CN117787670B - BIM data management method and system based on constructional engineering - Google Patents
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Abstract
The application discloses a BIM data management method and system based on construction engineering, belonging to the field of data management, wherein the method comprises the following steps: data point collection is carried out on a plurality of data sources in the target building engineering, and a building engineering data set is generated; performing cluster analysis on historical building engineering data of the target building engineering to generate a plurality of building engineering data classes; generating a data collaborative array, performing dimension reduction processing to obtain a dimension reduction data array, traversing BIM data of a target building project, performing matching with the dimension reduction data array to obtain a data matching point set, performing analysis management on the BIM data, and generating a management strategy; and managing BIM data of the target building engineering through a management strategy. The application solves the technical problem that the mass heterogeneous BIM data of the building engineering cannot be effectively managed in the prior art, and achieves the technical effect of realizing the integrated intelligent management of the BIM data of the building engineering through multi-source data acquisition and intelligent analysis.
Description
Technical Field
The invention relates to the field of data management, in particular to a BIM data management method and system based on constructional engineering.
Background
With the rapid development of computer technology and BIM technology, the construction industry has increasingly demanded management of engineering data. The BIM technology adopts a three-dimensional model and parameterized data to describe the building engineering, so that abundant and various data are contained, and the data support of the whole life cycle of the building engineering is realized. However, with the increase of the complexity of the BIM model, the quantity of engineering data is explosively increased, the data format is complex and various, and a plurality of difficulties are brought to the management and application of the data. The existing BIM data management technology mainly adopts a mode of collecting and sorting data in a mode of predefining a data template, is difficult to adapt to dynamic changes of data formats and contents, and cannot realize effective management of BIM data.
Disclosure of Invention
The application provides a BIM data management method and system based on constructional engineering, and aims to solve the technical problem that mass heterogeneous BIM data of the constructional engineering cannot be effectively managed in the prior art.
In view of the above problems, the present application provides a building engineering-based BIM data management method and system.
In a first aspect of the disclosure, a building engineering-based BIM data management method is provided, and the method includes: carrying out data point acquisition on a plurality of data sources in a target building project through a multidimensional sensing equipment group to generate a building project data set; generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of a target building engineering; generating a data collaborative array, wherein the data collaborative array is obtained by classifying and archiving a building engineering data set according to a plurality of building engineering data types and carrying out collaborative arrangement based on classified archiving results; performing dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set; performing BIM data analysis management on the data matching point set by combining a data analysis tool to generate a management strategy; and managing BIM data of the target building engineering through a management strategy based on the expected engineering progress of the target building engineering.
In another aspect of the disclosure, a building engineering-based BIM data management system is provided, the system comprising: the data point acquisition module is used for carrying out data point acquisition on a plurality of data sources in the target building engineering through the multidimensional sensing equipment group to generate a building engineering data set; the data cluster analysis module is used for generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of the target building engineering; the data collaborative arrangement module is used for generating a data collaborative array, wherein the data collaborative array is obtained by classifying and archiving a building engineering data set according to a plurality of building engineering data types and carrying out collaborative arrangement based on a classified archiving result; the data array matching module is used for carrying out dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, traversing BIM data of the target building engineering and matching the dimension reduction data array to obtain a data matching point set; the management strategy generation module is used for carrying out analysis management on BIM data in combination with the data analysis tool to generate a management strategy; and the BIM data management module is used for managing BIM data of the target building engineering through a management strategy based on the expected engineering progress of the target building engineering.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The multi-source heterogeneous data of the target building engineering is obtained by adopting the multi-dimensional sensing equipment group to collect data points of a plurality of data sources in the target building engineering and generating a building engineering data set; generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of a target building engineering, and providing a basis for realizing the integrated analysis of massive multi-source data; classifying and archiving the construction engineering data sets according to a plurality of construction engineering data types, carrying out cooperative arrangement based on classification archiving results, generating a data cooperative array, realizing archiving and classifying the collected data, and providing a basis for carrying out data dimension reduction processing; performing dimension reduction processing based on the data collaborative array, and matching BIM data of the target building engineering with the dimension reduction data array to obtain a data matching point set, so as to provide a basis for generating a management strategy; generating a management strategy for accurately and efficiently managing the BIM data of the target building engineering based on the data matching point set by utilizing a data analysis tool; according to project progress requirements, the technical scheme for dynamically managing BIM data through a management strategy solves the technical problem that the mass heterogeneous BIM data of the building engineering cannot be effectively managed in the prior art, and achieves the technical effect of realizing the integrated intelligent management of the BIM data of the building engineering through multi-source data acquisition and intelligent analysis.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of a building engineering-based BIM data management method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of obtaining a reduced-dimension data array in a BIM data management method based on construction engineering according to an embodiment of the application;
Fig. 3 is a schematic structural diagram of a building engineering-based BIM data management system according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a data point acquisition module 11, a data cluster analysis module 12, a data collaborative arrangement module 13, a data array matching module 14, a management strategy generation module 15 and a BIM data management module 16.
