Digital intelligence integration method and system based on BIM (Building Information Modeling) technology

Abstract

The invention discloses a digital intelligence integration method based on a BIM technology. The method comprises the steps of collecting project plans, preset cost, project sizes and quality detection data of a building project; processing the data according to the standard of the BIM model to establish a data resource library; building a digital and intelligent collaboration platform based on the BIM technology, connecting a data resource library, and building a building project BIM-3D model by using data of the data resource library; attribute tags of time, cost and quality are added to the BIM-3D model, a building project BIM-6D model is obtained, data in the BIM-6D model are mined and analyzed by using a clustering algorithm, and regions or equipment groups of different energy consumption modes are identified; evaluating and predicting the performance indexes of the regions or equipment groups of different energy consumption modes by using an anomaly detection algorithm, and performing early warning; vR and AR technologies are combined to carry out visual display; in the construction project operation stage, construction equipment and environment data are continuously collected and transmitted to the BIM-6D model to be updated. The invention further provides a digital intelligence integration system based on the BIM technology.

Description

Technical Field

[0001] The present invention relates to the field of integrated management and control technology, and in particular to a digital integration method and system based on BIM technology. Background Art

[0002] In the rapid development of today's construction industry, there are many complex challenges and opportunities. With the acceleration of urbanization, the scale of buildings is becoming larger and the functions are becoming more complex. Traditional construction project management methods and technical means are gradually exposing many limitations.

[0003] On the one hand, the entire life cycle of a construction project involves a massive amount of data and information, including architectural blueprints, structural design data, and equipment selection information in the planning and design phase; material procurement data, construction schedules, and quality inspection records in the construction phase; equipment operating parameters, energy consumption data, and space usage in the operation and maintenance phase. These data are often stored in different systems and documents in different data formats, making it difficult to achieve effective integration and sharing, forming information islands. For example, the design data generated by the design team using professional design software is difficult to be directly used by the construction team for construction organization arrangements. After taking over the building, the operation team needs to collect and organize relevant data again, which not only leads to repeated data collection and processing, increasing labor and time costs, but also easily leads to wrong decisions due to poor or inconsistent data transmission, affecting the smooth progress of the project.

[0004] On the other hand, the various parties involved in the construction project, such as the owner, design unit, construction unit, supervision unit, and operation unit, lack an efficient collaborative work platform in the traditional model. Communication between the parties mainly relies on meetings, emails, paper documents, etc., and the timeliness and accuracy of information transmission are difficult to guarantee. During the implementation of the project, the design change information cannot be communicated to the construction party in a timely manner, which may lead to construction errors or delays; after the problems found during the construction process are fed back to the design party, it is difficult to quickly get a solution due to poor communication, which seriously affects the collaborative efficiency and quality control of the project.

[0005] At the same time, as people's requirements for building performance continue to increase, such as energy conservation, environmental protection, and improved comfort, traditional building technologies and management methods are difficult to meet the requirements for accurate analysis, prediction, and optimization of building performance throughout its life cycle. In terms of building energy consumption management, there is a lack of effective data monitoring and analysis systems, and it is impossible to grasp the energy consumption of various areas and equipment in the building in real time, making it difficult to formulate targeted energy-saving measures; in terms of building quality control, it mainly relies on manual inspections and post-inspections, making it difficult to discover potential quality problems in advance and prevent them during the design and construction process.

[0006] The emergence of BIM technology provides a powerful tool to solve these problems. BIM can organically combine the three-dimensional geometric information of a building with various non-geometric information (such as material properties, equipment parameters, construction progress, etc.) to create a digital building information database. However, the application of pure BIM technology in actual projects still has some shortcomings, such as insufficient integration with other intelligent technologies, and it is difficult to give full play to its potential in data intelligent analysis, decision support, etc. Summary of the invention

[0007] In view of this, it is necessary to provide a digital integration method based on BIM technology to solve the technical problems that the background technology makes it difficult to conduct comprehensive and systematic management and control of the project's design, investment, quality, safety, progress, personnel and environment, and the data has not established effective associations, resulting in difficulty in achieving efficient management. There is a lack of an efficient collaborative work platform among project participants, which can easily affect the project's collaborative efficiency and quality control.

[0008] It is also necessary to provide a digital integration method system based on BIM technology.

[0009] A digital integration method based on BIM technology includes the following steps:

[0010] S1: Comprehensively collect data from all stages of a construction project using a variety of sensors and data collection equipment; the data from all stages include project plans, pre-set costs, project dimensions, and quality inspection data;

[0011] S2: Classify, organize and encode the collected data according to the data standards of the BIM model to establish a unified data resource library;

[0012] S3: Build a digital collaboration platform based on BIM technology, connect to the data resource library, extract project dimensions and build a BIM-3D model of the construction project;

[0013] S4: Allocate the planned construction time to the timeline according to the project plan, associate it with the building components involved in each timeline, and add time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong;

[0014] S5: extracting preset costs, associating them with building components, and adding cost attribute tags to model components of the BIM-3D model of the building project;

[0015] S6: A quality score is obtained by performing quality inspection on the checkpoints preset in the BIM-3D model of the construction project according to the quality standard of the construction project, and a quality score attribute label is added to the checkpoints of the BIM-3D model of the construction project;

[0016] S7: Integrate the BIM-3D model of the construction project based on the time attribute tag, the cost attribute tag, and the quality score attribute tag to obtain the BIM-6D model of the construction project, and use the clustering algorithm to mine and analyze the data in the BIM-6D model of the construction project to automatically identify the areas or equipment groups with different energy consumption patterns;

[0017] S8: Use anomaly detection algorithms to comprehensively evaluate and predict various performance indicators of regions or equipment groups with different energy consumption patterns, predict equipment failure or energy waste risks, and issue corresponding early warnings;

[0018] S9: Set up corresponding platform docking ports for project participants, and combine VR and AR technologies for visual display; during the operation stage of the construction project, continuously collect data from each stage through the Internet of Things technology, and transmit it to the BIM-6D model of the construction project for real-time updating.

[0019] Preferably, the digital collaborative platform automatically detects the connected data and automatically issues warnings for data that exceeds a preset threshold.

[0020] Preferably, the checkpoints preset in the BIM-3D model of the construction project in S6 are obtained based on analysis of a machine learning algorithm; and the quality score is obtained by analyzing quality inspection data based on a machine learning algorithm.

[0021] Preferably, a user editing function is also provided in the digital collaborative platform, so that users can adjust the BIM-6D model of the construction project. The BIM-6D model of the construction project is adjusted in real time according to the user editing results. The digital collaborative platform also compares and analyzes the change data in the BIM-6D model of the construction project constructed each time.

[0022] Preferably, the regional or device grouping performance data in S7 is extracted, including device information, time attribute tags, cost attribute tags and quality score attribute tags, and the device information, time attribute tags, cost attribute tags and quality score attribute tags are spliced ​​as the input of the pre-trained energy consumption analysis model to obtain the corresponding energy consumption data, and an isolated forest is constructed according to the collected energy consumption data set X containing n data, wherein each isolated tree corresponds to a region or device group with the same energy consumption mode, and for each isolated tree T, the fth energy consumption data x is randomly selected. f and a split point p, the energy consumption data set X is divided into X1 = {x f |x f ∈X,x f ≤p} and X2={x f |x f ∈X,x f>p} two subsets, recursively splitting X1 and X2 until only one data point remains in the subset or the maximum depth of the tree is reached, that is, the upper limit of the number of times the isolated tree divides the energy consumption score during the construction process h max ;

[0023] Calculate the i-th data point x in the energy consumption dataset X i The path length in the isolated tree T, that is, the difficulty of isolating the energy consumption data h(x i ):

[0024] h(x i )=λ+δ(T,size)-δ(T l ,size)-δ(T r ,size)

[0025] Among them, λ is the depth of the current node; T is the subtree where the current node is located; T l and T r are the left subtree and the right subtree respectively; δ(·) is the correction function;

[0026]

[0027] Where n is the correction variable; α is the Euler constant;

[0028] For a data point x i , calculate its anomaly score s(x i ):

[0029]

[0030] Among them, g(h(x i )) is x i The average path length among all isolated trees;

[0031] The anomaly score s(x i ) is compared with the abnormal score threshold, and the abnormal state of the energy consumption data is classified according to the comparison result, and then the corresponding warning of equipment failure or energy waste risk is issued according to the abnormal state.

[0032] Preferably, in S5, a containerization tool is used to package each component of the digital collaboration platform and its dependencies into an independent container image; the BIM collaboration platform is split into a number of fine-grained microservices, and each microservice uses a container instance packaged into an independent container image to perform corresponding business functions.

[0033] Preferably, during operation, the digital collaborative platform predicts the maximum resource demand expected for the construction project based on the pre-planning results of the construction project in combination with a machine learning algorithm, and dynamically plans the number of nodes and the hardware configuration of each node according to the prediction results.

[0034] Preferably, the digital collaboration platform records and stores data based on blockchain technology during the data sharing process among project participants.

[0035] A digital and intelligent integrated system based on BIM technology, including:

[0036] Data collection module: used to comprehensively collect data of each stage of the construction project using a variety of sensors and data collection equipment; the data of each stage includes project plan, preset cost, project size and quality inspection data;

[0037] Data integration module: used to classify, organize and encode the collected data according to the data standards of the BIM model and establish a unified data resource library;

[0038] Platform building module: build a digital collaborative platform based on BIM technology, connect to the data resource library, extract project dimensions and build a BIM-3D model of the construction project;

[0039] Time overlay module: allocates the planned construction time to the timeline according to the project plan, associates it with the building components involved in each timeline, and adds time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong;

[0040] Cost overlay module: extracts preset costs, associates them with building components, and adds cost attribute tags to model components in the BIM-3D model of the building project;

[0041] Quality score overlay module: The quality score is obtained by quality inspection of the preset checkpoints in the BIM-3D model of the construction project according to the quality standards of the construction project, and the quality score attribute tags are added to the checkpoints of the BIM-3D model of the construction project;

[0042] Deepening mining module: used to integrate the BIM-3D model of the construction project based on the time attribute tag, cost attribute tag and quality score attribute tag to obtain the BIM-6D model of the construction project, and use the clustering algorithm to mine and analyze the data in the BIM-6D model of the construction project to automatically identify the areas or equipment groups with different energy consumption patterns;

[0043] Risk warning module: It is used to use anomaly detection algorithms to comprehensively evaluate and predict the performance indicators of areas or equipment groups with different energy consumption patterns in building projects, predict the risk of equipment failure or energy waste, and issue corresponding warnings;

[0044] Collaborative visualization module: used to set up corresponding platform docking ports for project participants, and combine VR and AR technologies for visualization; during the operation stage of the construction project, data from each stage is continuously collected through the Internet of Things technology and transmitted to the BIM-6D model of the construction project for real-time updating.

[0045] In the above-mentioned digital integration method and system based on BIM technology, the collected data is stored in the data resource library, and the data in the data resource library is integrated, mined and analyzed, so as to realize the effective association and efficient management of data, and provide strong support for the all-round management of the project; it also carries out all-round comprehensive management and control of the project based on the BIM-6D model of the construction project and the anomaly detection algorithm, and achieves visual and real-time dynamic management and control effects through data integration and analysis at all levels, which is helpful to timely discover problems, optimize project management processes, and improve the overall benefits of the project; it also builds a collaborative platform integrating all project participants, which can realize information sharing and improve project collaboration efficiency and project quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the method mentioned in Example 1 of the present invention;

[0047] Figure 2 This is a system block diagram mentioned in Example 1 of the present invention.

[0048] In the figure: data collection module 100, data integration module 200, platform building module 300, time overlay module 400, cost overlay module 500, quality score overlay module 600, in-depth mining module 700, risk warning module 800, collaborative visualization module 900. DETAILED DESCRIPTION

[0049] The present invention achieves effective association and efficient management of data by storing the collected data in a data resource library and integrating, mining and analyzing the data in the data resource library, thereby providing strong support for the all-round management of the project; it also conducts all-round comprehensive management and control of the project based on the BIM-6D model of the construction project and the anomaly detection algorithm, and achieves visual and real-time dynamic management and control effects through data integration and analysis at all levels, which helps to discover problems in time, optimize project management processes, and improve the overall benefits of the project; it also builds a collaborative platform integrating all project participants, which can realize information sharing and improve project collaboration efficiency and project quality.

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 The present invention provides a digital integration method based on BIM technology, comprising the following steps:

[0052] S1: Comprehensively collect data from all stages of the construction project using a variety of sensors and data collection equipment; details are as follows:

[0053] By using a variety of sensors and data acquisition equipment, comprehensive data on construction projects at all stages, from planning and design, construction to operation and maintenance, including project plans, preset costs, project dimensions and quality inspection data, can be collected. This can provide rich and accurate original materials for the later construction of the BIM-6D model of the construction project, so that the model can truly reflect the characteristics of all aspects of the building, covering dimensional information such as geometry, cost input, and quality status.

[0054] S2: Classify, organize and encode the collected data according to the data standards of the BIM model to establish a unified data resource library; the details are as follows:

[0055] The collected data shall be classified, organized and coded according to the data standards of the BIM model, such as integrating data of different formats and sources through data mapping and conversion technology; a unified data resource library shall be established to uniformly store the classified, organized and coded data to realize the integration and sharing of multi-source data.

[0056] S3: Build a digital collaborative platform based on BIM technology, connect to the data resource library, extract project dimensions and build a BIM-3D model of the construction project; the details are as follows:

[0057] Use containerization tools to package each component of the digital collaboration platform and its dependencies into an independent container image; split the BIM collaboration platform into several fine-grained microservices, such as model management services, data storage services, collaborative communication services, etc. Each microservice can be independently developed, deployed and upgraded to facilitate functional expansion and optimization. For example, when a new data analysis function needs to be added, only the corresponding data analysis microservice needs to be developed and deployed without affecting the normal operation of other functional modules, thereby improving the flexibility and maintainability of the platform. Each microservice uses a container instance packaged into an independent container image to perform the corresponding business function. During the operation of the digital collaboration platform, based on the pre-planning results of the construction project, combined with machine learning algorithms, the maximum resource requirements expected for the construction project are predicted, and the number of nodes and the hardware configuration of each node are dynamically planned according to the prediction results. Computing resources, storage resources, etc. can be automatically allocated according to the different stages and needs of the project. In the early stage of project design, the resource demand is relatively small, and the platform automatically allocates fewer resources; with the development of computing-intensive tasks such as design deepening and construction simulation, the platform automatically expands resources to ensure the smoothness of project operation while reducing resource costs.

[0058] The digital collaboration platform introduces blockchain technology in the data sharing process of all project participants. Users can create and edit construction project schedules and project costs on the platform, but each creation, modification and transmission of data is recorded on the blockchain to ensure that the data cannot be tampered with and is traceable. For example, the design change data uploaded by the design unit can be obtained in real time by the construction unit, supervision unit, etc. and trust the authenticity of the data. At the same time, the origin and approval process of the change can be traced back to effectively solve the data trust problem and improve collaboration efficiency.

[0059] The digital collaboration platform automatically detects the docked data and automatically issues warnings for data that exceeds the preset threshold. The digital collaboration platform automatically builds the BIM-3D model of the construction project based on the docked project size and BIM technology.

[0060] S4: Allocate the planned construction time to the timeline according to the project plan, associate it with the building components involved in each timeline, and add time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong; the details are as follows:

[0061] Extract the project plan, and allocate the different construction stages and specific activities of the construction project, such as foundation construction, main structure construction, decoration engineering, etc., to the timeline according to the planned construction time, and associate them with the corresponding building components. In this way, by viewing the model, you can intuitively understand the construction status of each component at different time points and the progress of the entire project. For example, in the BIM-3D model of a residential project, the masonry construction of the first layer of walls is scheduled to be carried out from the 30th to the 40th day, so this construction activity can be associated with the model component representing the first layer of walls in the time dimension. By superimposing the time dimension on the BIM-3D model of the construction project, the logical relationship between the construction processes can be clarified and reflected in the model. For example, in the time dimension, it is set that the construction of the main structure can only start after the foundation project is completed. This logical relationship setting helps to better plan and control the progress, avoid problems such as process reversal, and ensure that the project is carried out in a reasonable order. Based on the information of the time dimension, the construction progress can also be simulated. By adjusting the time parameters, such as advancing or delaying the start time of certain processes, the impact on the progress of the entire project can be observed, so as to discover potential progress risks and take measures to optimize them in advance. For example, if it is found that a delay in a process on a critical path will cause a delay in the total project duration, the duration of the process can be shortened by adjusting resource allocation or optimizing construction methods to ensure that the project is completed on time.

[0062] S5: extract the preset cost, associate it with the building component, and add the cost attribute tag to the model component of the BIM-3D model of the building project; the details are as follows:

[0063] Extract the preset cost and annotate the corresponding cost information for each component or assembly in the BIM-3D model of the construction project, which may include material cost, labor cost, equipment rental cost, etc. These cost data can be obtained from the project's budget list or cost estimation system and accurately correspond to the specific components in the model. For example, in addition to its geometric dimensions and material information, the model component of a concrete column can also be annotated with its concrete material cost, steel bar cost, formwork and support cost, and labor cost required to cast the column. The annotated cost information can also be summarized and displayed according to different classification methods, such as classification and summary according to building systems (structural systems, water supply and drainage systems, electrical systems, etc.), construction stages or cost subjects (direct costs, indirect costs, etc.) to facilitate cost analysis and control. In this way, the cost distribution of each part can be clearly understood and the key links of cost control can be found. As the project progresses, actual cost data will continue to be generated. The actual cost data can also be compared and analyzed with the budget cost in the model to monitor the cost deviation in real time. If it is found that the actual cost of a certain stage or a certain component exceeds the budget, the cause can be found in time and corresponding measures can be taken to control the cost, such as optimizing the construction plan, reducing material waste, adjusting resource allocation, etc., to ensure that the project cost is within a controllable range.

[0064] S6: A quality score is obtained by performing quality inspection on the checkpoints preset in the BIM-3D model of the construction project according to the quality standards of the construction project, and a quality score attribute label is added to the checkpoints of the BIM-3D model of the construction project; the details are as follows:

[0065] Based on the machine learning algorithm, the key quality inspection points and acceptance parts are determined in the BIM-3D model of the construction project, and the corresponding quality standards and acceptance requirements are marked according to the quality standards of the construction project. These inspection points can be key parts of concealed projects, important components involving structural safety, key nodes of waterproof projects, etc. For example, before pouring concrete, the quality inspection points of the steel bar binding are marked in the model, and the quality standards such as the spacing, anchorage length, and joint position of the steel bars are clearly defined so that construction personnel and quality management personnel can conduct comparative inspections on site. By checking and obtaining quality scores, various safety risk factors in the construction process of the construction project can be identified and evaluated to reduce the possibility of safety accidents. Quality inspection records, safety accident reports, rectification measures and other quality and safety related information can be integrated with the BIM-3D model of the construction project. By clicking on the corresponding component or part in the model, you can view its detailed quality and safety history and achieve traceability of quality and safety issues. This helps to quickly locate the root cause of the problem, clarify responsibilities, take effective rectification measures, and avoid the recurrence of similar problems when quality and safety problems occur.

[0066] S7: Integrate the BIM-3D model of the building project based on the time attribute tag, the cost attribute tag, and the quality score attribute tag to obtain the BIM-6D model of the building project, use the clustering algorithm to mine and analyze the data in the BIM-6D model of the building project, and automatically identify the areas or equipment groups with different energy consumption patterns; the details are as follows:

[0067] The time attribute label, cost attribute label and quality score attribute label are added to the corresponding point coordinates of the BIM-3D model of the building project for integration to obtain the BIM-6D model of the building project. The clustering algorithm is then used to mine and analyze the data in the BIM-6D model of the building project to automatically identify the areas or equipment groups with different energy consumption patterns. The clustering algorithm is used to mine and analyze the BIM-6D model of the building project, and the building components can be clustered according to their spatial position, shape, size and other geometric characteristics to identify component groups with similar energy consumption characteristics. The energy consumption changes of various areas or equipment in the building in different time periods can also be analyzed. For example, office areas with high energy consumption during the day and low energy consumption at night on weekdays are clustered into one category, while public activity areas with relatively stable energy consumption on weekends are clustered into another category. In this way, the differences in energy consumption patterns related to time can be more accurately identified, providing a basis for formulating time-sharing energy consumption management strategies; the cost expenditure caused by energy consumption can also be comprehensively evaluated. For example, for some equipment with high energy consumption but also important production functions, although the absolute value of their energy consumption is high, it may be reasonable from the perspective of the energy consumption cost per unit output, and these equipment can be grouped together; and for some equipment or areas with high energy consumption and low efficiency, they can be classified into another group, so as to carry out targeted energy-saving transformation or optimization management to achieve dual control of energy consumption and cost; energy consumption patterns can also be mined and analyzed from the dimension of quality and safety. For example, certain quality problems may cause the thermal insulation performance of the building envelope to decline, thereby causing abnormal energy consumption in the area; or certain safety hazards may affect the normal operation of the equipment, resulting in energy consumption fluctuations. Through clustering algorithms, areas or equipment with similar quality and safety problems and abnormal energy consumption can be identified, potential energy waste risk points can be discovered in a timely manner, and corresponding measures can be taken to repair and improve them, so as to improve the overall energy utilization efficiency and quality and safety level of the building; it can provide clues for targeted energy-saving transformation.

