CN115146883B - Management and control method and system for intelligent construction of building engineering - Google Patents

Management and control method and system for intelligent construction of building engineering Download PDF

Info

Publication number
CN115146883B
CN115146883B CN202211082006.9A CN202211082006A CN115146883B CN 115146883 B CN115146883 B CN 115146883B CN 202211082006 A CN202211082006 A CN 202211082006A CN 115146883 B CN115146883 B CN 115146883B
Authority
CN
China
Prior art keywords
model
construction
relevance
building
digital twin
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
Application number
CN202211082006.9A
Other languages
Chinese (zh)
Other versions
CN115146883A (en
Inventor
江书峣
赵兴辉
闵元
袁野
胡春
左傲
李贺
周玉兵
吴荣会
杨发凯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Construction Second Engineering Bureau Co Ltd
Original Assignee
China Construction Second Engineering Bureau Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by China Construction Second Engineering Bureau Co Ltd filed Critical China Construction Second Engineering Bureau Co Ltd
Priority to CN202211082006.9A priority Critical patent/CN115146883B/en
Publication of CN115146883A publication Critical patent/CN115146883A/en
Application granted granted Critical
Publication of CN115146883B publication Critical patent/CN115146883B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models

Abstract

The invention discloses a control method for intelligent construction of building engineering, which comprises the following steps: s1, acquiring real-time building image data, and constructing a three-dimensional building model based on a real scene; s2, constructing a self-adaptive digital twin model based on the three-dimensional building model; s3, monitoring and predicting a construction site by utilizing the interactivity of the digital twin model; and S4, managing and controlling the construction site according to the preset construction progress and tasks. The invention also discloses a management and control system for the intelligent construction of the building engineering. According to the invention, a high-precision digital twin model fusing the construction engineering data is constructed by adopting a ground-space combined high-precision three-dimensional data and scene fusion technology, so that full connection between the construction engineering and an Internet database is realized, the real-time construction data of a construction site and the three-dimensional scene model are comprehensively associated, and an intelligent construction system capable of realizing remote intelligent supervision, production multi-dimensional data monitoring and engineering task allocation scheduling is formed.

