A method for simulating construction of an architectural finish

Abstract

This invention discloses a method for simulating construction of architectural decoration, belonging to the field of architectural decoration construction technology. The method includes: S1, constructing a dynamic digital twin base model that maps in real-time to the construction site through multi-source heterogeneous data acquisition and fusion processing; S2, based on the decoration construction drawings and the digital twin base model; S3, performing error pre-simulation of the design stage, prefabrication production stage, and assembly stage sequentially based on the digital twin simulation model; S4, using optimization algorithms to adaptively optimize and simulate construction procedures under different working conditions; S5, generating differentiated technical briefing content based on the capability profiles of construction personnel; and S6, collecting on-site construction data in real-time and automatically comparing it with the digital twin model. This invention solves the problems in existing technologies such as the disconnect between simulation and dynamic on-site working conditions, insufficient prediction of multi-factor coupling errors, poor process adaptability, and the disconnect between briefing and on-site operation.

Description

Technical Field

[0001] This invention relates to the field of architectural decoration construction technology, specifically to a method for simulating architectural decoration construction. Background Technology

[0002] Construction and decoration engineering is characterized by complex procedures, multiple disciplines involved, high precision requirements, and variable on-site conditions. Traditional construction simulation methods have the following limitations:

[0003] (1) The on-site data collection method is singular, relying mainly on single three-dimensional scanning or manual measurement. The established on-site model is a static model, which cannot reflect dynamic factors such as structural deformation and environmental parameter changes during construction, resulting in time-delay deviation between the simulated base and the actual on-site working conditions.

[0004] (2) Existing simulation models are mostly limited to the digital expression of geometric information and material properties, lacking the ability to simulate material physical deformation, installation mechanical behavior, and the coupling effect of multiple professional systems. It is difficult to predict the deformation risk of decorative components under actual working conditions and the conflict with mechanical, electrical, structural and other disciplines.

[0005] (3) Error control often adopts the post-comparison and adjustment mode, that is, problems are found by comparing the model and drawings after the design is completed. It is difficult to predict the error transmission and cumulative effect of the whole chain of prefabrication, production and assembly in the design stage.

[0006] (4) Construction process simulation mostly adopts a fixed process mode, which makes it difficult to adaptively optimize according to the actual working conditions, schedule constraints and resource conditions on site. It lacks the ability to respond to sudden working conditions and has limited ability to predict the spatiotemporal conflicts of multi-professional cross-operations.

[0007] (5) Technical briefings often use standardized drawings or static 3D displays without considering the differences in technical capabilities of construction teams. The briefing content is not well connected with the actual on-site operation and lacks immersive rehearsal and real-time error correction mechanisms.

[0008] (6) Construction simulations are mostly completed in advance, and there is a lack of real-time data feedback and dynamic model update mechanism during the construction process. The simulation plan is disconnected from the actual construction on site, making it difficult to form a closed-loop management. Summary of the Invention

[0009] (a) Technical problems to be solved

[0010] To address the shortcomings of existing technologies, this invention provides a construction decoration simulation method. By constructing a dynamic digital twin substrate that maps in real time to the physical site and embedding multi-physics field coupling simulation capabilities, it achieves full-chain error pre-simulation and closed-loop correction, adaptive process optimization, differentiated and precise briefing, and real-time iteration of the construction process. This solves the problems in existing technologies such as the disconnect between simulation and dynamic on-site working conditions, insufficient prediction of multi-factor coupling errors, poor process adaptability, and the disconnect between briefing and on-site operation.

[0011] (II) Technical Solution

[0012] To solve the above-mentioned technical problems, the technical solution provided by the present invention is as follows: 1. A method for simulating construction of building decoration, characterized by comprising the following steps:

[0013] S1. Construction of Dynamic Digital Twin Base: Multi-source heterogeneous data collection at the construction site, including geometric data acquisition through 3D laser scanning, and simultaneous collection of dynamic data on structural stress, environmental parameters, and construction progress; the collected data is fused and processed to construct a digital twin base model that is dynamically updated and mapped in real time to the physical entities of the construction site.

[0014] S2. Construction of Multi-Physics Coupled Simulation Model: Based on the decoration construction drawings and the digital twin base model, an initial three-dimensional information model of the decoration project is built, and a material deformation simulation module, an installation stress simulation module and a multi-professional coupled simulation module are embedded in the model to construct a multi-physics coupled decoration digital twin simulation model, which is used to predict the deformation of decoration components, installation risks and conflicts with other disciplines.

[0015] S3. Full-chain error pre-simulation and closed-loop correction: Based on the digital twin simulation model, error pre-simulation is performed sequentially in the design stage, prefabrication stage and assembly stage to quantify the error transmission law and cumulative effect. The model and drawings are iteratively corrected according to the simulation results until the full-chain error is controlled within the preset threshold.

[0016] S4. Adaptive Process Simulation and Conflict Prediction: Import the construction plan parameters into the modified digital twin model, use optimization algorithms to adaptively optimize and simulate the construction process under different working conditions, generate and output the optimal process plan, and perform a visual dynamic simulation of the entire construction process of the plan to predict spatial collisions and safety risks.

[0017] S5. Human-machine collaborative precise briefing: Based on the construction personnel's capability profile, combined with the digital twin model, differentiated technical briefing content is generated, and immersive operation simulation and real-time error correction are carried out through augmented reality / virtual reality technology;

[0018] S6. Real-time closed-loop iteration and dynamic optimization: During the construction process, on-site construction data is collected in real time and transmitted back, and automatically compared with the digital twin model. When deviations occur, early warning and rectification plans are generated, and the construction simulation model is automatically updated based on on-site dynamic data to dynamically adjust subsequent construction plans and procedures.

[0019] As an improvement, the multi-source heterogeneous data in S1 also includes the accuracy verification data of key structural control points and pre-embedded points obtained by total station, the stress, strain, settlement and deformation data of the main structure obtained by structural health sensor, and the on-site temperature, humidity, wind speed and illuminance data obtained by environmental sensor; the fusion processing includes denoising, point cloud registration and spatiotemporal coordinate unification of multi-source data.

[0020] As an improvement, the multiphysics coupled decorative digital twin simulation model described in S2 also includes:

[0021] Material Deformation Simulation Module: Based on the constitutive properties of materials and on-site environmental data, this module simulates the deformation patterns of decorative components under different working conditions.

[0022] Installation stress simulation module: Simulates anchoring stress, splicing stress and hoisting deformation during the installation of decorative components;

[0023] Multidisciplinary Coupled Simulation Module: Combining computational fluid dynamics simulation, acoustic simulation, and optical simulation, it verifies the indoor wind environment, acoustic environment, and light environment of the decoration scheme, and predicts the resonance and thermal expansion and contraction conflicts between the decoration components and the electromechanical system.

[0024] As an improvement, the full-chain error pre-simulation and closed-loop correction described in S3 specifically include:

[0025] Design phase error pre-simulation: The decoration information model is compared with the digital twin base model in terms of all elements such as geometric dimensions, structural deformation, and pipeline routing, in order to eliminate systematic deviations in the design phase;

[0026] Prefabrication production error pre-simulation: Based on component design parameters and factory prefabrication process capabilities, simulate component production deviations to optimize component breakdown design and tolerance allocation;

[0027] Assembly process error pre-simulation: The cumulative assembly error of single component units, node assembly and overall decoration system is simulated in stages, and the assembly sequence and node adjustment margin are optimized by combining the simulation results of material deformation and installation stress.

[0028] As an improvement, S4 describes the use of optimization algorithms to adaptively optimize and simulate construction procedures under different working conditions. Specifically, it includes: based on genetic algorithms and reinforcement learning models, automatically generating multiple alternative procedure schemes according to on-site working conditions, schedule requirements, and cross-operation situations, and simulating the schedule, cost, safety risks, and conflict situations under each scheme to output the comprehensive optimal procedure scheme.

[0029] As an improvement, the adaptive process simulation and conflict pre-simulation described in S4 also includes: conducting emergency construction simulations and generating corresponding process adjustment plans for extreme working conditions such as component delays, personnel adjustments, sudden environmental changes, or design changes.

[0030] As an improvement, the precise human-machine collaborative handover described in S5 specifically includes:

[0031] A capability profile of construction personnel is constructed based on the personnel configuration, technical capabilities, and historical construction quality data of construction teams.

[0032] Based on the different capabilities of different work teams, differentiated technical briefing content is generated, and high-difficulty processes and complex nodes are broken down and explained.

[0033] By using augmented reality overlay technology, a digital twin model is superimposed on the actual construction site in a 1:1 ratio. The entire process is simulated and rehearsed through a virtual reality system, allowing for real-time prediction and correction of operational deviations.

[0034] As an improvement, S5 also includes: after the briefing is completed, the construction personnel’s mastery is verified through online theoretical assessment and simulated operation evaluation, forming a closed-loop briefing system of “briefing-simulation-evaluation-re-briefing”.

[0035] As an improvement, the real-time acquisition of on-site construction data described in S6 specifically includes collecting construction data of completed processes, component installation accuracy data, on-site environmental data, and construction progress data through on-site 3D scanning, IoT sensors, and smart wearable devices.

[0036] As an improvement, the method further includes:

[0037] S7. As-built digital twin delivery and operation and maintenance simulation: After all construction is completed, the completion acceptance data, the whole process construction data and component parameters are integrated into the digital twin simulation model to form an as-built digital twin model and deliver it; based on the as-built model, the operation and maintenance simulation of the entire life cycle of building decoration is carried out, including the simulation of wear and aging of decorative components, the calculation of maintenance cycle and the pre-simulation of renovation and transformation scheme.

[0038] (III) Beneficial Effects

[0039] The advantages of this invention compared to the prior art are:

[0040] (1) Through the fusion and dynamic update mechanism of multi-source heterogeneous data, the digital twin base model can synchronously reflect the geometric state, structural deformation and environmental parameter changes of the construction site, providing a data foundation for subsequent simulation and real-time mapping with the physical site, and reducing decision-making bias caused by model time delay.

[0041] (2) By embedding simulation modules for material deformation, installation stress and multi-disciplinary coupling, the simulation model has the ability to predict the physical behavior of decorative components and the interaction of multiple systems. It can identify component deformation risks, installation mechanical risks and conflicts with electromechanical disciplines before construction, thereby reducing the rework rate.

[0042] (3) By pre-simulating and correcting the error of the whole chain, the error control is moved forward to the design stage, the error transmission law of each link of design, prefabrication and assembly is quantified, and the error of the whole chain is made controllable before construction through iterative correction, thereby reducing on-site installation deviation.

[0043] (4) Through the adaptive process optimization algorithm, the appropriate process plan can be automatically generated according to the on-site working conditions and resource constraints, and emergency plans can be generated for extreme working conditions, thereby improving the adaptability of the construction plan to changes on the site and reducing the time and space conflicts of multi-professional cross-operations.

[0044] (5) By profiling the capabilities of construction personnel and providing differentiated briefings, the briefing content is matched with the technical capabilities of the work team. Combined with AR / VR immersive pre-shows and real-time error correction, construction quality problems caused by operational misunderstandings are reduced.

[0045] (6) By collecting real-time data during the construction process and updating the model dynamically, a closed-loop iteration between the simulation scheme and the on-site construction can be achieved, so that the subsequent construction plan can be dynamically adjusted based on the actual deviation on site, and the consistency between simulation and construction can be maintained. Attached Figure Description

[0046] Figure 1 This is a flowchart of a construction decoration simulation construction method according to the present invention. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example 1

[0048] Taking the interior decoration project of a large commercial complex as an example, the specific implementation of the present invention is described, such as... Figure 1 As shown:

[0049] S1, Dynamic Digital Twin Foundation Construction

[0050] During the project commencement phase, a 3D laser scanner was used to perform a full-section scan of the construction site, acquiring high-density point cloud data of the main building structure, completed civil engineering works, electromechanical pipelines, and pre-installed / embedded structures. Simultaneously, a total station was used to verify the accuracy of key structural control points and pre-installed locations, ensuring the absolute accuracy of the point cloud data.

[0051] Stress and strain sensors and settlement observation points are installed at key structural locations to collect real-time data on stress, strain, and settlement deformation of the main structure; an environmental sensor network is deployed at the construction site to collect environmental parameters such as temperature, humidity, wind speed, and illuminance; and data from previous construction procedures, material testing, and construction plans of various professional subcontractors are also integrated.

[0052] The collected multi-source data underwent denoising processing, and the ICP algorithm was used for point cloud registration to unify the spatiotemporal coordinate system, constructing a digital twin base model that includes static geometric information, dynamic structural deformation data, and environmental parameters. This model is updated in real time through a cloud-edge collaborative architecture. The field-collected data is preprocessed by edge computing nodes and then uploaded to the cloud platform, and the model is dynamically updated as construction progresses.

[0053] S2. Construction of Multiphysics Coupled Simulation Model

[0054] Based on the decoration construction drawings and the digital twin base model constructed by S1, the initial three-dimensional information model of the decoration project is built using BIM technology. The model includes the geometric dimensions, material parameters, connection nodes and assembly relationships of the decoration components.

[0055] Embed a multiphysics simulation module based on the initial model:

[0056] Material Deformation Simulation Module: For different materials such as wood veneer, stone, and metal sheets, input their constitutive property parameters, combine them with on-site temperature and humidity monitoring data, simulate the deformation law of decorative components under different environmental conditions, and output the predicted value of the dimensional deviation after the component is installed.

[0057] Installation stress simulation module: Establishes mechanical models of component anchoring, splicing, and hoisting, simulates stress distribution and deformation during installation, and identifies stress concentration areas and the risk of deformation exceeding standards.

[0058] Multidisciplinary coupled simulation module: Coupled computational fluid dynamics (CFD) model, acoustic simulation model and optical simulation model to verify the impact of decoration scheme on indoor wind environment, sound environment and light environment; At the same time, establish thermal-structural coupling model of decoration components and electromechanical system to predict the dimensional conflict and resonance risk caused by thermal expansion and contraction.

[0059] S3, Full-chain error pre-simulation and closed-loop correction

[0060] Based on the simulation model built using S2, conduct full-chain error pre-simulation:

[0061] Design phase: The decoration information model is compared with the digital twin base model in all aspects, including geometric dimensions, structural deformation, pipeline routing deviation, systematic deviation between design drawings and on-site entities, and the decoration model and construction drawings are optimized.

[0062] Prefabrication stage: Based on component design parameters and factory equipment accuracy data, simulate dimensional deviations and hole position deviations during component production, optimize component disassembly schemes and tolerance allocation strategies, and formulate component production accuracy control standards.

[0063] Assembly stage: Conduct staged simulations of single component unit installation errors, node assembly errors, and overall system assembly cumulative errors. Combine the simulation results of material deformation and installation stress to optimize the assembly sequence and node adjustment margin design.

[0064] After the first round of simulation, a second multiphysics coupling simulation is performed to verify the model. If there are still deviations exceeding the threshold, iterative corrections are repeated until the error across the entire chain meets the preset control requirements. Finally, through 1:1 physical assembly testing or 3D printing node verification, the measured data is fed back to correct the simulation parameters, forming a locked construction digital twin model.

[0065] S4, