Fabricated intelligent building management method and system based on BIM model

Abstract

The invention discloses a fabricated intelligent building management method and system based on a BIM model, and belongs to the technical field of intelligent building management, and the method comprises the following steps: S1, constructing a digital passport; s2, intelligent design collaboration; s3, self-adaptive production regulation and control; s4, dynamic construction management; s5, AI block chain collaboration is carried out; s6, performing self-learning operation maintenance; according to the invention, intelligentization and refinement of fabricated building are realized, a BIM-based fabricated building intelligent construction management system is constructed, effectiveness of the management system is evaluated, control key points of different control levels of the fabricated building intelligent construction management system are determined, and fabricated building system environment and system elements are determined. According to the BIM technology, a synergistic effect mechanism among subsystems is mined, each characteristic index in the system is determined, an intelligent management system is determined through quantitative and qualitative combined analysis, importance analysis is carried out on effectiveness influence factors, and information islands and repeated work in traditional design are avoided by realizing sharing and cooperative work of design information through the BIM technology.

Description

Technical Field

[0001] This invention specifically relates to a management method and system for prefabricated intelligent buildings based on BIM models, belonging to the field of intelligent building management technology. Background Technology

[0002] According to the current development trend of the construction industry, traditional construction methods are gradually being replaced by more efficient and intelligent prefabricated methods. Especially in the context of the development of building industrialization, BIM-based prefabricated buildings are becoming increasingly popular. Traditional construction methods are less efficient in management and construction, and are prone to problems such as mismatch between design and construction and difficulty in cost control. With the support of BIM technology, prefabricated buildings can achieve three-dimensional visualization of building information, thereby improving project coordination and accuracy and reducing uncertainty in the construction process. However, although the application of BIM technology in individual projects is relatively mature, there are still shortcomings in the systematic application at the management level. In particular, in prefabricated buildings, the coordination of construction and production processes, quality control, and cost management still require more refined and intelligent management methods. Current intelligent construction management systems for prefabricated buildings typically focus on technical support, with less emphasis on the construction and optimization of the management system. Existing management methods are often limited to traditional planning and control, making it difficult to meet the needs of prefabricated building projects for refined management and intelligent decision-making. Especially in the face of the ever-changing construction market, even with excellent BIM technology tools, without a supporting intelligent management system, they cannot realize their full value. To address the aforementioned technical challenges, a management method and system for prefabricated intelligent buildings based on BIM models is proposed. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a BIM-based intelligent building management method and system. This method aims to clarify the environment and elements of the prefabricated building system, explore the synergistic mechanisms between subsystems, determine the characteristic indicators within the system, establish an intelligent management system through a combination of quantitative and qualitative analysis, analyze the importance of factors affecting effectiveness, and achieve intelligent management of prefabricated buildings based on BIM technology. The BIM-based management method for prefabricated intelligent buildings includes the following steps: S1. Construct a digital passport to create an NFT digital identity for each prefabricated component, store key information such as material testing reports and production process parameters, and support traceability by scanning codes. S2. Intelligent design collaboration: It adopts a generative design algorithm, inputs land use conditions and functional requirements, and automatically outputs an optimized solution that meets the assembly rate requirements. S3. Adaptive production control: Deploy a visual inspection system in the prefabrication plant to capture the component processing in real time and compare it with BIM model data through CNN; S4. Dynamic construction management: Based on clustering algorithms, the position error threshold is dynamically calculated to improve assembly accuracy. S5, AI and blockchain work together to analyze historical project data to predict risks and dynamically optimize component production sequencing and logistics scheduling. S6. Self-learning operation and maintenance: Use error data accumulated during the construction period to train the LSTM network and predict the degradation trend of key nodes.

[0004] In a further preferred embodiment, in step S1, the sensor data is linked to quickly locate the faulty component and retrieve the digital maintenance record.

[0005] More preferably, in step S2, a blockchain-based collaborative platform records design changes across various disciplines to ensure model version consistency.

[0006] In a further preferred embodiment, in S3, when the assembly error is large, the machine automatically stops for adjustment, production data is synchronously updated to the twin, BIM drives the factory equipment parameters, RFID binds the component identity information, and production progress and logistics status are tracked.