Detailed Description
The technical scheme provided by the application has the following overall thought:
the embodiment of the application provides a BIM data management method and system based on constructional engineering. Firstly, multi-source heterogeneous data are widely collected through data sources in target-oriented constructional engineering. Then, the collected data is subjected to intelligent analysis processing, and feature extraction, data matching and management strategy formulation of BIM data are realized. And finally, according to engineering plan requirements, implementing dynamic closed-loop control on BIM data of the target building engineering through an intelligent analyzed management strategy.
By means of data acquisition, analysis processing and closed-loop management, the method and the system effectively solve the technical problem that the intelligent integrated management of the building engineering BIM data is difficult to realize in the prior art, achieve efficient processing of multi-source heterogeneous data and accurate dynamic management of the BIM data in the building engineering, and achieve the technical effect of intelligent management of the building engineering BIM data.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the embodiment of the application provides a building engineering-based BIM data management method, which is applied to a building engineering-based BIM data management system, wherein the system is in communication connection with a multidimensional sensing device group and a data analysis tool.
Specifically, the embodiment of the application discloses a BIM data management method based on building engineering, which is used for effectively managing and utilizing BIM digital modeling data of the building engineering. The method is applied to a BIM data management system based on constructional engineering, and is in communication connection with a multidimensional sensing device group and a data analysis tool in a wired mode (such as Ethernet interface connection) or a wireless mode (such as WiFi, bluetooth, zigBee and other wireless communication protocols), so that data transmission and interaction between the BIM data management system and the multidimensional sensing device group and between the BIM data management system and the data analysis tool are realized. The multi-dimensional sensor group is a group of various sensors for collecting and detecting building environment data and comprises a plurality of types of sensors such as a temperature sensor, a humidity sensor, an image sensor, a gas sensor and the like, wherein the sensors measure parameters such as temperature, humidity, illumination and the like in the building environment and send the collected data to the BIM data management system through communication connection so as to realize multi-dimensional and omnibearing monitoring of the building internal environment and obtain a rich and comprehensive building data set; the data analysis tool is a software platform for analyzing and processing a data matching point set generated by building environment data, and is connected with the BIM data management system through communication to process the data in real time so as to provide a scientific management strategy.
The BIM data management method comprises the following steps:
Carrying out data point acquisition on a plurality of data sources in a target building project through the multidimensional sensing equipment group to generate a building project data set;
further, the method comprises the following steps:
Extracting a plurality of data features according to engineering expectations of a target building engineering;
determining a plurality of key indexes of the target building engineering according to the plurality of data characteristics;
The plurality of data sources is determined in accordance with the plurality of key indicators based on the multi-dimensional sensing device group.
