CN117952565B - BIM model-based intelligent management method and system for fabricated building - Google Patents
BIM model-based intelligent management method and system for fabricated building Download PDFInfo
- Publication number
- CN117952565B CN117952565B CN202410337951.1A CN202410337951A CN117952565B CN 117952565 B CN117952565 B CN 117952565B CN 202410337951 A CN202410337951 A CN 202410337951A CN 117952565 B CN117952565 B CN 117952565B
- Authority
- CN
- China
- Prior art keywords
- point
- measured
- points
- acquisition
- cluster
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Geometry (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Artificial Intelligence (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Civil Engineering (AREA)
- Health & Medical Sciences (AREA)
- Architecture (AREA)
- Structural Engineering (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of intelligent management, in particular to an intelligent management method and system for an assembled building based on a BIM model. The method comprises the following steps: acquiring acquisition points, and clustering to obtain point clusters at the same position; determining the distribution significance according to the spatial distribution characteristics of all the acquisition points of the point to be measured in the position point cluster and the spatial distances between the point to be measured and other acquisition points of the position point cluster; determining a structural error index according to the distribution significance degree of the acquisition points and the distance value of the center position coordinates of the points to be detected and the corresponding point clusters at the same position; determining a target error range of the position of the acquisition point according to the structural error indexes of all the acquisition points; and managing the assembly of the component to be tested according to the target error range. The method can obtain accurate and objective error precision of different acquisition point positions, thereby facilitating assembly management according to a target error range and improving the intelligent management effect of BIM model simulation.
Description
Technical Field
The invention relates to the technical field of intelligent management, in particular to an intelligent management method and system for an assembled building based on a BIM model.
Background
The assembly type design simulation technology based on the combination of quantum computation and BIM model can realize the simulation assembly effect of high efficiency, high precision, high automation and high intellectualization, and is very important for the intelligent assembly of the assembly type building by performing simulation verification and management on the position of each component in the simulation assembly process.
In the related art, by setting the standard position of each point in the simulation, and then analyzing whether the simulation assembly of the component is qualified or not according to the difference between the current position and the standard position of the component, in this way, the assembly accuracy required by different components in the actual assembly process is different, that is, the assembly errors of the components inevitably generate an acceptable range in the actual assembly process, and the assembly errors of different components in different point positions also have certain difference, the reasonable and effective management cannot be realized on the actual assembly of the component by a unified qualification judging mode, and the intelligent management effect is poor when the BIM model is used for simulation.
Disclosure of Invention
In order to solve the technical problem that the actual assembly of components cannot be reasonably and effectively managed by a unified qualification judging mode in the related art, and the intelligent management effect is poor when BIM model simulation is used, the invention provides an intelligent management method and system for an assembled building based on the BIM model, and the adopted technical scheme is as follows:
The invention provides an intelligent management method of an assembled building based on a BIM model, which comprises the following steps:
acquiring angular points of the same components to be detected in at least two assembly buildings of the same type as acquisition points, and clustering the acquired acquisition points of all the components to be detected to obtain a same-position point cluster;
taking the acquisition points of any co-located point cluster as to-be-measured points, determining local distribution characteristic factors of the to-be-measured points according to the spatial distribution characteristics of all the acquisition points of the to-be-measured points in the co-located point cluster, and determining distance influence factors of the to-be-measured points according to the spatial distances between the to-be-measured points and other acquisition points of the co-located point cluster;
Determining the distribution significance degree of the points to be measured according to the local distribution characteristic factors and the distance influence factors of the points to be measured; determining a structural error index of the point to be measured according to the distribution significance degree of all the acquisition points in the point cluster at the same position of the point to be measured and the distance value of the point to be measured from the central position coordinate of the point cluster at the same position;
determining a target error range of the position of the acquisition point corresponding to the member to be detected according to the structural error indexes of all the acquisition points in the position point cluster; and managing the assembly of the components to be tested according to the target error range of each collecting point position in all the components to be tested.
Further, the determining the local distribution characteristic factor of the to-be-measured point according to the spatial distribution characteristics of all the acquisition points of the to-be-measured point in the co-located point cluster includes:
Calculating the ratio of the number of all the acquisition points of the point to be measured in the position point cluster to the minimum external spherical volume of the position point cluster as the density to be measured;
respectively connecting the point to be detected with each acquisition point in a preset neighborhood range to obtain a connecting line; taking an included angle formed by any two connecting lines as a connecting included angle; normalizing the maximum value of the connecting included angle to obtain an angle influence coefficient;
and determining local distribution characteristic factors of the to-be-measured points according to the to-be-measured density and the angle influence coefficient.