Description

Management and control method and system for intelligent construction of building engineering
Technical Field
The invention relates to the technical field of constructional engineering, in particular to a management and control method and a management and control system for intelligent construction of constructional engineering.
Background
With the development of scientific technology and the trend of new and old kinetic energy conversion, the traditional construction industry faces the examination of transformation and upgrading, the traditional construction and management mode cannot meet the current large-scale construction project, and the problems existing in the construction process can be effectively solved through a more scientific and intelligent management mode.
The intelligent construction is a new stage of the information development of modern construction engineering technology, and is to enhance information management and service by using the technology of the Internet of things and the equipment monitoring technology on the basis of digital construction engineering; the controllability of the building process is improved, and a high-efficiency, energy-saving, environment-friendly and environment-comfortable humanized building environment is constructed by combining green intelligent means, intelligent systems and other emerging technologies.
In the prior art, most of constructed digital management platforms mainly manage single sites such as construction sites or delivery sites, cannot form remote and unified comprehensive construction management and control, only manage a certain scene or a certain task, are difficult to realize data interaction among different engineering projects, and do not accord with a green and efficient intelligent construction concept.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
The present invention provides a method and a system for managing and controlling intelligent building of architectural engineering, which are directed to the problems in the related art, so as to overcome the technical problems in the related art.
Therefore, the invention adopts the following specific technical scheme:
according to one aspect of the invention, a management and control method for intelligent building of construction engineering is provided, which comprises the following steps:
s1, acquiring real-time building image data, and constructing a three-dimensional building model based on a real scene;
s2, constructing a self-adaptive digital twin model based on the three-dimensional building model;
s3, monitoring and predicting a construction site by utilizing the interactivity of the digital twin model;
and S4, managing and controlling the construction site according to the preset construction progress and tasks.
Further, the acquiring of real-time building image data and the construction of a three-dimensional building model based on a real scene comprise the following steps:
s11, acquiring color image data and space coordinate information of a target building by using an unmanned aerial vehicle image technology and a three-dimensional laser scanning technology;
s12, resolving laser point cloud data by using a PCL point cloud base, and performing a point cloud reconstruction model on the unmanned aerial vehicle image to form a three-dimensional topographic map;
and S13, fusing the three-dimensional topographic map with a pre-designed BIM model to construct a three-dimensional building model.
Further, the method for constructing the adaptive digital twin model based on the three-dimensional building model comprises the following steps:
s21, constructing a basic digital model by associating and fusing building construction data by taking the three-dimensional building model as a carrier;
s22, monitoring a construction site in real time, periodically migrating and synchronizing construction information, and realizing periodic updating of the building construction data;
s23, merging the updated building construction data into the basic digital model to obtain a reconstructed updated self-adaptive digital twin model;
and S24, calculating the association degree between the digital twin model and the actual building engineering based on multiple dimensions, and maintaining to obtain the digital twin model with the association degree meeting the construction requirement.
Further, the building construction data comprises construction progress information, a construction process, three-dimensional building image data, meteorological data, temperature and humidity of a construction site, electromechanical data and constructor management information;
the digital twin model comprises a data storage operation, a quality prediction model and a model base, and the quality prediction model comprises a mechanism model and an algorithm model.
Further, the step of merging the updated building construction data into the basic digital model to obtain a reconstructed updated adaptive digital twin model includes the following steps:
s231, the basic digital model preferentially performs self-updating of the mechanism model according to updated building construction data;
s232, matching a source model to be reconstructed from the algorithm model;
and S233, reconstructing and updating the digital model according to construction change analysis and corresponding updating strategies of the building engineering to obtain the self-adaptive digital twin model.
Further, the method comprises the following steps of calculating the association degree between the digital twin model and the actual building engineering based on multiple dimensions, and maintaining to obtain the digital twin model with the association degree meeting the requirement, wherein the association degree comprises the following steps:
s241, determining a plurality of correlation indexes corresponding to all dimensions;