In one possible implementation, prior to determining a plurality of data sources within a target building project, a plurality of data features that require attention are refined according to engineering expectations. Wherein, the engineering expectations refer to requirements expectations on the aspects of using functions, comfort level, energy saving indexes and the like of target building engineering, for example, the engineering expectations for office buildings comprise providing comfortable working environment, ensuring sufficient indoor illumination and the like; the plurality of data features are data parameters such as temperature, humidity, illumination intensity and the like which need to be monitored according to engineering expectations. Then, on the basis of extracting a plurality of data characteristics of the target building engineering, whether the building engineering reaches a plurality of key indexes expected by the engineering or not is determined and evaluated. For example, for an office building, its plurality of data features includes temperature, humidity, illumination, air quality, etc.; according to office use requirements, indoor temperature and humidity directly influence air conditioner energy consumption and human comfort level, illumination conditions influence working environment, and indoor carbon dioxide concentration reflects ventilation effect; therefore, the temperature, the humidity, the illumination intensity and the carbon dioxide concentration can be determined as a plurality of key indexes, and a direction is provided for building data acquisition. And setting specific sensors and acquisition positions for acquiring key index data through the multi-dimensional sensing equipment group to the determined multiple key indexes, so as to determine multiple data sources needing to be acquired.
And then, the multi-dimensional sensing equipment group collects the building environment data of each data source through a wired or wireless communication mode, collects the collected data of the multi-dimensional sensing equipment group, organizes and generates a building engineering data set, reflects information such as temperature distribution, humidity distribution, illumination distribution and gas concentration distribution in a target building engineering, and lays a data foundation for subsequent building data analysis and management.
Generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of a target building engineering;
in the embodiment of the application, the historical building engineering data refer to various sensor data such as a large amount of accumulated temperature, humidity, illumination and the like collected in the historical operation process of the building, and reflect the integral characteristics of the operation of the building.
Firstly, collecting historical building engineering data containing various historical monitoring data such as temperature, humidity, illumination, energy consumption, air quality and the like of a target building engineering, and preprocessing and denoising the historical building engineering data. Then, clustering algorithms, such as distance-based partitioning K-Means, density-based partitioning DBSCAN, hierarchical clustering, etc., are designed to aggregate similar data in the historical construction data into one class. And then, a clustering algorithm is operated to obtain a plurality of building engineering data clusters such as temperature data types, humidity data types, illumination data types, energy consumption data types and the like.
Through carrying out cluster analysis on the historical building engineering data of the target building engineering, a plurality of categories of building engineering data are obtained, and templates are provided for classifying and archiving newly acquired building engineering data sets.
Generating a data collaborative array, wherein the data collaborative array is obtained by classifying and archiving the building engineering data set according to the plurality of building engineering data types and carrying out collaborative arrangement based on classified archiving results;
further, the method comprises the following steps:
creating a data archive directory structure according to the plurality of building engineering data classes;
Based on the data archiving directory structure, carrying out data storage on the construction engineering data set to obtain a classified archiving result;
And cooperatively arranging the storage positions of the classified filing results to generate the data cooperative array.
In one possible embodiment, first, a plurality of acquired construction engineering data classes, such as a temperature class, a humidity class, an energy consumption class, etc., are extracted. Then, a first-level catalogue for data archiving is set according to the categories of a plurality of building engineering data categories, and the catalogue names are data category names, such as 'temperature', 'humidity', and the like. Next, under the primary directory of each type of data, a secondary subdirectory is created according to the acquisition time of the data, forming a time division, such as "2023 1 month data", etc. And then, setting a third-level subdirectory in the second-level subdirectory to store data of different building areas, and identifying the area position information acquired by the data by the file folder names. Thus, according to the plurality of construction engineering data classes, the data archiving directory structure is obtained according to the hierarchical organization, so that the construction engineering data sets are orderly stored.