Further, the density to be measured and the angle influence coefficient are in negative correlation with the local distribution characteristic factor of the point to be measured, and the value of the local distribution characteristic factor is a normalized value.
Further, the determining the distance influence factor of the to-be-measured point according to the spatial distance between the to-be-measured point and other acquisition points of the located position point cluster includes:
and calculating the normalized value of the length mean value of all connecting lines as the distance influence factor of the to-be-measured point.
Further, the determining the distribution significance level of the point to be measured according to the local distribution feature factor and the distance influence factor of the point to be measured includes:
And calculating a normalized value of the product of the local distribution characteristic factor and the distance influence factor of the point to be measured to obtain the distribution significance of the point to be measured.
Further, the determining the structural error index of the point to be measured according to the distribution significance degree of all the acquisition points in the point cluster at the same position where the point to be measured is located and the distance value of the point to be measured from the central position coordinate of the point cluster at the same position, includes:
Calculating the average value of the distribution significance degree of all the acquisition points in the point cluster at the same position where the point to be detected is located, and obtaining the average value of the significance degree;
Taking a normalized value of a difference value between the distribution significance level of the points to be detected and the significance level mean value as a structural abnormality index;
and calculating a normalized value of the product of the structural abnormality index and the distance value of the point to be measured from the central position coordinate of the corresponding co-located point cluster, and taking the normalized value as the structural error index of the point to be measured.
Further, the determining, according to the structural error indexes of all the acquisition points in the same position point cluster, the target error range of the position of the acquisition point corresponding to the member to be measured includes:
Taking an acquisition point with the structure error index of which the position point cluster belongs to less than a preset index threshold value as an error point, taking a point cluster formed by all error points as a target point cluster, and acquiring a midpoint of the target point cluster;
Calculating the maximum value of Euclidean distances between all error points and the midpoint of the target point cluster, and taking the maximum value as an error threshold;
And taking a numerical range which is larger than or equal to a numerical value 0 and smaller than or equal to the error threshold value as a target error range of the position of the corresponding acquisition point of the component to be detected.
Further, the managing the assembly of the components to be tested according to the target error range of each collecting point position in all components to be tested includes:
determining the Euclidean distance between each acquisition point in the member to be measured and the midpoint of the corresponding target point cluster as a distance to be measured;
Determining whether the distance to be detected of each acquisition point in the member to be detected belongs to a target error range of the position of the corresponding acquisition point;
If the distance to be measured of one or more acquisition points does not belong to the target error range of the position of the corresponding acquisition point, adjusting the member to be measured;
And if all the distances to be measured of the acquisition points belong to the target error range corresponding to the positions of the acquisition points, taking the positions of the members to be measured as assembly positions.
Further, the clustering processing is performed on the collected points of all the collected components to be tested to obtain a same-position point cluster, which comprises the following steps:
Taking the number of corner points of the component to be detected as the number of clustering clusters;
And taking the number of the clustering point clusters as a k value, and carrying out spatial position clustering on all the acquisition points of all the fabricated buildings by using a k-means clustering algorithm to obtain a plurality of position point clusters of the number of the clustering point clusters.
The invention also provides an intelligent management system of the fabricated building based on the BIM model, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the intelligent management method of the fabricated building based on the BIM model when executing the computer program.
The invention has the following beneficial effects:
According to the invention, the acquisition points are clustered to obtain the same-position point cluster, the distribution significance degree of the points to be detected is determined according to the spatial distribution characteristics and the spatial distances of all the acquisition points in the same-position point cluster, the spatial distribution characteristics represent the relative spatial positions of the same-position point cluster where the points to be detected are located, and the spatial distances represent the offset conditions of the corresponding points to be detected and other acquisition points, so that the distribution significance degree can accurately represent the significance index of the points to be detected, and then the structural error index of the points to be detected is determined according to the distribution significance degree of all the acquisition points in the same-position point cluster where the points to be detected and the distance value of the points to be detected, which corresponds to the central position coordinates of the same-position point cluster, so that the structural error index objectivity is stronger, the offset conditions of the points to be detected can be represented more, and further, the abnormal analysis is carried out on all the acquisition points according to the structural error index, the target error range can accurately represent the error influence precision of the positions corresponding to the points to be detected, the target error range is obtained, the self-adaptive target error range can be obtained for each acquisition point position, and the BIM is convenient for intelligent management error management according to the target error management.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an intelligent management method for an assembled building based on a BIM model according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects thereof according to the BIM model-based intelligent management method and system for the fabricated building provided by the invention with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a concrete scheme of the intelligent management method for the fabricated building based on the BIM model.