s242, determining and dividing the relevance grade between the digital twin model and the construction project, and taking the highest grade of the relevance grade as a safety grade;
s243, calculating the correlation index to obtain the correlation degree between the digital twin model and the construction engineering;
s244, if the relevance level meets the safety level, judging that the digital twin model meets the construction requirement, and using the digital twin model as a final digital twin model;
and S245, if the relevance degree grade does not meet the safety grade, updating and adding corresponding relevance indexes to update and maintain the digital twin model until the relevance degree meets the safety grade.
Further, the multiple dimensions comprise five dimensions of a building entity, a mathematical model, model parameters, connection interaction and utility value.
Further, the operation of the correlation index to obtain the correlation degree between the digital twin model and the construction engineering includes the following operation steps:
Figure 100002_DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 133206DEST_PATH_IMAGE002
a relevance value representing each relevance index;
Figure 253608DEST_PATH_IMAGE003
representing a single correlation index;
Figure 298925DEST_PATH_IMAGE004
the highest level of the relevance degree can be reached by a single relevance index;
Figure 162976DEST_PATH_IMAGE005
represents a single correlation indexiThe relevance grade is used for improving the weight of the relevance grade of 1 grade in the relevance index, and
Figure 180610DEST_PATH_IMAGE006
；
Figure 268652DEST_PATH_IMAGE007
the highest level of relevance that can be achieved by a single relevance index
Figure 988215DEST_PATH_IMAGE004
To increase to
Figure 655957DEST_PATH_IMAGE008
As required toiWhether the degree of correlation is reached or not, and when the degree of correlation is reached
Figure 590415DEST_PATH_IMAGE009
When it is not reached
Figure 787041DEST_PATH_IMAGE010
；
Figure 10212DEST_PATH_IMAGE011
Representing a relevance value for each dimension;
Figure 12803DEST_PATH_IMAGE012
representing a single dimension;
Figure 988718DEST_PATH_IMAGE013
representing the highest level of achievable relevance for a single dimension;
Figure 887404DEST_PATH_IMAGE014
representing a single dimensioniThe relevance grade of each relevance index is used for improving the weight of the relevance grade of the 1 level in the dimension, and
Figure 394609DEST_PATH_IMAGE015
；
Figure 872994DEST_PATH_IMAGE016
the first step of representing the degree of association grade up grade which can be achieved by all the association indexes in a single dimensioniWhether the relevance grade of each relevance index is reached or not, and when the relevance grade is reached
Figure 782045DEST_PATH_IMAGE017
When it is not reached
Figure 586053DEST_PATH_IMAGE018
；
Figure 767504DEST_PATH_IMAGE019
Representing the finally calculated relevance value;
Figure 111898DEST_PATH_IMAGE020
representing a current relevance grade;
Figure 813137DEST_PATH_IMAGE021
representing a single dimensioniThe relevance grade of each relevance index is weighted to promote the relevance grade of level 1, and
Figure 53626DEST_PATH_IMAGE022
；
Figure 269844DEST_PATH_IMAGE023
indicating the number of steps required to raise the current relevance level by one stepiWhether the degree of correlation of each correlation index is reached or not, and when the degree of correlation is reached
Figure 355611DEST_PATH_IMAGE024
When it is not reached
Figure 426204DEST_PATH_IMAGE025
；
iA number indicating a correlation index.
According to another aspect of the invention, a management and control system for building engineering intelligent construction comprises the following modules:
the monitoring module is used for monitoring the construction site in real time to obtain engineering image data and building construction data;
the digital twin technical module is used for constructing a digital twin model and carrying out self-adaptive updating, relevance calculation and optimization on the digital twin model;
the supervision and scheduling module is used for carrying out centralized supervision on the construction site of the building engineering and distributing scheduling tasks of all levels;
and the field construction module is used for carrying out actual construction operation on a construction field and completing construction tasks.
Furthermore, the monitoring module comprises a video monitoring camera, a strain displacement monitoring sensor for engineering and a temperature and humidity sensor;
the supervision and scheduling module comprises a multilayer structure which is an enterprise level management and control unit, a project level management and control unit and a production level management and control unit respectively.
The invention has the beneficial effects that: by adopting a high-precision three-dimensional data and scene fusion technology combined with the ground, a standard three-dimensional building model is constructed, and a high-precision digital twin model fusing building engineering data is constructed on the basis, so that full connection between the building engineering and an internet database can be realized, that is, the real-time engineering data of a construction site and the three-dimensional scene model are comprehensively associated, thereby forming an intelligent construction system capable of realizing remote intelligent supervision, production multi-dimensional data monitoring and engineering task distribution scheduling, and greatly improving the construction efficiency and the engineering precision of the actual building engineering; in addition, the self-adaptive digital twin model can be automatically reconstructed and updated in time according to construction site feedback data to ensure full connectivity with the construction project, and a correlation evaluation system is constructed to evaluate and calculate the model by adopting multiple correlation indexes, so that high correlation between the digital twin model and the actual construction project can be effectively ensured, and the potential safety hazard of the project due to difference is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method for managing and controlling intelligent construction of a construction project according to the present invention;