And then, reading the constructional engineering data set, and determining the classification catalog to which each data belongs according to the existing data archiving catalog structure. For example, the temperature data is stored under a first-level catalog of 'temperature class', and the humidity data is stored in a first-level catalog of 'humidity class'; and determining a secondary time directory and a tertiary area directory of file storage according to the time attribute and the area attribute of the data file under the directory of the corresponding category, so as to realize classified filing and storage of the data. Repeating the above operation to obtain the data set storage result organized according to the category, time and area, namely the classified filing result. Thereafter, the inherent business associations and statistical correlations between the different data categories, i.e., the interactions or constraint relationships between them, are analyzed. Then, the rules of data storage are set without changing the data archive directory structure. For example, data storage locations with a large influence are to be adjacent or connected in sequence as much as possible, and correspondingly reflect business relevance. And then, re-planning and adjusting the address of the classified filing result in the storage according to the set data storage rule to generate a data collaborative array, wherein the organization form of the data collaborative array still keeps the data filing directory structure, but the data storage position is re-planned according to the tightness degree of the business constraint relation among the data, so that the storage layout of the classified organization and business association collaboration is realized.
Performing dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set;
further, as shown in fig. 2, obtaining the reduced data array includes:
Calculating and acquiring the distribution probability of a plurality of cooperative data and preset cooperative data in the data cooperative array in a preset conflict interval to acquire a distribution probability array;
Constructing a lattice similarity probability distribution function, performing dimension reduction processing on lattice distribution of the distribution probability array, and generating a first dimension reduction similarity divergence of the distribution probability array as a stage dimension reduction result;
and constructing the dimension reduction data array based on the stage dimension reduction result.
Further, the lattice similarity probability distribution function includes:
The lattice similarity probability distribution function is as follows:
；
Wherein, To characterize the measure of the proximity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array, let/>Performing dimension reduction processing by taking 0 as constraint condition,/>To characterize the divergence of the similarity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array,/>For lattice/>, in reduced dimension lattice distribution based on the distributed probability arrayAnd lattice/>Similarity probability of/>For dot matrix/>, in a dot matrix distribution based on the distributed probability arrayAnd lattice/>The similarity probability of (i, j) is any coordinate point in the data cooperative array, the value range of i is 0< i < n, the value range of j is 0< j < n, and n is the total number of coordinate points in the data cooperative array.
Further, obtaining the data matching point set includes:
accessing a historical building information file of the target building engineering, and extracting the BIM data of the target building engineering;
Traversing the BIM data of the target building engineering to determine a plurality of key feature vectors;
and matching the plurality of key feature vectors with the reduced-dimension data array by using a distance measure to obtain the data matching point set.
In a preferred embodiment, firstly, according to the business knowledge of the construction engineering data, the range of the normal value of the cooperative data is set, and when the range is beyond, the range is defined as conflict abnormal data, and the preset conflict range is obtained. And secondly, converting the normal value of the cooperative data in the preset conflict interval to a 0-1 interval based on a probability density distribution function to obtain a probability interval [ min, max ] corresponding to the preset conflict interval, taking the probability interval as a reference state, sequentially reading a plurality of cooperative data in the data cooperative array, and solving the probability distribution value x of the cooperative data based on the probability density distribution function. And then, comparing the solved probability distribution value with the magnitude relation of the reference state obtained from the preset conflict interval, and judging whether a conflict exists. If x is greater than or equal to min and x is less than or equal to max, judging that the cooperative data corresponding to the probability distribution value x has no conflict with a preset conflict interval. If x is smaller than min or x is larger than max, judging that the cooperative data corresponding to the probability distribution value x conflicts with a preset conflict interval, and removing the cooperative data with the conflict. In the reserved cooperative data, each data point is used for calculating probability distribution values of the data points and a reference state, and the probability distribution values are organized into a distribution probability array.