[0007] More preferably, in step S4, the spatial coordinates of the component are obtained through UWB positioning technology during the hoisting stage, and the edge server calculates the deviation between the actual coordinates and the BIM model. If the deviation is greater than the allowable threshold, an adjustment plan is automatically generated.

[0008] In a further preferred embodiment, in S5, AI analyzes historical project data to predict risks, automatically adjusts insurance terms in smart contracts, and dynamically optimizes component production sequencing and logistics scheduling based on blockchain-shared supply chain data.

[0009] In a further preferred embodiment, in S6, the operation and maintenance phase simulates the long-term impact of different maintenance strategies using a digital twin, generates a point cloud model through on-site laser scanning, compares it with the BIM design model in real time, automatically identifies deviations in component installation positions, and generates correction instructions.

[0010] The prefabricated intelligent building management system based on BIM model includes a management terminal, a data sensing unit, a digital twin unit, an intelligent decision-making unit, and an operation and maintenance unit. The management terminal includes a construction management module and a production and logistics module. The data sensing unit includes a 3D modeling and optimization module and a construction simulation and schedule planning module. The digital twin unit includes an autonomous acceptance module. The intelligent decision-making unit includes an intelligent dismantling engine and a spatial conflict prediction module.

[0011] In a further preferred embodiment, the construction management module adopts a visual guidance method for on-site assembly. By scanning the component's QR code with a mobile terminal, the BIM model is retrieved to guide installation and construction errors are compared in real time. The production and logistics module directly connects the BIM design data to the factory control system, driving equipment for precise processing. It combines RFID or QR code tracking of component production status, quality inspection, and inventory information, and monitors transportation location and environmental parameters in real time. The 3D modeling and optimization module supports collaborative modeling of multiple disciplines such as architecture, structure, and MEP. It automatically identifies design conflicts through collision detection and generates optimization solutions to reduce construction changes. The parametric component library supports rapid disassembly and standardized design of prefabricated components. The construction simulation and schedule planning module simulates the hoisting sequence and process connection based on the BIM model, optimizes the construction plan, associates the schedule plan with the 3D model, and dynamically adjusts the schedule. The digital twin unit performs dynamic mapping between the BIM model and the physical building. The autonomous acceptance module automatically detects the fullness of the sleeve grouting during construction through machine vision and generates a 3D deviation cloud map by comparing it with the BIM model.

[0012] In a further preferred embodiment, the intelligent decision-making unit inputs design specifications and transportation constraints through an intelligent disassembly engine and outputs the optimal component segmentation scheme; the spatial conflict prediction module trains a neural network based on historical data to provide early warning of installation collision risks 48 hours in advance; and the operation and maintenance unit quickly locates and retrieves production information and maintenance records through RFID scanning equipment or component tags, automatically generates maintenance plans, and pushes repair plans and spare parts inventory information when a fault occurs.

[0013] Beneficial effects: This invention achieves intelligent and refined prefabricated construction, constructs a BIM-based intelligent construction management system for prefabricated buildings, evaluates the effectiveness of the management system, determines the key control points for different control levels of the intelligent construction management system for prefabricated buildings, clarifies the system environment and system elements of prefabricated buildings, explores the synergistic mechanism between subsystems, determines the characteristic indicators within the system, and determines the intelligent management system through a combination of quantitative and qualitative analysis. It also conducts an importance analysis of the factors affecting effectiveness. BIM technology, by enabling the sharing and collaborative work of design information, avoids information silos and repetitive work in traditional design, improving design efficiency and accuracy. BIM technology provides precise guidance and management for the production and construction of prefabricated buildings, achieving seamless integration from design to construction, and improving the overall efficiency and quality of the project. Attached Figure Description

[0014] Figure 1 This is a flowchart of the prefabricated intelligent building management method based on BIM model according to the present invention; Figure 2 This is a diagram of the prefabricated intelligent building management system based on BIM model according to the present invention; Figure 3 This is a functional module diagram of the prefabricated intelligent building management system based on BIM model according to the present invention.

[0015] In the diagram: 10. Management terminal; 11. Construction management module; 12. Production and logistics module; 20. Data sensing unit; 21. 3D modeling and optimization module; 22. Construction simulation and schedule planning module; 30. Digital twin unit; 31. Autonomous acceptance module; 40. Intelligent decision-making unit; 41. Intelligent dismantling engine; 42. Spatial conflict prediction module; 50. Operation and maintenance unit. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 Please see Figure 1 As shown, this embodiment of the invention provides a management method for prefabricated intelligent buildings based on BIM models, including the following steps: S1. Construct a digital passport to create an NFT digital identity for each prefabricated component, store key information such as material testing reports and production process parameters, and support traceability by scanning codes. S2. Intelligent design collaboration: It adopts a generative design algorithm, inputs land use conditions and functional requirements, and automatically outputs an optimized solution that meets the assembly rate requirements. S3. Adaptive production control: Deploy a visual inspection system in the prefabrication plant to capture the component processing in real time and compare it with BIM model data through CNN; S4. Dynamic construction management: Based on clustering algorithms, the position error threshold is dynamically calculated to improve assembly accuracy. S5, AI and blockchain work together to analyze historical project data to predict risks and dynamically optimize component production sequencing and logistics scheduling. S6. Self-learning operation and maintenance: Use error data accumulated during the construction period to train the LSTM network and predict the degradation trend of key nodes.

[0018] As a technical optimization of the present invention, in S1, the sensor data is linked to quickly locate the faulty component and retrieve the digital maintenance file.

[0019] As a technical optimization of the present invention, in S2, a blockchain-based collaborative platform records design changes of various disciplines to ensure model version consistency.

[0020] As a technical optimization of the present invention, in S3, when the assembly error is large, the machine is automatically stopped for adjustment, the production data is synchronously updated to the twin, the BIM drives the factory equipment parameters, the RFID binds the component identity information, and the production progress and logistics status are tracked.

[0021] As a technical optimization of the present invention, in S4, the spatial coordinates of the component are obtained through UWB positioning technology during the hoisting stage. The edge server calculates the deviation between the actual coordinates and the BIM model. If the deviation is greater than the allowable threshold, an adjustment plan is automatically generated.

[0022] As a technical optimization of the present invention, in S5, AI analyzes historical project data to predict risks, automatically adjusts insurance terms in smart contracts, and dynamically optimizes component production sequencing and logistics scheduling based on blockchain-shared supply chain data.

[0023] As a technical optimization of the present invention, in S6, the long-term impact of different maintenance strategies is simulated by digital twins during the operation and maintenance phase. A point cloud model is generated by on-site laser scanning and compared with the BIM design model in real time to automatically identify the deviation of component installation position and generate correction instructions.

[0024] like Figure 2 and Figure 3 The prefabricated intelligent building management system based on the BIM model shown includes a management terminal 10, a data sensing unit 20, a digital twin unit 30, an intelligent decision-making unit 40, and an operation and maintenance unit 50. The management terminal 10 includes a construction management module 11 and a production and logistics module 12. The data sensing unit 20 includes a 3D modeling and optimization module 21 and a construction simulation and schedule planning module 22. The digital twin unit 30 includes an autonomous acceptance module 31. The intelligent decision-making unit 40 includes an intelligent dismantling engine 41 and a spatial conflict prediction module 42.

[0025] As a technical optimization solution of the present invention, the construction management module 11 adopts a visual guidance method for on-site assembly. By scanning the QR code of the component with a mobile terminal, the BIM model is retrieved to guide the installation and the construction error is compared in real time. The production and logistics module 12 directly connects the BIM design data to the factory control system to drive the equipment to process precisely. Combined with RFID or QR code tracking of component production status, quality inspection and inventory information, the transportation location and environmental parameters are monitored in real time. The 3D modeling and optimization module 21 supports collaborative modeling of multiple disciplines such as architecture, structure, and electromechanical. Through collision detection, it automatically identifies design conflicts and generates optimization solutions to reduce construction changes. The parametric component library supports the rapid disassembly and standardized design of prefabricated components. The construction simulation and schedule planning module 22 simulates the hoisting sequence and process connection based on the BIM model, optimizes the construction plan, associates the schedule plan with the 3D model, and dynamically adjusts the schedule. The digital twin unit 30 performs dynamic mapping between the BIM model and the physical building. The autonomous acceptance module 31 automatically detects the fullness of the sleeve grouting during the construction process through machine vision and generates a 3D deviation cloud map by comparing it with the BIM model.

[0026] As a technical optimization scheme of the present invention, the intelligent decision-making unit 40 inputs design specifications and transportation constraints through the intelligent disassembly engine 41 and outputs the optimal component segmentation scheme. The spatial conflict prediction module 42 trains a neural network based on historical data and provides early warning of installation collision risks 48 hours in advance. The operation and maintenance unit 50 quickly locates and retrieves production information and maintenance records through RFID scanning equipment or component tags, automatically generates maintenance plans, and pushes repair plans and spare parts inventory information when a fault occurs.

[0027] As a technical optimization of the present invention, the data sensing unit 20 collects component coordinates, stress, and environmental data in real time through laser scanning or IoT devices; the intelligent decision-making unit 40 integrates reinforcement learning algorithms to autonomously optimize the hoisting sequence and resource scheduling; and the management terminal 10 is equipped with a blockchain storage module to record the hash values ​​of key processes to ensure that the data is tamper-proof.

[0028] Example 2 This invention also provides a BIM model-based prefabricated intelligent building management method, comprising the following steps: S1. Construct a digital passport to create an NFT digital identity for each prefabricated component, store key information such as material testing reports and production process parameters, support QR code traceability, link sensor data, quickly locate faulty components, and retrieve digital maintenance records. S2. Intelligent design collaboration adopts generative design algorithms. Inputting land use conditions and functional requirements, it automatically outputs optimized solutions that meet assembly rate requirements. The blockchain-based collaborative platform records design changes of various disciplines to ensure model version consistency. S3. Adaptive production control: A visual inspection system is deployed in the prefabrication plant to capture the component processing process in real time. The data is compared with the BIM model data through CNN. When the assembly error is large, the machine will automatically stop to adjust. The production data is updated synchronously to the twin. BIM drives the factory equipment parameters. RFID binds the component identity information to track the production progress and logistics status. S4. Dynamic construction management: Based on clustering algorithm, the position error threshold is dynamically calculated to improve assembly accuracy. During the hoisting stage, the spatial coordinates of the components are obtained through UWB positioning technology. The edge server calculates the deviation between the actual coordinates and the BIM model. If the deviation is greater than the allowable threshold, an adjustment plan is automatically generated. S5, AI and blockchain work together. AI analyzes historical project data to predict risks and automatically adjusts insurance terms in smart contracts. Based on blockchain-shared supply chain data, AI dynamically optimizes component production sequencing and logistics scheduling. S6. Self-learning operation and maintenance: LSTM network is trained using error data accumulated during the construction period to predict the deterioration trend of key nodes. During the operation and maintenance phase, the long-term impact of different maintenance strategies is simulated through digital twins. Point cloud models are generated through on-site laser scanning and compared with BIM design models in real time to automatically identify component installation position deviations and generate correction instructions.

[0029] The prefabricated intelligent building management system based on BIM model includes a management terminal 10, a data sensing unit 20, a digital twin unit 30, an intelligent decision-making unit 40, and an operation and maintenance unit 50. The management terminal 10 includes a construction management module 11 and a production and logistics module 12. The data sensing unit 20 includes a 3D modeling and optimization module 21 and a construction simulation and schedule planning module 22. The digital twin unit 30 includes an autonomous acceptance module 31. The intelligent decision-making unit 40 includes an intelligent dismantling engine 41 and a spatial conflict prediction module 42. The operation and maintenance unit 50 includes an environmental monitoring module, a quality management module, and a safety management module.

[0030] As a technical optimization of the present invention, the management terminal 10 is equipped with a blockchain storage module to record the hash values ​​of key processes and ensure that the data is tamper-proof. The construction management module 11 adopts a visual guidance method for on-site assembly, which allows the BIM model to be retrieved by scanning the component QR code through a mobile terminal to guide the installation and compare construction errors in real time. The production and logistics module 12 connects the BIM design data directly to the factory control system to drive the equipment to process precisely. It combines RFID or QR code to track the component production status, quality inspection and inventory information, and monitors the transportation location and environmental parameters in real time.