fig. 2 is a system block diagram of a management and control system for building engineering intelligent construction according to the present invention.
In the figure:
1. a monitoring module; 2. a digital twinning technology module; 3. a supervision and scheduling module; 4. and (5) a field construction module.
Detailed Description
According to an embodiment of the present invention, there is provided a method for managing and controlling intelligent building of a building engineering, as shown in fig. 1, the method includes the following steps:
s1, acquiring real-time building image data, and constructing a three-dimensional building model based on a real scene, wherein the method comprises the following steps:
s11, acquiring color image data and spatial coordinate information of a target building by using an unmanned aerial vehicle image technology and a three-dimensional laser scanning technology;
s12, resolving laser point cloud data by using a PCL point cloud base, and performing a point cloud reconstruction model on the unmanned aerial vehicle image to form a three-dimensional topographic map;
and S13, fusing the three-dimensional topographic map with a pre-designed BIM model to construct a three-dimensional building model.
S2, constructing an adaptive digital twin model based on the three-dimensional building model,
the digital twin model simulates the behavior and performance of the self in a real environment in real time by constructing a virtual model which completely corresponds and accords with physical entities in the real world.
The step S2 includes the steps of:
s21, constructing a basic digital model by associating and fusing building construction data by taking the three-dimensional building model as a carrier;
s22, monitoring a construction site in real time, periodically migrating and synchronizing construction information, and realizing periodic updating of the building construction data;
s23, integrating the updated building construction data into the basic digital twin model to obtain a reconstructed updated self-adaptive digital twin model;
the construction data comprises construction progress information, construction technology, three-dimensional building image data, meteorological data, construction site temperature and humidity, electromechanical data, constructor management information and the like, and basically covers factors influencing the construction engineering quality in the whole construction process of the construction engineering, so that a comprehensive analysis and supervision digital twin model is constructed.
The digital twin model comprises a data storage operation, a quality prediction model and a model base, and the quality prediction model comprises a mechanism model and an algorithm model.
Wherein, step S23 includes the steps of:
s231, the basic digital model preferentially carries out self-updating of the mechanism model according to the updated building construction data;
s232, matching a source model to be reconstructed from the algorithm model;
and S233, reconstructing and updating the digital model according to construction change analysis and corresponding updating strategies of the building engineering to obtain the self-adaptive digital twin model.
In the operation principle of the quality prediction model, the mechanism model extracts the primary characteristics of the updated building construction data, and the algorithm model adopts a deep learning method to carry out deep characteristic extraction and quality prediction on the building construction data. Therefore, in the quality prediction model, the digital twin model is predicted by fusing the mechanism model and the algorithm model, namely when the conditions in the model change, the source model to be reconstructed and updated is obtained from the algorithm model base through characteristic data operation in a matching manner and is subjected to self-adaptive updating, and the data generated by the reconstructed and updated model is stored in the database again to serve as historical data to provide a basis for subsequent model reconstruction and update.
S24, calculating the relevance between the digital twin model and the actual construction project based on multiple dimensions, and maintaining to obtain the digital twin model with the relevance meeting the construction requirement, wherein the method comprises the following steps:
s241, determining a plurality of correlation indexes corresponding to all dimensions;
the multi-dimension comprises five dimensions of a building entity, a mathematical model, model parameters, connection interaction and utility value.
Each dimension has its own associated index for reflecting the characteristics of each dimension. The building entity dimension provides data and foundation related to an actual building body model, so that the dimension has relevance indexes in the aspects of monitoring sensors, data interfaces, control equipment and the like;
the mathematical model dimension shows the description of the building entity through a data technology to realize a high simulation function, so that the dimension adopts multi-angle correlation indexes such as effectiveness, intuition, connectivity, flexibility and the like to show the correlation degree of the model;
the model parameter dimensionality comprises correlation indexes such as compatibility, accessibility and quality, and is used for carrying out parameter embodiment on the digital twin model and further describing the influence of the model on the correlation degree on the aspect of actual parameters;