Then, constructing a lattice similarity probability distribution function as follows:
；
Wherein, To characterize the measure of the proximity between the lattice distribution of the distribution probability array and the dimension-reduction lattice distribution of the distribution probability array, let/>Performing dimension reduction processing by taking 0 as constraint condition,/>To characterize the divergence of the similarity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array,/>For dot matrix/>, in reduced dimension dot matrix distribution based on distributed probability arrayAnd lattice/>Similarity probability of/>For dot matrix/>, in dot matrix distribution based on distribution probability arrayAnd lattice/>The similarity probability of (i, j) is any coordinate point in the data cooperative array, the value range of i is 0< i < n, the value range of j is 0< j < n, and n is the total number of coordinate points in the data cooperative array. The lattice similarity probability distribution function is used for measuring the distribution difference between an original distribution probability array and a new array formed after the dimension reduction processing.
Then, initializing the array after dimension reduction, and calculating probability distribution between each data point and other points in the initialized matrix after dimension reduction; And calculates the probability distribution/>, between each data point and other points in the distributed probability array; The obtained/>And/>Substituting the lattice similarity probability distribution function to calculate the similarity divergence between the distribution probability array and the array after dimension reduction. Then, the array after dimension reduction is optimized and adjusted to ensure that/>Approaching 0,/>The value is minimum, even if the array after dimension reduction is highly consistent with the distribution probability array in distribution, the internal data distribution and similarity are kept, so that an optimized dimension reduction array is obtained, the divergence of the first dimension reduction similarity is obtained and is used as a stage dimension reduction result, and the array after dimension reduction, which corresponds to the stage dimension reduction result, is used as a dimension reduction data array, so that the efficiency of obtaining the data matching point set is improved.
And accessing a digital archive database for storing past engineering information of the target building engineering to obtain a historical building information archive, and searching and inquiring all BIM model data from design to operation of the building to obtain BIM data of the target building engineering, wherein the BIM data covers all information models such as door and window structures, pipeline hydropower, equipment configuration and the like of the target building. Then, various parameters in BIM data of the target building engineering are read, statistical features and distribution features are extracted for each type of parameters in sequence, for example, parameters of doors and windows comprise shapes, sizes, materials, position configurations and the like, and feature vectors capable of representing rules of the parameters are selected. After the traversal is completed, a plurality of key feature vectors such as parameter types, numerical intervals, distribution modes and the like of BIM data are formed. And then traversing a plurality of key feature vectors based on a distance calculation mode of vector angle or parameter correlation, and calculating the distance distribution between each key feature vector and each data point in the data reduction array one by one to obtain the matching degree or similarity of each key feature vector and the data points in the data reduction array. And then, according to threshold filtering, outputting the most matched data as matching points, thereby obtaining a data matching point set and providing decision basis for making management strategies.
Performing analysis management on the BIM data by combining the data analysis tool to the data matching point set to generate a management strategy;
further, the embodiment of the application further comprises:
Importing the data matching point set into the data analysis tool for data preprocessing, and performing feature visualization on the BIM data according to a preprocessing result to generate visualized data;
Building a three-dimensional data model of the target building engineering based on the visual data, analyzing the BIM data according to a preset feasible threshold of the target building engineering through the three-dimensional data model, and generating BIM data with feasibility;
And formulating the management strategy according to the BIM data with feasibility.
In a possible implementation manner, after the data matching point set is obtained, the data matching point set is imported into a data analysis tool, and the data analysis tool performs preprocessing on the data matching point set, including cleaning, denoising, sampling and the like, so that the quality of the data matching point set is improved, and a preprocessing result is obtained. Then, the data analysis tool expresses key data characteristics in BIM data by using a visualization technical means, wherein the key data characteristics comprise visual display of shape parameters, material composition, position distribution and the like, visually represents data characteristics, and forms visualized data with rich and complete characteristics. And then, under the support of visual data, the coordinates, geometry and topology information of BIM data are considered, the three-dimensional configuration of the target building engineering is formed by assembling, and the three-dimensional data model is obtained by realizing fine rendering with the assistance of materials and performance parameters. Meanwhile, based on the use expectation of the target building, defining the feasible threshold of various indexes to obtain a preset feasible threshold, automatically checking and comparing BIM model parameter values in the three-dimensional data model, and outputting BIM data results conforming to the preset feasible threshold, namely BIM data with feasibility. And then, analyzing installation and use nodes of components and equipment corresponding to BIM data with feasibility in an engineering progress plan to obtain a management strategy, wherein the BIM data processing and application schemes comprise BIM data processing and application schemes of different time periods, different building areas and equipment.