Referring to fig. 1, a flow chart of an intelligent management method for an assembled building based on a BIM model according to an embodiment of the present invention is shown, where the method includes:
s101: and acquiring angular points of the same component to be detected in at least two assembly buildings of the same type as acquisition points, and clustering the acquired acquisition points of all the components to be detected to obtain the same-position point cluster.
The method has the specific application scene that the angular points of the to-be-detected component are detected in the BIM model, so that the assembly process of the to-be-detected component is managed and analyzed.
In the embodiment of the invention, as the number of the components to be tested in the assembled building is more, the number of the acquisition points of the components to be tested is more, and the BIM model simulation analysis on the space is required to be carried out on the components to be tested in a plurality of identical assembled buildings, the accurate calculation analysis is required, and the assembled management is effectively carried out.
In the embodiment of the invention, a plurality of sensors can be used for carrying out space scanning on the assembled building, and then information obtained by scanning is simulated in a BIM model, wherein the to-be-detected component can be a precast concrete component, a steel structural component and the like. That is, in the BIM model, the center points of all the fabricated buildings are used as the origins, and all the acquisition points of all the components to be tested of all the fabricated buildings are marked in the three-dimensional coordinate system of the BIM model, so that unified analysis of all the fabricated buildings is realized.
It should be noted that, in the fabricated building, the position sensitivity of some components to be tested is low, for example, some support rods are slightly shifted, and still can play a supporting role, but the position sensitivity of some components to be tested is high, and a slight shift can cause the actual assembly problem of the fabricated building itself, for example, a part needing to be nested, if a shift occurs, the nesting process cannot be performed, so that the analysis of different components to be tested of the fabricated building is different, and separate analysis is required for each component to be tested.
In a specific assembly process, the model needs to be corrected according to an actual measurement result, a large amount of error information data generated by the model often affects the data intelligent management efficiency of the BIM model of the assembly type building, and the structural error data generated by module assembly affects the accurate range of the acceptable error of the model component.
It will be appreciated that in some embodiments of the present invention, the angular point positions of the members to be measured with different shapes may be changed, so that the shape and position characteristics of the members to be measured may be determined by detecting the angular points, and of course, in other embodiments of the present invention, the collecting points may be further determined according to preset collecting points, for example, collecting points in any multiple directions, or collecting points in preset directions, so as to collect collecting points capable of effectively characterizing the shape and position of the members to be measured.
In the embodiment of the invention, each acquisition point has a corresponding position attribute, the position attribute can be presented relative to a certain fixed space point in the assembled building, for example, in a BIM model, the center point of the assembled building is determined as the fixed space point, then, according to the center point, space simulation construction is carried out on all the components to be tested in the assembled building, in the space simulation construction process, the acquisition point corresponding to the corresponding components to be tested is placed in the corresponding space range, thus, the simulation construction of the whole assembled building can be realized, and it can be understood that in the process of detecting the angular point of the position to be tested, certain displacement can be generated in the actual assembly process of the components to be tested due to the connection condition in the actual assembly process of the components to be tested, and if the assembly failure is too serious, the position deviation of the components to be tested per se is required to be effectively analyzed, and intelligent assembly management is realized according to the analysis result.
Further, in some embodiments of the present invention, clustering the collected points of all the collected members to be tested to obtain a co-located point cluster includes: taking the number of corner points of the member to be detected as the number of clustering point clusters; and taking the number of the clustering point clusters as a k value, and carrying out spatial position clustering on all the acquisition points of all the fabricated buildings by using a k-means clustering algorithm to obtain a plurality of position point clusters of the number of the clustering point clusters.
In the embodiment of the invention, the number of the corner points of the component to be detected can be used as the number of the cluster point clusters, that is, when the number of the corner points of one component to be detected is 8, all the collection points can be correspondingly divided into 8 cluster point clusters, and each cluster point cluster can represent the spatial position area of the corresponding corner point.
After the number of the clustering point clusters is obtained, the method can cluster all the acquisition points of all the assembled buildings at the whole space position in space position according to the number of the clustering point clusters, wherein the method can use a k-means clustering algorithm or can use a plurality of other clustering modes to cluster all the acquisition points of all the assembled buildings to obtain the number of the clustering point clusters at the same position.
The same position point cluster can represent that all the acquisition points in the corresponding point cluster are the acquisition points in the same position, and the sizes of the position point clusters corresponding to different members to be detected are different due to the different sizes of the members to be detected, and the sizes of the same position point cluster to be detected are similar.
S102: and taking the acquisition points of any co-located point cluster as to-be-measured points, determining local distribution characteristic factors of the to-be-measured points according to the spatial distribution characteristics of all the acquisition points of the to-be-measured points in the co-located point cluster, and determining distance influence factors of the to-be-measured points according to the spatial distances between the to-be-measured points and other acquisition points of the co-located point cluster.
After the same-position point cluster is determined, the spatial distribution of the acquisition points in the same-position point cluster is analyzed. Further, in some embodiments of the present invention, determining a local distribution characteristic factor of the to-be-measured point according to the spatial distribution characteristics of all the collected points of the to-be-measured point in the co-located point cluster includes: calculating the ratio of the number of all the acquisition points of the point to be measured in the position point cluster to the volume of the minimum circumscribed sphere of the position point cluster as the density to be measured; respectively connecting the to-be-measured point with each acquisition point in a preset neighborhood range to obtain a connecting line; taking an included angle formed by any two connecting lines as a connecting included angle; normalizing the maximum value of the connecting included angle to obtain an angle influence coefficient; and determining local distribution characteristic factors of the to-be-measured points according to the to-be-measured density and the angle influence coefficient.
The spatial distribution characteristics can represent the spatial distribution discrete condition of all the acquisition points in the same-position point cluster, and the invention uses the local distribution characteristic factors to represent the spatial distribution characteristics of the points to be measured in a local range, and specifically, the two characteristic attributes of density and spatial angle are used for analysis.
The density characteristic is that the ratio of the number of all the acquisition points in the position point cluster to the minimum external sphere of the position point cluster is calculated as the density to be measured, that is, the number of the acquisition points is compared with the volume of the position point cluster, so that the density to be measured is obtained.
In the embodiment of the invention, each acquisition point in the range of the to-be-measured point and the preset neighborhood is respectively connected to obtain a connecting line; the included angle formed by any two connecting lines is used as a connecting included angle, namely, any two connecting lines intersect with the point to be detected to form a corresponding included angle, so that the maximum value of the included angle and the spatial distribution of the position point clusters corresponding to the visual angle of the point to be detected can be represented, when the point to be detected is positioned in the middle of the position point clusters, other collecting points are arranged around the point to be detected, and the included angle is larger; when the point to be measured is positioned at the edge of the point cluster at the same position, only one side is provided with other acquisition points, and the included angle is smaller. That is, the closer the spatial position of the point to be measured is to the edge of the point cluster at the same position, the smaller the angle of the connecting line between the corresponding point to be measured and other acquisition points is, the smaller the angle influence coefficient is, and the more abnormal the point to be measured is.
Therefore, the method and the device can be used for analyzing the local distribution characteristic factors of the points to be measured by combining the density to be measured and the angle influence coefficient, and further, in the embodiment of the invention, the density to be measured and the angle influence coefficient are in negative correlation with the local distribution characteristic factors of the points to be measured, and the value of the local distribution characteristic factors is a normalized value.
The negative correlation represents that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, and may be a subtraction relationship, a division relationship, or the like, which is determined by practical application. The normalization process may specifically be, for example, maximum and minimum normalization processes, and the normalization in the subsequent steps may all employ maximum and minimum normalization processes, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein. In the embodiment of the invention, the inverse proportion normalization value of the product of the density to be measured and the angle influence coefficient can be calculated to obtain the local distribution characteristic factor.
That is, the present invention uses the position point cluster where the point to be measured is located as a local area, and then, the spatial position of the point to be measured in the local area and the density of the local area are specifically analyzed to obtain the local distribution characteristic factor.
Further, in some embodiments of the present invention, determining a distance impact factor of a to-be-measured point according to a spatial distance between the to-be-measured point and other acquisition points of the located location point cluster includes: and calculating the normalized value of the length mean value of all the connecting lines as a distance influence factor of the to-be-measured point.
In the embodiment of the invention, the Euclidean distance between the point to be detected and other acquisition points in the BIM model can be used as the space distance.
In the embodiment of the invention, the space distance between the point to be measured and other acquisition points can be detected, and then the deviation degree of the point to be measured is analyzed according to the space distance, and it can be understood that under the normal assembly of the component to be measured, the space distance between the point to be measured and other normal acquisition points is smaller, and when the assembly is abnormal, the corresponding space distance is larger.
S103: determining the distribution significance degree of the points to be measured according to the local distribution characteristic factors and the distance influence factors of the points to be measured; and determining the structural error index of the point to be measured according to the distribution significance degree of all the acquisition points in the point cluster at the same position where the point to be measured is located and the distance value of the point to be measured from the central position coordinate of the point cluster at the same position.
After the local distribution characteristic factors and the distance influence factors are obtained, the embodiment of the invention can combine the local distribution characteristic factors and the distance influence factors of the points to be measured to specifically analyze the distribution significance of the points to be measured.
Further, in some embodiments of the present invention, determining the distribution saliency degree of the points to be measured according to the local distribution characteristic factor and the distance influence factor of the points to be measured includes: and calculating a normalized value of the product of the local distribution characteristic factor and the distance influence factor of the point to be measured to obtain the distribution significance of the point to be measured.
The distribution significance degree is a significance index of the distribution of the point to be measured, the distribution of all the acquisition points in the point cluster at the position to be measured is compared, and the more abnormal the point to be measured is, namely the larger the difference is relative to other acquisition points, the higher the corresponding distribution significance degree is.
Further, in some embodiments of the present invention, determining a structural error index of a point to be measured according to a distribution significance level of all collected points in a point cluster at the same position where the point to be measured is located and a distance value of a point to be measured from a central position coordinate of the point cluster at the same position, includes: calculating the average value of the distribution significance degree of all the acquisition points in the point cluster at the same position of the point to be detected, and obtaining the average value of the significance degree; taking a normalized value of a difference value between the distribution significance level and the significance level mean value of the points to be detected as a structural abnormality index; and calculating a normalized value of the product of the structural abnormality index and the distance value of the point to be measured, which corresponds to the central position coordinate of the point cluster at the same position, as a structural error index of the point to be measured.
The structural error index specifically represents an error index of a spatial structure of a point cluster at a position of a point to be measured, represents an error condition of the position of the point to be measured when the point to be measured is assembled with a standard, and it can be understood that all the acquisition points in the point cluster at the same position of the point to be measured can represent error conditions of all the same position, and the larger the numerical value is, the lower the position requirement precision of a corresponding member to be measured during assembly is, so that the average value of the distribution significance degree of all the acquisition points in the point cluster at the same position of the point to be measured is calculated, the average value of the significance degree is obtained, and the larger the average value of the significance degree is, the lower the position requirement precision of the position of the corresponding point to be measured during assembly is represented.
Therefore, a normalized value of a difference value between the distribution significance level and the significance level mean value of the to-be-measured points is calculated to serve as a structural abnormality index, when the difference value between the distribution significance level and the significance level mean value of the to-be-measured points is larger, the corresponding significance level mean value of the to-be-measured points is represented to be still abnormal compared with the significance level mean value containing error influence, and the structural abnormality index and the normalized value of the product of the distance value of the to-be-measured points from the center position coordinates of the corresponding same-position point clusters are calculated to serve as the structural error index of the to-be-measured points to represent the most objective practical abnormal condition of the to-be-measured points.
S104: determining a target error range of the position of the acquisition point corresponding to the member to be detected according to the structural error indexes of all the acquisition points in the same-position point cluster; and managing the assembly of the components to be tested according to the target error range of the position of each acquisition point in all the components to be tested.
It can be understood that the structural error index of the acquisition point can influence the abnormal analysis result of the point cluster at the same position where the point to be detected is located, so that the influence of the acquisition point which is excessively abnormal on the abnormal analysis in the BIM model simulation process is required to be eliminated.
Further, in some embodiments of the present invention, determining a target error range of a position of an acquisition point corresponding to a member to be measured according to a structural error index of all the acquisition points in a same-position point cluster includes: taking an acquisition point with the structure error index of which the position point cluster belongs to less than a preset index threshold value as an error point, taking a point cluster formed by all error points as a target point cluster, and acquiring a midpoint of the target point cluster; calculating the maximum value of Euclidean distances between all error points and the middle point of the target point cluster, and taking the maximum value as an error threshold; and taking the numerical range which is larger than or equal to the numerical value 0 and smaller than or equal to the error threshold value as a target error range of the position of the corresponding acquisition point of the component to be detected.
In the embodiment of the invention, the preset index threshold is set to be 0.8, that is, the acquisition point with the structural error index less than 0.8 in the same-position point cluster is used as the error point, the error point is the acquisition point in the allowable error range, and the acquisition point with the structural error index greater than or equal to 0.8 is the abnormal point of the assembly, so that the point cluster formed by all the error points is used as the target point cluster, that is, the target point cluster is compared with the same-position point cluster, the abnormal point is deleted, and the normal error condition can be represented.