the connection interaction dimension comprises relevant correlation indexes such as connection modes, connection objects and connection time delay;
the utility value dimension comprises related indexes such as diversity, integration degree and the like.
S242, determining and dividing the relevance grade between the digital twin model and the construction project, and taking the highest grade of the relevance grade as a safety grade;
in the invention, the relevance grade is divided into five grades which are respectively unqualified, qualified, general, good and safe from low to high, and four grades except the safety grade are defined as unsafe grades, if the result is lower than the safety grade in the calculation process, the unqualified relevance indexes are searched and positioned according to the relevance in the calculation process, and the safety grade in the aspect is improved by changing relevant parameter data, namely the model is maintained and updated.
S243, calculating the correlation index to obtain the correlation degree between the digital twin model and the construction engineering, and the method comprises the following calculation steps:
Figure 634332DEST_PATH_IMAGE026
in the formula (I), the compound is shown in the specification,
Figure 275529DEST_PATH_IMAGE002
a relevance value representing each relevance index;
Figure 961725DEST_PATH_IMAGE003
representing a single correlation index;
Figure 903136DEST_PATH_IMAGE004
the highest level of the relevance degree can be reached by a single relevance index;
Figure 219848DEST_PATH_IMAGE005
represents a single correlation indexiThe relevance grade is used for improving the weight of the relevance grade of 1 grade in the relevance index, and
Figure 410658DEST_PATH_IMAGE006
；
Figure 353075DEST_PATH_IMAGE007
the highest level of relevance that can be achieved by a single relevance index
Figure 617834DEST_PATH_IMAGE004
To increase to
Figure DEST_PATH_IMAGE027
As required toiWhether or not the level of the degree of association is reached, and when the level of the degree of association is reached
Figure 636606DEST_PATH_IMAGE009
When it is not reached
Figure 252395DEST_PATH_IMAGE010
；
Figure 545973DEST_PATH_IMAGE011
Representing a relevance value for each dimension;
Figure 469102DEST_PATH_IMAGE012
representing a single dimension;
Figure 862038DEST_PATH_IMAGE013
representing the highest level of achievable relevance for a single dimension;
Figure 27440DEST_PATH_IMAGE014
representing a single dimensioniThe relevance grade of each relevance index is used for improving the weight of the relevance grade of 1 level in the dimension, and
Figure 62392DEST_PATH_IMAGE015
；
Figure 567322DEST_PATH_IMAGE016
the first step of representing the level of the relevance grade which can be reached by all the relevance indexes in a single dimension and is required to be improved by one stepiWhether the relevance grade of each relevance index is reached or not, and when the relevance grade is reached
Figure 193476DEST_PATH_IMAGE017
If not, the time is up;
Figure 33125DEST_PATH_IMAGE019
representing the finally calculated relevance value;
Figure 871768DEST_PATH_IMAGE020
representing a current relevance grade;
Figure 27943DEST_PATH_IMAGE021
representing a single dimensioniThe relevance grade of each relevance index is weighted to promote the relevance grade of grade 1, and
Figure 28260DEST_PATH_IMAGE022
；
Figure 105937DEST_PATH_IMAGE023
indicating the number of steps required to raise the current relevance level by one stepiWhether the degree of correlation of each correlation index is reached or not, and when the degree of correlation is reached
Figure 545009DEST_PATH_IMAGE024
When it is not reached
Figure 8220DEST_PATH_IMAGE025
；
iA number indicating a correlation index.
S244, if the relevance level meets the safety level, judging that the digital twin model meets the construction requirement, and using the digital twin model as a final digital twin model;
and S245, if the relevance degree grade does not meet the safety grade, updating and adding corresponding relevance indexes to update and maintain the digital twin model until the relevance degree meets the safety grade.
S3, monitoring and predicting a construction site by utilizing the interactivity of the digital twin model;
the interactivity mainly refers to real-time dynamic interaction which can be realized between the virtual three-dimensional model and the building entity object thereof. In the process, the internet of things is used as a core technology for interaction between the virtual and the real. The virtual digital twin model realizes the functions of prediction and optimization through the digitalized function of the model, and then predicts and intervenes the building entity according to the optimization result, namely, transmits the next step instruction or the reference direction to the real world. Similarly, the building working conditions and real-time states of building entities in the real world need to be transmitted to the digital world in time, and finally, intelligent construction of building engineering and accurate prediction and optimization of a construction site are realized through the interaction of the building working conditions and the real-time states.
And S4, managing and controlling the construction site according to the preset construction progress and tasks.
According to another embodiment of the present invention, as shown in fig. 2, there is also provided a management and control system for building engineering intelligent construction, which includes the following modules:
the monitoring module 1 is used for monitoring a construction site in real time to obtain engineering image data and building construction data;
the digital twin technical module 2 is used for constructing a digital twin model and carrying out self-adaptive updating, relevance calculation and optimization on the digital twin model;
the supervision and scheduling module 3 is used for carrying out centralized supervision on the construction site of the building engineering and distributing scheduling tasks of various levels;
and the field construction module 4 is used for carrying out actual construction operation on a construction field and completing construction tasks.
The monitoring module 1 comprises a video monitoring camera, a strain displacement monitoring sensor for engineering and a temperature and humidity sensor;
the supervision and scheduling module 3 comprises a multilayer structure which is an enterprise level management and control unit, a project level management and control unit and a production level management and control unit respectively.
The enterprise-level management and control unit is used for managing and controlling overall macroscopic production progress, production value, safety early warning information and the like; the project level control unit controls specific projects, including meteorological conditions, production output values, material consumption, personnel distribution and the like; the production-level management and control unit is responsible for managing and controlling the multidimensional management of the process, the progress, the quality and the like of a construction site, and the management and control are carried out on the basis of four aspects of the construction process, project management, basic information, sensor monitoring and the like.
In conclusion, by means of the technical scheme, a standard three-dimensional building model is constructed by adopting a ground-air combined high-precision three-dimensional data and scene fusion technology, and a high-precision digital twin model fusing building engineering data is constructed on the basis, so that full connection between the building engineering and an internet database can be realized, namely, real-time engineering data of a construction site is comprehensively associated with the three-dimensional scene model, and thus, an intelligent building system capable of realizing remote intelligent supervision, production multi-dimensional data monitoring and engineering task allocation scheduling is formed, and the construction efficiency and the engineering precision of the actual building engineering are greatly improved; in addition, the self-adaptive digital twin model can be automatically reconstructed and updated in time according to construction site feedback data to ensure full connectivity with the construction project, and a correlation evaluation system is constructed to evaluate and calculate the model by adopting multiple correlation indexes, so that high correlation between the digital twin model and the actual construction project can be effectively ensured, and the potential safety hazard of the project due to difference is avoided.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A control method for intelligent construction of building engineering is characterized by comprising the following steps:
s1, acquiring real-time building image data, and constructing a three-dimensional building model based on a real scene;
s2, constructing a self-adaptive digital twin model based on the three-dimensional building model;
s3, monitoring and predicting a construction site by utilizing the interactivity of the digital twin model;
s4, managing and controlling a construction site according to a preset construction progress and task;
the method for constructing the self-adaptive digital twin model based on the three-dimensional building model comprises the following steps:
s21, constructing a basic digital model by associating and fusing building construction data by taking the three-dimensional building model as a carrier;
s22, monitoring a construction site in real time, periodically migrating and synchronizing construction information, and realizing periodic updating of the building construction data;
s23, integrating the updated building construction data into the basic digital model to obtain a reconstructed and updated self-adaptive digital twin model;
s24, calculating the association degree between the digital twin model and the actual building engineering based on multiple dimensions, and maintaining to obtain the digital twin model with the association degree meeting the construction requirement;
the building construction data comprises construction progress information, construction technology, three-dimensional building image data, meteorological data, construction site temperature and humidity, electromechanical data and constructor management information;
the digital twin model comprises a data storage operation, a quality prediction model and a model library, and the quality prediction model comprises a mechanism model and an algorithm model;
the method for integrating the updated building construction data into the basic digital model to obtain the reconstructed updated self-adaptive digital twin model comprises the following steps of:
s231, the basic digital model preferentially performs self-updating of the mechanism model according to updated building construction data;
s232, matching a source model to be reconstructed from the algorithm model;
s233, reconstructing and updating the digital model according to construction change analysis and corresponding updating strategy of the building engineering to obtain a self-adaptive digital twin model;
the method comprises the following steps of calculating the association degree between the digital twin model and the actual building engineering based on multiple dimensions, and maintaining to obtain the digital twin model with the association degree meeting the requirements, wherein the method comprises the following steps:
s241, determining a plurality of correlation indexes corresponding to all dimensions;
s242, determining and dividing the relevance grade between the digital twin model and the construction project, and taking the highest grade of the relevance grade as a safety grade;
s243, calculating the correlation index to obtain the correlation degree between the digital twin model and the construction engineering;
s244, if the relevance level meets the safety level, judging that the digital twin model meets the construction requirement, and using the digital twin model as a final digital twin model;
and S245, if the association degree grade does not meet the safety grade, updating and adding corresponding association indexes to update and maintain the digital twin model until the association degree grade meets the safety grade.
2. The method as claimed in claim 1, wherein the step of obtaining real-time building image data to construct a three-dimensional building model based on real scenes comprises the steps of:
s11, acquiring color image data and spatial coordinate information of a target building by using an unmanned aerial vehicle image technology and a three-dimensional laser scanning technology;
s12, resolving laser point cloud data by using a PCL point cloud base, and performing a point cloud reconstruction model on the unmanned aerial vehicle image to form a three-dimensional topographic map;
and S13, fusing the three-dimensional topographic map with a pre-designed BIM model to construct a three-dimensional building model.
3. The method as claimed in claim 1, wherein the multiple dimensions include five dimensions of building entity, mathematical model, model parameters, connection interaction and utility value.
4. The method as claimed in claim 3, wherein the calculating the correlation index to obtain the correlation between the digital twin model and the construction project includes the following steps:
Figure DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE002
a correlation value representing each correlation index;
Figure DEST_PATH_IMAGE003
representing a single correlation index;
Figure DEST_PATH_IMAGE004
the highest level of the relevance degree can be reached by a single relevance index;
Figure DEST_PATH_IMAGE005
represents a single correlation indexiThe relevance grade is used for improving the weight of the relevance grade of the 1 grade in the relevance index, and
Figure DEST_PATH_IMAGE006
；
Figure DEST_PATH_IMAGE007
the highest level of relevance that can be achieved by a single relevance index
Figure DEST_PATH_IMAGE008
To increase to
Figure DEST_PATH_IMAGE009
As required toiWhether the degree of correlation is reached or not, and when the degree of correlation is reached
Figure DEST_PATH_IMAGE010
When it is not reached
Figure DEST_PATH_IMAGE011
；
Figure DEST_PATH_IMAGE012
Representing a relevance value for each dimension;
Figure DEST_PATH_IMAGE013
representing a single dimension;
Figure DEST_PATH_IMAGE014
representing the highest level of achievable relevance for a single dimension;
Figure DEST_PATH_IMAGE015
representing a single dimensioniThe relevance grade of each relevance index is used for improving the weight of the relevance grade of the 1 level in the dimension, and
Figure DEST_PATH_IMAGE016
；
Figure DEST_PATH_IMAGE017
the first step of representing the level of the relevance grade which can be reached by all the relevance indexes in a single dimension and is required to be improved by one stepiWhether the relevance grade of each relevance index is reached or not, and when the relevance grade is reached
Figure DEST_PATH_IMAGE018
When it is not reached
Figure DEST_PATH_IMAGE019
；
Figure DEST_PATH_IMAGE020
Representing the finally calculated relevance value;
u represents the current relevance grade;
Figure DEST_PATH_IMAGE021
representing a single dimensioniThe relevance grade of each relevance index is weighted to promote the relevance grade of level 1, and
Figure DEST_PATH_IMAGE022
；
Figure DEST_PATH_IMAGE023
indicating the first order required to raise the current association level by one leveliWhether the relevance grade of each relevance index is reached or not, and when the relevance grade is reached
Figure DEST_PATH_IMAGE024
When it is not reached
Figure DEST_PATH_IMAGE025
；
iA number indicating a correlation index.
5. A management and control system for intelligent building of building engineering, which is used for executing the management and control method for intelligent building of building engineering of any one of claims 1-4, and is characterized by comprising the following modules:
the monitoring module is used for monitoring the construction site in real time to obtain engineering image data and building construction data;
the digital twin technical module is used for constructing a digital twin model and carrying out self-adaptive updating, relevance calculation and optimization on the digital twin model;
the supervision and scheduling module is used for carrying out centralized supervision on the construction site of the building engineering and distributing scheduling tasks of all levels;
and the field construction module is used for carrying out actual construction operation on a construction field and completing construction tasks.
6. The system according to claim 5, wherein the monitoring module comprises a video monitoring camera, a strain displacement monitoring sensor for engineering, and a temperature and humidity sensor;
the supervision and scheduling module comprises a multilayer structure which is an enterprise level management and control unit, a project level management and control unit and a production level management and control unit respectively.
CN202211082006.9A 2022-09-06 2022-09-06 Management and control method and system for intelligent construction of building engineering Active CN115146883B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211082006.9A CN115146883B (en) 2022-09-06 2022-09-06 Management and control method and system for intelligent construction of building engineering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211082006.9A CN115146883B (en) 2022-09-06 2022-09-06 Management and control method and system for intelligent construction of building engineering