And managing the BIM data of the target building engineering through the management strategy based on the expected engineering progress of the target building engineering.
In the embodiment of the application, first, the expected project progress of the target building project is decomposed, and the expected work key points and progress nodes of different stages are determined. And then, analyzing the key engineering areas, the important equipment and the corresponding data management schemes of each node by comparing with the management strategy. And then, a specific BIM data management implementation plan of the current stage is formulated in combination with the progress requirement, and policy execution of key areas and matters is preferentially arranged, so that response to the expected engineering progress requirement is realized, and BIM data management is realized.
In summary, the BIM data management method based on the construction engineering provided by the embodiment of the application has the following technical effects:
And carrying out data point acquisition on a plurality of data sources in the target building engineering through the multidimensional sensing equipment group, generating a building engineering data set, acquiring multi-source heterogeneous data of the building engineering, and laying a data foundation for subsequent analysis and management. And generating a plurality of building engineering data classes by carrying out cluster analysis on the historical building engineering data of the target building engineering, and providing support for classified archiving of the building engineering data sets. Classifying and archiving the construction engineering data sets according to the plurality of construction engineering data types, and carrying out cooperative arrangement based on classification archiving results to generate a data cooperative array so as to realize unified integration of heterogeneous data. And performing dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set, so as to provide support for generating a management strategy. And carrying out analysis management on BIM data on the data matching point set by combining a data analysis tool, generating a management strategy, and providing the management strategy for realizing intelligent management of the BIM data. Based on the expected engineering progress of the target building engineering, BIM data is managed on the target building engineering through a management strategy, the whole process from collection to management is completed, and engineering-oriented BIM data integrated intelligent dynamic management is realized.
Example two
Based on the same inventive concept as the building engineering-based BIM data management method in the foregoing embodiments, as shown in fig. 3, an embodiment of the present application provides a building engineering-based BIM data management system, which is communicatively connected with a multidimensional sensing device group and a data analysis tool, and includes:
The data point acquisition module 11 is used for carrying out data point acquisition on a plurality of data sources in the target building engineering through the multi-dimensional sensing equipment group to generate a building engineering data set;
A data cluster analysis module 12 for generating a plurality of construction engineering data classes by performing cluster analysis on the historical construction engineering data of the target construction engineering;
The data collaborative arrangement module 13 is configured to generate a data collaborative array, where the data collaborative array is obtained by classifying and archiving the construction engineering data set according to the plurality of construction engineering data types, and performing collaborative arrangement based on a classified archiving result;
the data array matching module 14 is used for performing dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set;
The management policy generation module 15 is configured to perform analysis management of the BIM data on the data matching point set in combination with the data analysis tool, and generate a management policy;
and the BIM data management module 16 is used for managing the BIM data of the target building engineering through the management strategy based on the expected engineering progress of the target building engineering.
Further, the data point acquisition module 11 includes the following steps:
Extracting a plurality of data features according to engineering expectations of a target building engineering;
determining a plurality of key indexes of the target building engineering according to the plurality of data characteristics;
The plurality of data sources is determined in accordance with the plurality of key indicators based on the multi-dimensional sensing device group.
Further, the data collaboration arrangement module 13 includes the following steps:
creating a data archive directory structure according to the plurality of building engineering data classes;
Based on the data archiving directory structure, carrying out data storage on the construction engineering data set to obtain a classified archiving result;
And cooperatively arranging the storage positions of the classified filing results to generate the data cooperative array.
Further, the data array matching module 14 includes the following steps:
Calculating and acquiring the distribution probability of a plurality of cooperative data and preset cooperative data in the data cooperative array in a preset conflict interval to acquire a distribution probability array;
Constructing a lattice similarity probability distribution function, performing dimension reduction processing on lattice distribution of the distribution probability array, and generating a first dimension reduction similarity divergence of the distribution probability array as a stage dimension reduction result;
and constructing the dimension reduction data array based on the stage dimension reduction result.
Further, the data array matching module 14 further includes the following steps:
The lattice similarity probability distribution function is as follows:
；
Wherein, To characterize the measure of the proximity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array, let/>Performing dimension reduction processing by taking 0 as constraint condition,/>To characterize the divergence of the similarity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array,/>For lattice/>, in reduced dimension lattice distribution based on the distributed probability arrayAnd lattice/>Similarity probability of/>For dot matrix/>, in a dot matrix distribution based on the distributed probability arrayAnd lattice/>The similarity probability of (i, j) is any coordinate point in the data cooperative array, the value range of i is 0< i < n, the value range of j is 0< j < n, and n is the total number of coordinate points in the data cooperative array.
Further, the data array matching module 14 further includes the following steps:
accessing a historical building information file of the target building engineering, and extracting the BIM data of the target building engineering;
Traversing the BIM data of the target building engineering to determine a plurality of key feature vectors;
and matching the plurality of key feature vectors with the reduced-dimension data array by using a distance measure to obtain the data matching point set.
Further, the management policy generation module 15 includes the following execution steps:
Importing the data matching point set into the data analysis tool for data preprocessing, and performing feature visualization on the BIM data according to a preprocessing result to generate visualized data;
Building a three-dimensional data model of the target building engineering based on the visual data, analyzing the BIM data according to a preset feasible threshold of the target building engineering through the three-dimensional data model, and generating BIM data with feasibility;
And formulating the management strategy according to the BIM data with feasibility.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the application and its equivalents.
Claims (7)
1. A building engineering-based BIM data management method, wherein the method is applied to a building engineering-based BIM data management system, and the building engineering-based BIM data management system is in communication connection with a multidimensional sensing device group and a data analysis tool, and the method comprises the following steps:
Carrying out data point acquisition on a plurality of data sources in a target building project through the multidimensional sensing equipment group to generate a building project data set;
generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of a target building engineering;
generating a data collaborative array, wherein the data collaborative array is obtained by classifying and archiving the building engineering data set according to the plurality of building engineering data types and carrying out collaborative arrangement based on classified archiving results;
performing dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set;
Performing analysis management on the BIM data by combining the data analysis tool to the data matching point set to generate a management strategy;
Based on the expected project progress of the target building project, managing the BIM data of the target building project through the management strategy;
the BIM data analysis and management are carried out on the data matching point set by combining the data analysis tool, and a management strategy is generated, wherein the method comprises the following steps:
Importing the data matching point set into the data analysis tool for data preprocessing, and performing feature visualization on the BIM data according to a preprocessing result to generate visualized data;
Building a three-dimensional data model of the target building engineering based on the visual data, analyzing the BIM data according to a preset feasible threshold of the target building engineering through the three-dimensional data model, and generating BIM data with feasibility;
And formulating the management strategy according to the BIM data with feasibility.
2. The method of claim 1, wherein the data point collection is performed on a plurality of data sources within the target construction project by the multi-dimensional sensing device group to generate a construction project data set, the method comprising:
Extracting a plurality of data features according to engineering expectations of a target building engineering;
determining a plurality of key indexes of the target building engineering according to the plurality of data characteristics;
The plurality of data sources is determined in accordance with the plurality of key indicators based on the multi-dimensional sensing device group.