[0031] As a technical optimization solution of the present invention, the data sensing unit 20 collects component coordinates, stress, and environmental data in real time through laser scanning or IoT devices. The three-dimensional modeling and optimization module 21 supports collaborative modeling of multiple disciplines such as architecture, structure, and electromechanical systems. It automatically identifies design conflicts and generates optimization solutions through collision detection, reducing construction changes. The parametric component library supports the rapid disassembly and standardized design of prefabricated components. The construction simulation and schedule planning module 22 simulates the hoisting sequence and process connection based on the BIM model, optimizes the construction plan, associates the schedule plan with the three-dimensional model, and dynamically adjusts the construction period.

[0032] As a technical optimization of the present invention, the digital twin unit 30 performs dynamic mapping between the BIM model and the physical building, and the autonomous acceptance module 31 automatically detects the fullness of the sleeve grouting during the construction process through machine vision and generates a three-dimensional deviation cloud map by comparing it with the BIM model.

[0033] As a technical optimization scheme of the present invention, the intelligent decision-making unit 40 inputs design specifications and transportation constraints through the intelligent dismantling engine 41 and outputs the optimal component segmentation scheme. The intelligent decision-making unit 40 integrates reinforcement learning algorithms to autonomously optimize the hoisting sequence and resource scheduling. The spatial conflict prediction module 42 trains a neural network based on historical data to provide early warning of installation collision risks 48 hours in advance.

[0034] As a technical optimization of the present invention, the operation and maintenance unit 50 can quickly locate and retrieve production information and maintenance records through RFID scanning equipment or component tags, automatically generate maintenance plans, and push repair plans and spare parts inventory information when a fault occurs. The environmental monitoring module monitors the temperature, humidity, dust, and harmful gases at the construction site in real time, and triggers audible and visual alarms when abnormalities occur. The quality management module marks checkpoints in the BIM model, records quality evaluations and tracks rectification processes. The safety management module marks dangerous areas, manages safety inspection and training records, and reduces accident risks.

[0035] Working Principle: An NFT digital identity is created for each prefabricated component, storing key information such as material inspection reports and production process parameters. It supports QR code traceability, links sensor data, quickly locates faulty components, and retrieves digital maintenance records. Employing a generative design algorithm, it automatically outputs optimized solutions that meet assembly rate requirements based on input land conditions and functional requirements. A blockchain-based collaborative platform records design changes across disciplines, ensuring model version consistency. A visual inspection system is deployed in the prefabrication plant to capture real-time component processing. CNN is used to compare BIM model data; when assembly errors are large, the system automatically stops for adjustment. Production data is synchronously updated to the BIM twin. BIM drives factory equipment parameters, and RFID binds component identity information to track production progress and logistics status. Clustering algorithms dynamically calculate position error thresholds to improve assembly accuracy. During the hoisting phase, UWB positioning technology is used to obtain the spatial coordinates of components. Edge servers calculate the deviation between the actual coordinates and the BIM model. If the deviation exceeds the allowable threshold, an adjustment plan is automatically generated. AI analyzes historical project data to predict risks and automatically adjusts insurance clauses in smart contracts. Based on blockchain-shared supply chain data, AI dynamically optimizes component production sequencing and logistics scheduling. Error data accumulated during construction is used to train an LSTM network to predict the deterioration trend of key nodes. During the operation and maintenance phase, digital twins are used to simulate the long-term impact of different maintenance strategies. Point cloud models are generated through on-site laser scanning and compared in real time with the BIM design model to automatically identify component installation position deviations and generate correction instructions.

[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A management method for prefabricated intelligent buildings based on BIM models, characterized in that, Includes the following steps: S1. Construct a digital passport to create an NFT digital identity for each prefabricated component, store key information such as material testing reports and production process parameters, and support traceability by scanning codes. S2. Intelligent design collaboration: It adopts a generative design algorithm, inputs land use conditions and functional requirements, and automatically outputs an optimized solution that meets the assembly rate requirements. S3. Adaptive production control: Deploy a visual inspection system in the prefabrication plant to capture the component processing in real time and compare it with BIM model data through CNN; S4. Dynamic construction management: Based on clustering algorithms, the position error threshold is dynamically calculated to improve assembly accuracy. S5, AI and blockchain work together to analyze historical project data to predict risks and dynamically optimize component production sequencing and logistics scheduling. S6. Self-learning operation and maintenance: Use error data accumulated during the construction period to train the LSTM network and predict the degradation trend of key nodes.

2. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S1, the sensor data is linked to quickly locate the faulty component and retrieve the digital maintenance record.

3. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S2, a blockchain-based collaborative platform records design changes across various disciplines to ensure model version consistency.

4. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S3, when the assembly error is large, the machine will automatically stop for adjustment, the production data will be updated synchronously to the twin, the BIM will drive the factory equipment parameters, the RFID will bind the component identity information, and the production progress and logistics status will be tracked.

5. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S4, during the hoisting stage, the spatial coordinates of the components are obtained through UWB positioning technology. The edge server calculates the deviation between the actual coordinates and the BIM model. If the deviation is greater than the allowable threshold, an adjustment plan is automatically generated.

6. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S5, AI analyzes historical project data to predict risks, automatically adjusts insurance terms in smart contracts, and dynamically optimizes component production sequencing and logistics scheduling based on blockchain-shared supply chain data.

7. The prefabricated intelligent building management method based on BIM model as described in claim 1, characterized in that: In S6, the operation and maintenance phase simulates the long-term impact of different maintenance strategies through digital twins, generates point cloud models through on-site laser scanning, compares them with the BIM design model in real time, automatically identifies component installation position deviations, and generates correction instructions.

8. A prefabricated intelligent building management system based on BIM model, characterized in that: It includes a management terminal (10), a data sensing unit (20), a digital twin unit (30), an intelligent decision-making unit (40), and an operation and maintenance unit (50). The management terminal (10) includes a construction management module (11) and a production and logistics module (12). The data sensing unit (20) includes a three-dimensional modeling and optimization module (21) and a construction simulation and schedule planning module (22). The digital twin unit (30) includes an autonomous acceptance module (31). The intelligent decision-making unit (40) includes an intelligent dismantling engine (41) and a spatial conflict prediction module (42).

9. The prefabricated intelligent building management system based on BIM model as described in claim 8, characterized in that: The construction management module (11) adopts a visual guidance method for on-site assembly. By scanning the component QR code with a mobile terminal, the BIM model is retrieved to guide the installation and compare the construction error in real time. The production and logistics module (12) directly connects the BIM design data to the factory control system to drive the equipment to process precisely. Combined with RFID or QR code tracking of component production status, quality inspection and inventory information, the transportation location and environmental parameters are monitored in real time. The three-dimensional modeling and optimization module (21) supports collaborative modeling of multiple disciplines such as architecture, structure, and electromechanical. It automatically identifies design conflicts and generates optimization schemes through collision detection to reduce construction changes. The parameterized component library supports the rapid disassembly and standardized design of prefabricated components. The construction simulation and progress planning module (22) simulates the hoisting sequence and process connection based on the BIM model, optimizes the construction scheme, associates the progress plan with the three-dimensional model, and dynamically adjusts the construction period. The digital twin unit (30) performs dynamic mapping between the BIM model and the physical building. The autonomous acceptance module (31) automatically detects the fullness of the sleeve grouting during the construction process through machine vision and generates a three-dimensional deviation cloud map by comparing it with the BIM model.

10. The prefabricated intelligent building management system based on BIM model as described in claim 8, characterized in that: The intelligent decision-making unit (40) inputs design specifications and transportation constraints through the intelligent disassembly engine (41) and outputs the optimal component segmentation scheme. The spatial conflict prediction module (42) trains a neural network based on historical data and provides an early warning of installation collision risks 48 hours in advance. The operation and maintenance unit (50) quickly locates and retrieves production information and maintenance records through RFID scanning equipment or component tags, automatically generates maintenance plans, and pushes repair plans and spare parts inventory information when a fault occurs.

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