Publications (2)

Publication Number Publication Date
CN115146883A CN115146883A (en) 2022-10-04
CN115146883B true CN115146883B (en) 2023-01-20

Family

ID=83416143

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211082006.9A Active CN115146883B (en) 2022-09-06 2022-09-06 Management and control method and system for intelligent construction of building engineering

Country Status (1)

Country Link
CN (1) CN115146883B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115492413A (en) * 2022-10-31 2022-12-20 温州理工学院 Control system and method of intelligent wall building robot
CN115526066B (en) * 2022-11-25 2023-03-03 中交第四航务工程勘察设计院有限公司 Engineering project virtual simulation teaching method and system based on BIM technology
CN115660509B (en) * 2022-12-26 2023-06-27 沈阳创新设计研究院有限公司 Factory building control method and system based on digital twin technology
CN115660262B (en) * 2022-12-29 2023-05-12 网思科技股份有限公司 Engineering intelligent quality inspection method, system and medium based on database application
CN117313223B (en) * 2023-11-30 2024-02-27 江苏云网数智信息技术有限公司 Intelligent building agile development system based on digital twinning
CN117541938B (en) * 2024-01-08 2024-03-29 清华大学 Linear cultural heritage data acquisition method and device based on unmanned aerial vehicle remote sensing

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113065276A (en) * 2021-03-09 2021-07-02 北京工业大学 Intelligent construction method based on digital twins
CN113433914B (en) * 2021-07-06 2022-09-30 山东电力工程咨询院有限公司 Intelligent construction site management and control platform and monitoring method thereof
CN114202622B (en) * 2022-02-18 2022-05-31 腾讯科技（深圳）有限公司 Virtual building generation method, device, equipment and computer readable storage medium
CN114898569B (en) * 2022-07-15 2022-10-21 山东金宇信息科技集团有限公司 Tunnel traffic emergency processing method and device

Also Published As

Publication number Publication date
CN115146883A (en) 2022-10-04

Similar Documents

Publication Publication Date Title
CN115146883B (en) Management and control method and system for intelligent construction of building engineering
CN110481536B (en) Control method and device applied to hybrid electric vehicle
CN110544296A (en) intelligent planning method for three-dimensional global flight path of unmanned aerial vehicle in environment with uncertain enemy threat
CN102323996A (en) Three-dimensional GIS (Geographic Information System) technology based visual state monitoring system for power transmission lines
Deng et al. Metaverse-driven remote management solution for scene-based energy storage power stations
CN110161999A (en) Coking intelligent manufacturing system based on big data
CN117171842A (en) Urban slow-moving bridge health monitoring and digital twin system
CN109347926A (en) Edge calculations intelligent perception system building method towards the protection of bright Ruins of Great Wall
CN116207739B (en) Optimal scheduling method and device for power distribution network, computer equipment and storage medium
CN108346009B (en) Power production configuration method and device based on user model self-learning
CN109002928A (en) A kind of electric load peak value prediction technique and device based on Bayesian network model
CN109214565A (en) A kind of subregion system loading prediction technique suitable for the scheduling of bulk power grid subregion
CN109242248A (en) Cigarette machine multidimensional data comprehensive analysis platform and method
CN116128094A (en) Industrial park energy management system and method based on digital twinning
CN114943456A (en) Resource scheduling method and device, electronic equipment and storage medium
CN114415607A (en) Design process manufacturing integrated digital twin system based on data driving
CN115994197A (en) GeoSOT grid data calculation method
CN115033380A (en) Building engineering intelligent integrated cloud platform based on BIM technology
CN115037590A (en) Network virtualization system structure and virtualization method
CN111260206A (en) Photovoltaic power generation influence factor evaluation model, construction method and application
CN112183918B (en) Intelligent generation method of power transmission line online inspection operation plan
CN116794532A (en) Unmanned aerial vehicle battery electric quantity prediction method based on multi-mode sensor fusion algorithm
Yao Internet of Things in the Quality Control of Cement Mixing Pile Construction
CN116307498A (en) Intelligent scheduling method and device for construction tasks, storage medium and processor
CN116308128A (en) Method, equipment and medium for green construction management of assembled building

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