3. The method of claim 1, wherein the collaborative array of data is obtained by sorting the set of construction data according to the plurality of classes of construction data, and collaborative arrangement based on sorting results of the sorting, the method comprising:
creating a data archive directory structure according to the plurality of building engineering data classes;
Based on the data archiving directory structure, carrying out data storage on the construction engineering data set to obtain a classified archiving result;
And cooperatively arranging the storage positions of the classified filing results to generate the data cooperative array.
4. The method of claim 1, wherein the dimension reduction processing is performed based on the data collaboration array to obtain a dimension reduction data array, the method comprising:
Calculating and acquiring the distribution probability of a plurality of cooperative data and preset cooperative data in the data cooperative array in a preset conflict interval to acquire a distribution probability array;
Constructing a lattice similarity probability distribution function, performing dimension reduction processing on lattice distribution of the distribution probability array, and generating a first dimension reduction similarity divergence of the distribution probability array as a stage dimension reduction result;
and constructing the dimension reduction data array based on the stage dimension reduction result.
5. The method of claim 4, wherein the lattice similarity probability distribution function is as follows:
；
Wherein, To characterize the measure of the proximity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array, let/>Performing dimension reduction processing by taking 0 as constraint condition,/>To characterize the divergence of the similarity between the lattice distribution of the distribution probability array and the dimension-reduced lattice distribution of the distribution probability array,/>For lattice/>, in reduced dimension lattice distribution based on the distributed probability arrayAnd lattice/>Similarity probability of/>For dot matrix/>, in a dot matrix distribution based on the distributed probability arrayAnd lattice/>The similarity probability of (i, j) is any coordinate point in the data cooperative array, the value range of i is 0< i < n, the value range of j is 0< j < n, and n is the total number of coordinate points in the data cooperative array.
6. The method of claim 1, wherein traversing the BIM data of the target building project matches the reduced data array to obtain a set of data matching points, the method comprising:
accessing a historical building information file of the target building engineering, and extracting the BIM data of the target building engineering;
Traversing the BIM data of the target building engineering to determine a plurality of key feature vectors;
and matching the plurality of key feature vectors with the reduced-dimension data array by using a distance measure to obtain the data matching point set.
7. A building engineering-based BIM data management system for implementing the building engineering-based BIM data management method of any one of claims 1 to 6, the building engineering-based BIM data management system being communicatively connected to a multidimensional sensing device set and a data analysis tool, the system comprising:
the data point acquisition module is used for carrying out data point acquisition on a plurality of data sources in the target building engineering through the multi-dimensional sensing equipment group to generate a building engineering data set;
The data cluster analysis module is used for generating a plurality of building engineering data classes by carrying out cluster analysis on historical building engineering data of a target building engineering;
The data collaborative arrangement module is used for generating a data collaborative array, and the data collaborative array is obtained by classifying and archiving the building engineering data set according to the plurality of building engineering data types and carrying out collaborative arrangement based on a classified archiving result;
The data array matching module is used for carrying out dimension reduction processing based on the data collaborative array to obtain a dimension reduction data array, and traversing BIM data of the target building engineering to match with the dimension reduction data array to obtain a data matching point set;
The management policy generation module is used for carrying out analysis management on the BIM data on the data matching point set by combining the data analysis tool to generate a management policy;
The BIM data management module is used for managing the BIM data of the target building engineering through the management strategy based on the expected engineering progress of the target building engineering;
the management policy generation module comprises the following execution steps:
Importing the data matching point set into the data analysis tool for data preprocessing, and performing feature visualization on the BIM data according to a preprocessing result to generate visualized data;
Building a three-dimensional data model of the target building engineering based on the visual data, analyzing the BIM data according to a preset feasible threshold of the target building engineering through the three-dimensional data model, and generating BIM data with feasibility;
And formulating the management strategy according to the BIM data with feasibility.
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