[0068] S8: Use anomaly detection algorithms to comprehensively evaluate and predict the performance indicators of areas or equipment groups with different energy consumption patterns in building projects, predict the risk of equipment failure or energy waste, and issue corresponding warnings; the details are as follows:

[0069] Extract regional or device grouping performance data, including device information, time attribute labels, cost attribute labels, and quality score attribute labels, and concatenate the device information, time attribute labels, cost attribute labels, and quality score attribute labels as the input of the pre-trained energy consumption analysis model to obtain the corresponding energy consumption data. Construct an isolation forest based on the collected energy consumption dataset X containing n data, where each isolated tree corresponds to a region or device group with the same energy consumption mode. For each isolated tree T, randomly select the fth energy consumption data x f and a split point p, the energy consumption data set X is divided into X1 = {x f |x f ∈X,x f ≤p} and X2={x f |x f ∈X,x f >p} two subsets, recursively splitting X1 and X2 until only one data point remains in the subset or the maximum depth of the tree is reached, that is, the upper limit of the number of times the isolated tree divides the energy consumption score during the construction process h max ;

[0070] Calculate the i-th data point x in the energy consumption dataset X i The path length in the isolated tree T, that is, the difficulty of isolating the energy consumption data h(x i ):

[0071] h(x i )=λ+δ(T,size)-δ(T l ,size)-δ(T r ,size)

[0072] Among them, λ is the depth of the current node; T is the subtree where the current node is located; T l and T r are the left subtree and the right subtree respectively; δ(·) is the correction function;

[0073]

[0074] Where n is the correction variable; α is the Euler constant;

[0075] For a data point x i , calculate its anomaly score s(x i ):

[0076]

[0077] Among them, g(h(x i )) is x i The average path length among all isolated trees;

[0078] The anomaly score s(xi ) is compared with the abnormal score threshold, and the abnormal state of the energy consumption data is classified according to the comparison result, and then the corresponding warning of equipment failure or energy waste risk is issued according to the abnormal state.

[0079] Management of construction projects based on the BIM-6D model of the construction project, combined with the results of area or equipment grouping and the corresponding risk levels, can be found in Table 1:

[0080] Table 1 6D management data of different building points and corresponding abnormal situations

[0081]

[0082] It can be seen from the table that based on the BIM-6D model of the construction project and the corresponding risk assessment data, the construction project can be evaluated intuitively, and abnormal situations can be warned in time to ensure the safe implementation of the construction project.

[0083] S9: Set up corresponding platform docking ports for project participants, and combine VR and AR technologies for visual display; during the operation stage of the construction project, continuously collect data from each stage through the Internet of Things technology, and transmit it to the BIM-6D model of the construction project for real-time update. The details are as follows:

[0084] VR technology is used to create highly realistic construction scene simulations, so that construction personnel can conduct equipment operation training and construction process drills in a virtual environment. For example, in large-scale steel structure installation training, operators can repeatedly practice the lifting, positioning and splicing of cranes in a virtual space, feel the actual effects under different operating parameters, and improve training results and construction proficiency.

[0085] At the construction site and during the operation and maintenance phase, AR equipment is used to overlay BIM-6D model information of the construction project onto the actual scene. For example, when repairing equipment, maintenance personnel can view the internal structure of the equipment, maintenance steps, and historical maintenance records through AR glasses, which can intuitively guide the maintenance operation; during the construction layout process, the design axis and component positioning information can be directly projected onto the ground to improve construction accuracy and efficiency.

[0086] During the operation of the building, IoT sensors continuously collect building equipment and environmental data and transmit them to the data processing center through wireless networks. The data processing center analyzes the data in real time to determine whether the equipment is operating normally, whether environmental parameters exceed the standard, etc. If an abnormal situation is found, an early warning message will be issued immediately and relevant personnel will be notified to handle it.

[0087] The BIM-6D model of the building project is updated regularly according to the actual use and maintenance records of the building. For example, when the building undergoes partial renovation or equipment replacement, the corresponding information in the BIM-6D model of the building project is updated in a timely manner to ensure that the model is consistent with the actual status of the building. At the same time, the updated model data is stored in the historical data archive to provide data reference for the long-term operation management and renovation and upgrading of the building.

[0088] For further information, see Figure 2 The present invention also provides a digital intelligent integration system based on BIM technology, including:

[0089] Data collection module 100: used to collect data from all stages of a construction project using a variety of sensors and data collection equipment. The data from all stages include project plans, preset costs, project dimensions, and quality inspection data.

[0090] Integrated construction module 200: used to classify, organize and encode the collected data according to the data standard of the BIM model. For example, data mapping and conversion technology can be used to achieve the above classification, organization and encoding, obtain the corresponding building organization data, and establish a unified data resource library based on the building organization data.

[0091] Platform building module 300: building a digital and intelligent collaborative platform based on BIM technology, connecting to the data resource library, extracting project dimensions and building a BIM-3D model of the construction project;

[0092] Time superposition module 400: allocates the planned construction time to the timeline according to the project plan, associates it with the building components involved in each timeline, and adds time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong;

[0093] Cost superposition module 500: extracts preset costs, associates them with building components, and adds cost attribute tags to model components of the BIM-3D model of the building project;

[0094] The quality score superposition module 600: performs quality inspection on the checkpoints preset in the BIM-3D model of the construction project according to the quality standard of the construction project to obtain the quality score, and adds the quality score attribute label to the checkpoint of the BIM-3D model of the construction project;

[0095] Deepening mining module 700: used to integrate the BIM-3D model of the construction project based on the time attribute tag, the cost attribute tag and the quality score attribute tag to obtain the BIM-6D model of the construction project, use the clustering algorithm to mine and analyze the data in the BIM-6D model of the construction project, and automatically identify the areas or equipment groups with different energy consumption patterns.

[0096] Risk warning module 800: used to use anomaly detection algorithms to comprehensively evaluate and predict various performance indicators of areas or equipment groups with different energy consumption patterns in a building project, predict equipment failure or energy waste risks, and issue corresponding warnings.

[0097] Collaborative visualization module 900: used to set corresponding platform docking ports for project participants, and combine VR and AR technologies for visualization; during the operation stage of the construction project, data from each stage is continuously collected through the Internet of Things technology and transmitted to the BIM-6D model of the construction project for real-time updating.

[0098] Although the above describes an illustrative specific embodiment of the present invention so that those skilled in the art can understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiment. As long as various changes are within the scope of the spirit and limitations of the claims attached to the present invention, these changes are obvious, and all inventions and creations using the concepts of the present invention are protected.

Claims

1. A digital integration method based on BIM technology, characterized in that: The steps include: S1: Comprehensively collect data from all stages of a construction project using a variety of sensors and data collection equipment; the data from all stages include project plans, pre-set costs, project dimensions, and quality inspection data; S2: Classify, organize and encode the collected data according to the data standards of the BIM model to establish a unified data resource library; S3: Build a digital collaboration platform based on BIM technology, connect to the data resource library, extract project dimensions and build a BIM-3D model of the construction project; S4: Allocate the planned construction time to the timeline according to the project plan, associate it with the building components involved in each timeline, and add time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong; S5: extracting preset costs, associating them with building components, and adding cost attribute tags to model components of the BIM-3D model of the building project; S6: A quality score is obtained by performing quality inspection on the checkpoints preset in the BIM-3D model of the construction project according to the quality standard of the construction project, and a quality score attribute label is added to the checkpoints of the BIM-3D model of the construction project; S7: Integrate the BIM-3D model of the building project based on the time attribute tag, the cost attribute tag, and the quality score attribute tag to obtain the BIM-6D model of the building project, and use the clustering algorithm to mine and analyze the data in the BIM-6D model of the building project to automatically identify the areas or equipment groups with different energy consumption patterns; S8: Use anomaly detection algorithms to comprehensively evaluate and predict various performance indicators of regions or equipment groups with different energy consumption patterns, predict equipment failure or energy waste risks, and issue corresponding early warnings; S9: Set up corresponding platform docking ports for project participants, and combine VR and AR technologies for visual display; during the operation stage of the construction project, continuously collect data from each stage through the Internet of Things technology, and transmit it to the BIM-6D model of the construction project for real-time updating.

2. The digital integration method based on BIM technology according to claim 1 is characterized in that: The digital collaborative platform automatically detects the connected data and automatically issues warnings for data that exceeds preset thresholds.

3. The digital integration method based on BIM technology according to claim 1 is characterized in that: The preset checkpoints in the BIM-3D model of the construction project described in S6 are obtained based on the analysis of the machine learning algorithm; the quality score is obtained by analyzing the quality inspection data based on the machine learning algorithm.

4. The digital integration method based on BIM technology according to claim 1 is characterized in that: The digital collaborative platform also has a user editing function for users to adjust the BIM-6D model of the construction project. The BIM-6D model of the construction project is adjusted in real time according to the user editing results. The digital collaborative platform also compares and analyzes the change data in the BIM-6D model of the construction project built each time.

5. The digital integration method based on BIM technology according to claim 3 is characterized in that: In S8, the regional or device grouping performance data in S7 is extracted, including device information, time attribute tags, cost attribute tags, and quality score attribute tags, and the device information, time attribute tags, cost attribute tags, and quality score attribute tags are spliced ​​as inputs of the pre-trained energy consumption analysis model to obtain corresponding energy consumption data, and an isolated forest is constructed according to the collected energy consumption data set X containing n data, wherein each isolated tree corresponds to a region or device grouping with the same energy consumption mode, and for each isolated tree T, the fth energy consumption data x is randomly selected. f and a split point p, the energy consumption data set X is divided into X1 = {x f x f ∈X,x f ≤p} and X2={x f x f ∈X,x f >p} two subsets, recursively splitting X1 and X2 until only one data point remains in the subset or the maximum depth of the tree is reached, that is, the upper limit of the number of times the isolated tree divides the energy consumption score during the construction process h max ; Calculate the i-th data point x in the energy consumption dataset X i The path length in the isolated tree T, that is, the difficulty of isolating the energy consumption data h(x i ): h(x i )=λ+δ(T,size)-δ(T l ,size)-δ(T r ,size) Among them, λ is the depth of the current node; T is the subtree where the current node is located; T l and T r are the left subtree and the right subtree respectively; δ(·) is the correction function; Where n is the correction variable; α is the Euler constant; For a data point x i , calculate its anomaly score s(x i ): Among them, g(h(x i )) is x i The average path length among all isolated trees; The anomaly score s(x i ) is compared with the abnormal score threshold, and the abnormal state of the energy consumption data is classified according to the comparison result, and then corresponding warnings are issued for equipment failure or energy waste risks according to the abnormal state.

6. The digital integration method based on BIM technology according to claim 1 is characterized in that: Use containerization tools to package each component of the digital collaboration platform and its dependencies into an independent container image; split the BIM collaboration platform into several fine-grained microservices, and each microservice uses a container instance packaged into an independent container image to perform corresponding business functions.

7. The digital integration method based on BIM technology according to claim 6 is characterized in that: During operation, the digital collaborative platform predicts the maximum resource demand expected for the construction project based on the preliminary planning results of the construction project and combines machine learning algorithms, and dynamically plans the number of nodes and the hardware configuration of each node according to the prediction results.

8. The digital integration method based on BIM technology according to claim 7 is characterized in that: During the data sharing process among project participants, the digital collaborative platform records and stores data based on blockchain technology.

9. A digital intelligent integration system based on BIM technology for implementing the method according to any one of claims 1 to 8, characterized in that: include: Data collection module (100): used to comprehensively collect data at all stages of a construction project using a variety of sensors and data collection equipment; The data for each stage include project plan, pre-set cost, project size and quality inspection data; Data integration module (200): used to classify, organize and encode the collected data according to the data standard of the BIM model, and establish a unified data resource library; Platform building module (300): building a digital intelligent collaborative platform based on BIM technology, connecting to a data resource library, extracting project dimensions, and building a BIM-3D model of a building project; Time superposition module (400): allocates the planned construction time to the timeline according to the project plan, associates it with the building components involved in each timeline, and adds time attribute tags to the model components of the BIM-3D model of the building project according to the timeline to which the building components belong; Cost superposition module (500): extracts preset costs, associates them with building components, and adds cost attribute tags to model components of the BIM-3D model of the building project; A quality score superposition module (600) is used to obtain a quality score by performing quality inspection on a preset checkpoint in a BIM-3D model of a construction project according to a quality standard of the construction project, and to add a quality score attribute tag to the checkpoint in the BIM-3D model of the construction project; A deepening mining module (700) is used to integrate the BIM-3D model of the building project based on the time attribute tag, the cost attribute tag and the quality score attribute tag to obtain the BIM-6D model of the building project, and to mine and analyze the data in the BIM-6D model of the building project using a clustering algorithm to automatically identify the areas or equipment groups with different energy consumption patterns; Risk warning module (800): used to use anomaly detection algorithms to comprehensively evaluate and predict various performance indicators of areas or equipment groups with different energy consumption patterns in a building project, predict equipment failure or energy waste risks, and issue corresponding warnings; Collaborative visualization module (900): used to set corresponding platform docking ports for project participants, and to combine VR and AR technologies for visualization; during the operation stage of the construction project, data from each stage is continuously collected through the Internet of Things technology and transmitted to the BIM-6D model of the construction project for real-time updating.

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