The invention analyzes all error points in the target point cluster to obtain the midpoint of the target point cluster; calculating the maximum value of Euclidean distances between all error points and the middle point of the target point cluster, and taking the maximum value as an error threshold; the error threshold is the maximum value of the positional deviation error, and thus, a numerical range greater than or equal to the numerical value 0 and less than or equal to the error threshold is taken as a target error range of the position of the corresponding acquisition point of the member to be detected, for example, when the error threshold is calculated to be 5, the numerical range corresponding to [0,5] is taken as the target error range. It will be appreciated that each acquisition point of each component to be tested has a corresponding error range.
Further, in some embodiments of the present invention, managing the assembly of the component to be tested according to the target error range of each acquisition point position in all the components to be tested includes: determining Euclidean distance between each acquisition point in the member to be measured and the midpoint of the corresponding target point cluster as a distance to be measured; determining whether the distance to be detected of each acquisition point in the member to be detected belongs to a target error range of the position of the corresponding acquisition point; if the distance to be measured of one or more acquisition points does not belong to the target error range of the position of the corresponding acquisition point, adjusting the member to be measured; and if the to-be-detected distances of the acquisition points all belong to the target error range corresponding to the positions of the acquisition points, taking the positions of the to-be-detected components as assembly positions.
The method comprises the steps of analyzing a member to be tested, wherein the distance to be tested of all the acquisition points in the member to be tested all belong to a target error range corresponding to the positions of the acquisition points, namely the assembly process of the member to be tested meets the requirement of actual assembly precision, and the member to be tested is assembled at a normal position and takes the position of the member to be tested as an assembly position.
When the distance to be measured of one or more collecting points in the member to be measured does not belong to the target error range of the position of the corresponding collecting point, the first condition is that the assembling position of the member to be measured is offset, namely, the position of the member to be measured is adjusted, so that the distance to be measured of the collecting points is all in the target error range of the position of the corresponding collecting point; the second condition is that the member to be detected generates damage or adhesion and other specification anomalies, namely the shape of the member to be detected changes, at the moment, other members with the same type as the member to be detected are replaced, and detection is continued until the distance to be detected of the acquisition point completely belongs to the target error range corresponding to the position of the acquisition point.
Through corresponding analysis, the intelligent simulation management of the actual assembly process of the components in the assembled building can be realized in the BIM model, so that a more reliable assembly effect can be obtained.
According to the invention, the acquisition points are clustered to obtain the same-position point cluster, the distribution significance degree of the points to be detected is determined according to the spatial distribution characteristics and the spatial distances of all the acquisition points in the same-position point cluster, the spatial distribution characteristics represent the relative spatial positions of the same-position point cluster where the points to be detected are located, and the spatial distances represent the offset conditions of the corresponding points to be detected and other acquisition points, so that the distribution significance degree can accurately represent the significance index of the points to be detected, and then the structural error index of the points to be detected is determined according to the distribution significance degree of all the acquisition points in the same-position point cluster where the points to be detected and the distance value of the points to be detected, which corresponds to the central position coordinates of the same-position point cluster, so that the structural error index objectivity is stronger, the offset conditions of the points to be detected can be represented more, and further, the abnormal analysis is carried out on all the acquisition points according to the structural error index, the target error range can accurately represent the error influence precision of the positions corresponding to the points to be detected, the target error range is obtained, the self-adaptive target error range can be obtained for each acquisition point position, and the BIM is convenient for intelligent management error management according to the target error management.
The invention also provides an intelligent management system of the assembled building based on the BIM model, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the intelligent management method of the assembled building based on the BIM model when executing the computer program.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (3)
1. The intelligent management method for the fabricated building based on the BIM model is characterized by comprising the following steps of:
acquiring angular points of the same components to be detected in at least two assembly buildings of the same type as acquisition points, and clustering the acquired acquisition points of all the components to be detected to obtain a same-position point cluster;
taking the acquisition points of any co-located point cluster as to-be-measured points, determining local distribution characteristic factors of the to-be-measured points according to the spatial distribution characteristics of all the acquisition points of the to-be-measured points in the co-located point cluster, and determining distance influence factors of the to-be-measured points according to the spatial distances between the to-be-measured points and other acquisition points of the co-located point cluster;
Determining the distribution significance degree of the points to be measured according to the local distribution characteristic factors and the distance influence factors of the points to be measured; determining a structural error index of the point to be measured according to the distribution significance degree of all the acquisition points in the point cluster at the same position of the point to be measured and the distance value of the point to be measured from the central position coordinate of the point cluster at the same position;
determining a target error range of the position of the acquisition point corresponding to the member to be detected according to the structural error indexes of all the acquisition points in the position point cluster; managing the assembly of the components to be tested according to the target error range of each collecting point position in all the components to be tested;
The determining the local distribution characteristic factor of the to-be-measured point according to the spatial distribution characteristics of all the acquisition points of the to-be-measured point in the same position point cluster comprises the following steps:
Calculating the ratio of the number of all the acquisition points of the point to be measured in the position point cluster to the minimum external spherical volume of the position point cluster as the density to be measured;
respectively connecting the point to be detected with each acquisition point in a preset neighborhood range to obtain a connecting line; taking an included angle formed by any two connecting lines as a connecting included angle; normalizing the maximum value of the connecting included angle to obtain an angle influence coefficient;
Determining local distribution characteristic factors of the to-be-measured points according to the to-be-measured density and the angle influence coefficient;
The density to be measured and the angle influence coefficient are in negative correlation with local distribution characteristic factors of the points to be measured, and the values of the local distribution characteristic factors are normalized values;
the determining the distance influence factor of the to-be-measured point according to the space distance between the to-be-measured point and other acquisition points of the located position point cluster comprises the following steps:
Calculating the normalized value of the length mean value of all connecting lines as the distance influence factor of the to-be-measured point;
The determining the distribution significance of the points to be measured according to the local distribution characteristic factors and the distance influence factors of the points to be measured comprises the following steps:
Calculating a normalized value of a product of the local distribution characteristic factor and the distance influence factor of the point to be measured to obtain the distribution significance of the point to be measured;
The determining the structural error index of the point to be measured according to the distribution significance degree of all the acquisition points in the point cluster at the same position where the point to be measured is located and the distance value of the point to be measured from the central position coordinate of the point cluster at the same position comprises the following steps:
Calculating the average value of the distribution significance degree of all the acquisition points in the point cluster at the same position where the point to be detected is located, and obtaining the average value of the significance degree;
Taking a normalized value of a difference value between the distribution significance level of the points to be detected and the significance level mean value as a structural abnormality index;
calculating a normalized value of a product of the structural abnormality index and a distance value of the point to be detected from a central position coordinate of a corresponding co-located point cluster, and taking the normalized value as a structural error index of the point to be detected;
The determining the target error range of the position of the acquisition point corresponding to the member to be detected according to the structural error indexes of all the acquisition points in the position point cluster comprises the following steps:
Taking an acquisition point with the structure error index of which the position point cluster belongs to less than a preset index threshold value as an error point, taking a point cluster formed by all error points as a target point cluster, and acquiring a midpoint of the target point cluster;
Calculating the maximum value of Euclidean distances between all error points and the midpoint of the target point cluster, and taking the maximum value as an error threshold;
taking a numerical range which is larger than or equal to a numerical value 0 and smaller than or equal to the error threshold value as a target error range of the position of the corresponding acquisition point of the component to be detected;
the managing the assembly of the components to be tested according to the target error range of each collecting point position in all components to be tested includes:
determining the Euclidean distance between each acquisition point in the member to be measured and the midpoint of the corresponding target point cluster as a distance to be measured;
Determining whether the distance to be detected of each acquisition point in the member to be detected belongs to a target error range of the position of the corresponding acquisition point;
If the distance to be measured of one or more acquisition points does not belong to the target error range of the position of the corresponding acquisition point, adjusting the member to be measured;
And if all the distances to be measured of the acquisition points belong to the target error range corresponding to the positions of the acquisition points, taking the positions of the members to be measured as assembly positions.
2. The intelligent management method for the fabricated building based on the BIM model as claimed in claim 1, wherein the clustering processing is performed on the collected points of all the collected components to be tested to obtain the same-position point clusters, and the method comprises the following steps:
Taking the number of corner points of the component to be detected as the number of clustering clusters;
And taking the number of the clustering point clusters as a k value, and carrying out spatial position clustering on all the acquisition points of all the fabricated buildings by using a k-means clustering algorithm to obtain a plurality of position point clusters of the number of the clustering point clusters.
3. A building information management system based on a BIM model, the system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the building information management method based on the BIM model according to any one of claims 1 to 2 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410337951.1A CN117952565B (en) | 2024-03-25 | 2024-03-25 | BIM model-based intelligent management method and system for fabricated building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410337951.1A CN117952565B (en) | 2024-03-25 | 2024-03-25 | BIM model-based intelligent management method and system for fabricated building |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117952565A CN117952565A (en) | 2024-04-30 |
| CN117952565B true CN117952565B (en) | 2024-06-18 |
Family
ID=90798099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410337951.1A Active CN117952565B (en) | 2024-03-25 | 2024-03-25 | BIM model-based intelligent management method and system for fabricated building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117952565B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119154515B (en) * | 2024-11-14 | 2025-06-24 | 山东德源电力科技股份有限公司 | Power distribution station intelligent monitoring platform based on artificial intelligence |
| CN119918809B (en) * | 2025-04-01 | 2025-07-15 | 中国电子工程设计院股份有限公司 | Method and device for generating PID of semiconductor secondary piping |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6420999B1 (en) * | 2000-10-26 | 2002-07-16 | Qualcomm, Inc. | Method and apparatus for determining an error estimate in a hybrid position determination system |
| KR101693405B1 (en) * | 2014-11-28 | 2017-01-05 | 건국대학교 산학협력단 | Apparatus and method for detecting anomaly intrusion using local deviation factor graph based algorithm |
| CN108629849A (en) * | 2018-05-16 | 2018-10-09 | 浙江大学 | A kind of component quality inspection system based on BIM with point cloud |
| CN116341069A (en) * | 2023-03-27 | 2023-06-27 | 中国十七冶集团有限公司 | BIM-based construction method for optimizing efficient collection, water storage and water storage of rainwater in artificial lake |
| CN116186634B (en) * | 2023-04-26 | 2023-07-07 | 青岛新航农高科产业发展有限公司 | Intelligent management system for construction data of building engineering |
-
2024
- 2024-03-25 CN CN202410337951.1A patent/CN117952565B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 张俊强 ; .基于智能视觉和BIM的建筑装配过程的高精度控制.计算机测量与控制.2017,(第10期),全文. * |
| 江玥 ; 周欣 ; 安晶晶 ; 燕达 ; 石邢 ; 金星 ; 曹静 ; 蒋选 ; .基于ASHARE-140标准的建筑热过程模拟结果对比校验――以DeST为例.建筑科学.2018,(第10期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117952565A (en) | 2024-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117952565B (en) | BIM model-based intelligent management method and system for fabricated building | |
| CN116186634B (en) | Intelligent management system for construction data of building engineering | |
| CN116431975B (en) | Environment monitoring method and system for data center | |
| CN111737078B (en) | Load type-based adaptive cloud server energy consumption measuring and calculating method, system and equipment | |
| CN113065223B (en) | Multi-level probability correction method for digital twin model of tower mast cluster | |
| CN117710008B (en) | Ecological product sales information management system suitable for karst region | |
| CN109444812B (en) | A RSSI Indoor Localization Method Introducing Dynamic Threshold | |
| CN112131752A (en) | A robust estimation algorithm for super-strong collapse pollution rate based on quasi-quasi-calibration | |
| CN117665716A (en) | An optimal vertical wavenumber design method for polarization interference synthetic aperture radar | |
| CN118470331A (en) | Ground point cloud segmentation method and system based on self-adaptive concentric circle model | |
| CN118882698B (en) | Elevation data error correction method and system for EGM2008 model | |
| CN118279745A (en) | Building construction monitoring method and system based on big data | |
| CN108519579B (en) | WiFi fingerprint positioning method for analyzing optimal AP based on interval overlapping degree | |
| CN117576087A (en) | Object surface convexity detection method based on point cloud normal | |
| CN111629321A (en) | Localization method of median Kalman filter based on outlier stripping | |
| CN117113248A (en) | Gas volume data anomaly detection method based on data driving | |
| CN113267139B (en) | Die casting deformation amount detection system with big data analysis | |
| CN115511929A (en) | Method and system for evaluating point cloud registration results based on distribution characteristics | |
| US12307738B2 (en) | 3D shape matching method and device based on 3D local feature description using SGHS | |
| Wenxuan et al. | Research on Indoor Edge Location Based on Location Fingerprint Recognition | |
| CN113190985A (en) | Method and system for analyzing data errors in layered heterogeneous region | |
| CN118049938B (en) | Rail measuring method, device, electronic equipment, readable storage medium and system | |
| CN112084286B (en) | Spatial data processing method and device, computer equipment and storage medium | |
| CN119803641B (en) | Electronic scale transaction misalignment monitoring method and system and electronic equipment | |
| CN117834455B (en) | Electric power Internet of things data transmission simulation method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |