtain the actual distance DL between the two points.

[0037] Select four points from the obtained 3D model data of the office building to be leased to form a rectangle. Set the coordinates of the four points to be A(x1, y1), B(x2, y2), C(x3, y3), and E(x4, y4). According to the distance formula between two points, the model distance between AB can be calculated as DL1, and the model distance between CE can be calculated as DL2.

[0038] The formula for the distance between two points is as follows:

[0039]

[0040] Calculate the ratio between the actual distance and the model distance

[0041] The actual area S=DL1×DL2×P of the three-dimensional model data of the office building to be leased is required.

[0042] The present invention calculates the actual distance by selecting the location information in the real scene of the office building, and calculates the distance in the model by selecting the same point in the three-dimensional model. At the same time, the actual area of ​​the office building is calculated by comparing the two sets of distances, thereby improving the efficiency of calculating the area of ​​the leased office building.

[0043] Preferably, the collecting of apartment type data of office buildings to be rented and clustering the office buildings to be rented collected in real time by a clustering algorithm based on the determined area of ​​the office buildings to be rented comprises the following steps:

[0044] S31, initialize the collected apartment type data and area data of office buildings that need to be rented, set the collected apartment type data and area data of office buildings that need to be rented as rental office building data, and construct a rental office building set x = {x1, x2, ..., x i ,...x n};

[0045] Among them, x i represents the i-th group of rental office building data, x n Represents the nth group of rental office building data;

[0046] Select k groups of rental office building data in the rental office building set as the initial cluster center a={a1,a2,...,a k}, set each cluster center to represent the central data of a rental office building type;

[0047] S32, based on each group of samples x in the rental office building set i , according to the distance formula, Calculate the data x for each group of rental office buildings i The distance to k cluster centers is calculated, and the samples are classified into the rental office building type corresponding to the cluster center with the smallest distance. One group is added at a time until all samples are clustered. The result of this classification is then used as the input of step S13, and step S13 is executed again.

[0048] in, Represents each group of rental office building data x i To cluster center a j The distance, a j represents the jth cluster center, and the value range of j is set to [1, k];

[0049] S33. After all the rental office building data are classified, for each rental office building type c={c1, c2, ..., c α}, recalculate the cluster center of each rental office building type Among them, a c represents the cluster center of rental office building type c;

[0050] Among them, c α represents the αth group of rental office building data in rental office building type c, c r represents the rth group of rental office building data in rental office building type c, Represents the rental office building data c r The distance to the center of the current product category cluster;

[0051] Based on the recalculation of the cluster center and distance formula for each type of leased office building, the clustered leased office building data in each type of leased office building is recalculated;

[0052] S34. Repeat steps S12-S13 until the cluster center and the leased office building data in the leased office building type no longer change, and then output the leased office building type, cluster center, and clustered leased office building data.

[0053] The present invention initializes the collected apartment type data and area data of office buildings that need to be rented and constructs a data set. At the same time, the apartment type data and area data of the office buildings in the data set are clustered by selecting data in the data set and calculating the distance, thereby ensuring the reliability of the classification of the apartment type data and area data of the office buildings.

[0054] Preferably, the step of managing the clustering results obtained based on the collected location data of the leased office buildings comprises the following steps:

[0055] Set H1 to represent the collected location data of the office building to be leased, set H2 to represent the calculated apartment type data of the office building to be leased, and set H3 to represent the calculated area data of the office building to be leased;

[0056] The location data of the office building to be leased, the unit type data of the office building to be leased, and the area data of the office building to be leased are correlated through the grey correlation algorithm;

[0057] Recalculate the attributes of each office building to be leased based on the attribute weights calculated using the grey correlation algorithm, and sort them in descending order based on the calculated results;

[0058] Set a minimum score threshold for each attribute of each office building that needs to be rented, and remove office buildings that do not meet the conditions from the list based on the set minimum score threshold.

[0059] Preferably, the associating the location data of the office building to be leased, the apartment type data of the office building to be leased, and the area data of the office building to be leased by using a grey correlation algorithm comprises the following steps:

[0060] The grey relational algorithm formula is as follows:

[0061]

[0062] r=1,2,3...m; β∈{H1,H2,H3};

[0063] Among them, R β represents the calculated weight of the βth attribute, H1(r) represents the score of user r on the location data of the rental office building, and Hβ (r) represents the importance that user r places on the location data of the leased office building; user r’s score for the βth attribute; ρ represents the grey correlation parameter; min β Indicates the minimum score of the βth attribute, max β Indicates the maximum score of the βth attribute, min r Indicates the minimum score of user r, max r represents the maximum rating of user r.

[0064] The present invention sets the collected office building location data and the clustered office building unit type data and area data, and associates the set office building data by using a grey correlation algorithm, calculates scores, and manages the office building data according to the calculated scores.

[0065] This embodiment also discloses an intelligent management system for office building leasing based on BIM technology, including: a data acquisition module, a real-time modeling module, a model processing module, a data classification module and an office building management module;

[0066] The data acquisition module is used to collect image data, location data and apartment type data input by the user of the office building to be rented in real time;

[0067] The real-time modeling module is used to build a three-dimensional model based on the real-time collection of image data of the office building to be leased;

[0068] The model processing module is used to calculate the area of ​​each area of ​​the office building based on the three-dimensional model established by the facts;

[0069] The data classification module is used to classify office buildings according to the calculated areas of each area of ​​the office building and the apartment type data input by the user;

[0070] The office building management module is used to calculate the scores of various attributes of each classified office building and manage the collected office building data according to the calculated scores.

[0071] (3) Beneficial effects

[0072] Compared with the existing technology, the present invention provides an intelligent management method and system for office building leasing based on BIM technology, which has the following beneficial effects:

[0073] 1. The invention collects image data and location data of office buildings to be leased in real time, performs three-dimensional visual modeling on the collected image data of office buildings to be leased, and processes the obtained three-dimensional model data of office buildings to be leased through a data processing method to determine the area of ​​the office buildings to be leased; at the same time, by collecting apartment data of office buildings to be leased, and based on the determined area of ​​office buildings to be leased, clusters the office buildings to be leased collected in real time through a clustering algorithm, and finally manages and analyzes the obtained clustering results based on the collected location data of leased office buildings, thereby improving the intelligence of intelligent management of office building leasing.

[0074] 2. This invention obtains point cloud data of the leased office building by using a camera to take multiple images of the leased office building from different angles, and matches the point cloud data in the leased office building images to a unified coordinate system through point cloud registration. After the coordinate system is unified, the three-dimensional model is reconstructed through a three-dimensional reconstruction algorithm, thereby improving the accuracy of three-dimensional modeling.

[0075] 3. This invention calculates the actual distance by selecting the location information in the real scene of the office building, and selects the same point in the three-dimensional model to calculate the distance in the model. At the same time, by comparing the two sets of distances, the actual area of ​​the office building is calculated, thereby improving the efficiency of calculating the area of ​​the leased office building.

[0076] 4. The invention initializes the collected apartment type data and area data of office buildings that need to be rented and constructs a data set. At the same time, the apartment type data and area data of the office buildings in the data set are clustered by selecting data in the data set and calculating the distance, thereby ensuring the reliability of the classification of the apartment type data and area data of the office buildings.

[0077] 5. The invention sets the collected office building location data and the clustered office building unit type data and area data, and associates the set office building data by using a grey correlation algorithm, calculates scores, and manages the office building data based on the calculated scores. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a schematic diagram of the intelligent management process structure of office building leasing according to the present invention. DETAILED DESCRIPTION

[0079] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0080] Example 1

[0081] See also Figure 1 This embodiment discloses an intelligent management method for office building leasing based on BIM technology, which specifically includes the following steps:

[0082] S1. Collect image data and location data of the office building to be leased in real time, and perform three-dimensional visualization modeling on the collected image data of the office building to be leased to obtain three-dimensional model data of the office building to be leased;

[0083] The following steps are involved in collecting image data and location data of an office building to be leased in real time and performing 3D visualization modeling on the collected image data of the office building to be leased:

[0084] S11. Using a camera to capture multiple images of the leased office building from different angles to obtain point cloud data of the leased office building.

[0085] S12. Matching the point cloud data in the leased office building image to a unified coordinate system through a point cloud registration method to obtain registered point cloud data;

[0086] Using point cloud registration, the point cloud data in the rental office building image is matched to a unified coordinate system. Obtaining the registered point cloud data includes the following steps:

[0087] Assume that the point cloud dataset of the rental office building is P(u1,u2,...,u n );

[0088] Calculate the mean and covariance of a point cloud dataset of rental office buildings;

[0089] The formulas for calculating mean and covariance are as follows:

[0090]

[0091] in, represents the mean of the point cloud dataset of the rental office building, cov represents the covariance of the point cloud dataset of the rental office building, u i Represents the i-th point cloud data in the point cloud dataset, n represents the number of point cloud datasets, and T represents the transpose calculation;

[0092] Based on the calculated mean and covariance of the point cloud dataset of the rental office building, the covariance of the calculated point cloud dataset of the rental office building is set as the coordinate axis of the coordinate system, and the calculated mean of the point cloud dataset of the rental office building is set as the origin of the coordinate system;

[0093] Furthermore, based on the set coordinate system and origin, the point cloud data in the point cloud dataset of the leased office building is recalculated to obtain the registered point cloud data;

[0094] S13, converting the registered point cloud data into a three-dimensional model by using a three-dimensional reconstruction algorithm;

[0095] The registered point cloud data is converted into a three-dimensional model based on the set coordinate system and origin, and the relationship between the two sets of coordinates is as follows:

[0096] Let Q = (x Q ,y Q ,z Q ) be the reconstructed three-dimensional model, and q = (x q ,y q ,w q ) be the registered point cloud data.

[0097] Where x Q ,y Q ,z Q represent the horizontal, vertical, and vertical coordinates of the reconstructed three-dimensional model, respectively; x q ,y q represent the horizontal and vertical coordinates of the registered point cloud data, respectively, and w q represents the camera's axis distance parameter.

[0098]

[0099] Where f x represents the x-axis camera axis distance, f y represents the y-axis camera axis distance, e x represents the horizontal coordinate of the point cloud data on the x-axis, and e y represents the vertical coordinate of the point cloud data on the y-axis. represents the rotation vector matrix, r 11 represents the first row and first column of the rotation vector in the rotation vector matrix, represents the translation vector matrix, and t1 represents the first row translation vector.

[0100] Further, the converted three-dimensional model is set as the three-dimensional model data of the office building to be rented out;

[0101] S2, performing data processing on the obtained three-dimensional model data of the office building to be rented out by using a data processing method to determine the area of the office building to be rented out;

[0102] The three-dimensional model data of the office building to be rented out obtained by using a data processing method is processed by using a data processing method to determine the area of the office building to be rented out, which includes the following steps:

[0103] Select any two points A(x1, y1) and B(x2, y2) in the real scene of the office building to be rented out, and measure the distance between the two selected points to obtain the actual distance DL between the two points.

[0104] Furthermore, four points are selected from the obtained 3D model data of the office building to be leased to form a rectangle. The coordinates of the four points are set as A(x1, y1), B(x2, y2), C(x3, y3), and E(x4, y4). According to the distance formula between two points, the model distance between AB can be calculated as DL1, and the model distance between CE can be calculated as DL2.

[0105] The formula for the distance between two points is as follows:

[0106]

[0107] Furthermore, the ratio between the actual distance and the model distance is calculated

[0108] The actual area of ​​the 3D model data of the office building to be leased is S = DL1 × DL2 × P;

[0109] S3. Collecting apartment type data of office buildings to be leased, and clustering the office buildings to be leased collected in real time based on the determined area of ​​the office buildings to be leased using a clustering algorithm to obtain clustering results. The clustering results include: leased office building type, cluster center, and clustered leased office building data;

[0110] S31, initialize the collected apartment type data and area data of office buildings that need to be rented, set the collected apartment type data and area data of office buildings that need to be rented as rental office building data, and construct a rental office building set x = {x1, x2, ..., x i ,...x n};

[0111] Among them, x i represents the i-th group of rental office building data, x n Represents the nth group of rental office building data;

[0112] Select k groups of rental office building data in the rental office building set as the initial cluster center a={a1,a2,...,a k}, set each cluster center to represent the central data of a rental office building type;

[0113] S32, based on each group of samples x in the rental office building set i , according to the distance formula, Calculate the data x for each group of rental office buildings i The distance to k cluster centers is calculated, and the samples are classified into the rental office building type corresponding to the cluster center with the smallest distance. One group is added at a time until all samples are clustered. The result of this classification is then used as the input of step S13, and step S13 is executed again.

[0114] wherein, represents the distance from each set of office building data x i to the cluster center a j , a j represents the jthcluster center, and the value range of j is set as [1, k];

[0115] S33, when all the office building data classification is completed, for each office building type c={c1, c2,..., c α}, the cluster center of each office building type is recalculated wherein, a c represents the cluster center of the office building type c;

[0116] wherein, c α represents the a-thset of office building data in the office building type c, c r represents the r-thset of office building data in the office building type c, represents the distance from the office building data c r to the current product category cluster center;

[0117] Based on the recalculated cluster center of each office building type and the distance formula, the clustered office building data in each office building type is recalculated;

[0118] S34, repeat steps S12-S13 until the cluster center and the office building data in the office building type no longer change, output the office building type, the cluster center and the clustered office building data;

[0119] S4, manage the obtained clustering results according to the collected office building location data;

[0120] The management of the obtained clustering results according to the collected office building location data includes the following steps:

[0121] Set H1 to represent the collected office building location data, set H2 to represent the calculated house type data of the office building to be rented, and set H3 to represent the calculated area data of the office building to be rented;

[0122] Correlate the office building location data, the house type data of the office building to be rented, and the area data of the office building to be rented by using the grey correlation degree algorithm;

[0123] The formula of the grey correlation degree algorithm is as follows:

[0124]

[0125] r=1,2,3...m; β∈{H1,H2,H3};

[0126] Among them, R β represents the calculated weight of the βth attribute, H1(r) represents the score of user r on the location data of the rental office building, and H β (r) represents the importance that user r places on the location data of the leased office building; user r’s score for the βth attribute; ρ represents the grey correlation parameter; min β Indicates the minimum score of the βth attribute, max β Indicates the maximum score of the βth attribute, min r Indicates the minimum score of user r, max r represents the maximum rating of user r;

[0127] Recalculate the attributes of each office building to be leased based on the attribute weights calculated using the grey correlation algorithm, and sort them in descending order based on the calculated results;

[0128] Furthermore, a minimum score threshold for each attribute of each office building to be leased is set, and office buildings to be leased that do not meet the conditions are removed from the list based on the set minimum score threshold;

[0129] Example 2

[0130] This embodiment also discloses an intelligent management system for office building leasing based on BIM technology, including: a data acquisition module, a real-time modeling module, a model processing module, a data classification module and an office building management module;

[0131] The data acquisition module is used to collect image data, location data and apartment type data input by the user of the office building to be rented in real time;

[0132] The real-time modeling module is used to build a three-dimensional model based on the real-time collection of image data of the office building to be leased;

[0133] The model processing module is used to calculate the area of ​​each area of ​​the office building based on the three-dimensional model established by the facts;

[0134] The data classification module is used to classify office buildings according to the calculated areas of each area of ​​the office building and the apartment type data input by the user;

[0135] The office building management module is used to calculate the scores of various attributes of each classified office building and manage the collected office building data according to the calculated scores.

[0136] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. An intelligent management method for office building leasing based on BIM technology, characterized in that: The following steps are involved: S1. Collect image data and location data of the office building to be leased in real time, and perform three-dimensional visualization modeling on the collected image data of the office building to be leased to obtain three-dimensional model data of the office building to be leased; S2. Processing the obtained three-dimensional model data of the office building to be leased by a data processing method to determine the area of ​​the office building to be leased; The S2 comprises the following steps: In a real-world scenario where an office building needs to be rented, select any two points A(x1,y1) and B(x2,y2), and measure the distance between the two points to obtain the actual distance DL between the two points. Select four points from the obtained 3D model data of the office building to be leased to form a rectangle. Set the coordinates of the four points to be A(x1, y1), B(x2, y2), C(x3, y3), and E(x4, y4). According to the distance formula between two points, the model distance between AB can be calculated as DL1, and the model distance between CE can be calculated as DL2. The distance formula between two points is as follows: Calculate the ratio between the actual distance and the model distance The actual area of ​​the 3D model data of the office building to be leased is S = DL1 × DL2 × P; S3. Collecting apartment type data of office buildings to be leased, and clustering the leased office building data based on the determined area of ​​the office buildings to be leased using a clustering algorithm to obtain clustering results, the clustering results including: leased office building type, cluster center, and clustered leased office building data; Set the collected apartment type data and area data of office buildings that need to be rented as rental office building data; S4. Perform management analysis on the clustering results based on the collected location data of the leased office buildings; The S4 comprises the following steps: Set H1 to represent the collected location data of the office building to be leased, set H2 to represent the calculated apartment type data of the office building to be leased, and set H3 to represent the calculated area data of the office building to be leased; The location data of the office building to be leased, the unit type data of the office building to be leased, and the area data of the office building to be leased are correlated through the grey correlation algorithm; The grey relational algorithm formula is as follows: Among them, R β represents the calculated weight of the βth attribute, H1(r) represents the score of user r on the location data of the rental office building, and H β (r) represents the rating of user r on the βth attribute, ρ represents the grey relational parameter, min β Indicates the minimum score of the βth attribute, max β Indicates the maximum score of the βth attribute, min r Indicates the minimum score of user r, max r represents the maximum rating of user r; Recalculate the attributes of each office building to be leased based on the attribute weights calculated using the grey correlation algorithm, and sort them in descending order based on the calculated results; Set a minimum score threshold for each attribute of each office building that needs to be rented, and remove office buildings that do not meet the conditions from the list based on the set minimum score threshold.

2. The intelligent management method for office building leasing based on BIM technology according to claim 1 is characterized in that: The real-time collection of image data and location data of the office building to be leased and the three-dimensional visualization modeling of the collected image data of the office building to be leased include the following steps: S11. Using a camera to capture multiple images of the leased office building from different angles to obtain point cloud data of the leased office building. S12. Matching the point cloud data in the leased office building image to a unified coordinate system through a point cloud registration method to obtain registered point cloud data; S13. Convert the registered point cloud data into a three-dimensional model using a three-dimensional reconstruction algorithm.

3. The intelligent management method for office building leasing based on BIM technology according to claim 2 is characterized in that: The point cloud data in the leased office building image is matched to a unified coordinate system by point cloud registration to obtain the registered point cloud data, including the following steps: Assume that the point cloud dataset of the rental office building is P(u1,u2,...,u n ); Calculate the mean and covariance of a point cloud dataset of rental office buildings; The formulas for calculating mean and covariance are as follows: in, represents the mean of the point cloud dataset of the rental office building, cov represents the covariance of the point cloud dataset of the rental office building, u i Represents the i-th point cloud data in the point cloud dataset, n represents the number of point cloud datasets, and T represents the transpose calculation; Based on the calculated mean and covariance of the point cloud dataset of the rental office building, the covariance of the calculated point cloud dataset of the rental office building is set as the coordinate axis of the coordinate system, and the calculated mean of the point cloud dataset of the rental office building is set as the origin of the coordinate system; Based on the set coordinate system and origin, the point cloud data in the point cloud dataset of the leased office building is recalculated to obtain the registered point cloud data.

4. The intelligent management method for office building leasing based on BIM technology according to claim 2 is characterized in that: The method of converting the registered point cloud data into a three-dimensional model using a three-dimensional reconstruction algorithm comprises the following steps: The registered point cloud data is converted into a 3D model based on the set coordinate system and origin. The relationship between the two sets of coordinates is as follows: Set Q = (x Q ,y Q ,z Q ) is the reconstructed three-dimensional model, q=(x q ,y q ,w q ) is the point cloud data after registration; Among them, x Q ,y Q ,z Q Respectively represent the horizontal, vertical and vertical coordinates of the reconstructed three-dimensional model; x q ,y q Respectively represent the horizontal and vertical coordinates in the registered point cloud data, w q Indicates the wheelbase parameter of the camera; Among them, f x Indicates the x-axis camera wheelbase, f y Indicates the y-axis camera wheelbase, e x Represents the horizontal coordinate of the point cloud data on the x-axis, e y Represents the vertical coordinate of the point cloud data on the y-axis; Represents the rotation vector matrix, r 11 Represents the rotation vector of the first row and first column in the rotation vector matrix, r 12 Represents the rotation vector in the first row and second column of the rotation vector matrix, r 13 Represents the rotation vector in the first row and third column of the rotation vector matrix, r 21 Represents the rotation vector in the second row and first column of the rotation vector matrix, r 22 Represents the rotation vector in the second row and second column of the rotation vector matrix, r 23 Represents the rotation vector in the second row and third column of the rotation vector matrix, r 31 Represents the rotation vector in the third row and first column of the rotation vector matrix, r 32 Represents the rotation vector in the third row and second column of the rotation vector matrix, r 33 Represents the rotation vector in the third row and third column of the rotation vector matrix. Represents the translation vector matrix, t1 represents the translation vector of the first row, t2 represents the translation vector of the second row, and t3 represents the translation vector of the third row; The converted 3D model is set as the 3D model data of the office building to be rented.

5. The intelligent management method for office building leasing based on BIM technology according to claim 1 is characterized in that: The collecting of apartment type data of office buildings to be leased and clustering the leased office building data using a clustering algorithm based on the determined area of ​​the office buildings to be leased comprises the following steps: S31, initialize the collected apartment type data and area data of the office buildings to be rented, and construct the rental office building set x = {x1, x2, ..., x i ,...x n }; Among them, x i represents the i-th group of rental office building data, x n Represents the nth group of rental office building data; Select k groups of rental office building data in the rental office building set as the initial cluster center a={a1,a2,...,a k }, set each cluster center to represent the central data of a rental office building type; S32, based on each group of samples x in the rental office building set i , according to the distance formula, Calculate the data x for each group of rental office buildings i The distance to k cluster centers is calculated, and the samples are classified into the rental office building type corresponding to the cluster center with the smallest distance. One group is added at a time until all samples are clustered. The result of this classification is then used as the input of step S13, and step S13 is executed again. in, Represents each group of rental office building data x i To cluster center a j The distance, a j represents the jth cluster center, and the value range of j is set to [1, k]; S33. After all the rental office building data are classified, for each rental office building type c={c1, c2, ..., c α }, recalculate the cluster center of each rental office building type Among them, a c represents the cluster center of rental office building type c; Among them, c α represents the αth group of rental office building data in rental office building type c, c r represents the rth group of rental office building data in rental office building type c, Represents the rental office building data c r The distance to the center of the current product category cluster; Based on the recalculation of the cluster center and distance formula for each type of leased office building, the clustered leased office building data in each type of leased office building is recalculated; S34. Repeat steps S12-S13 until the cluster center and the leased office building data in the leased office building type no longer change, and then output the leased office building type, cluster center, and clustered leased office building data.

6. A system for implementing the intelligent management method for office building leasing based on BIM technology according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, real-time modeling module, model processing module, data classification module and office building management module; The data acquisition module is used to collect image data, location data and apartment type data input by the user of the office building to be rented in real time; The real-time modeling module is used to build a three-dimensional model based on the real-time collection of image data of the office building to be leased; The model processing module is used to calculate the area of ​​each area of ​​the office building based on the three-dimensional model established by the facts; The data classification module is used to classify office buildings according to the calculated areas of each area of ​​the office building and the apartment type data input by the user; The office building management module is used to calculate the scores of various attributes of each classified office building and manage the collected office building data according to the calculated scores.

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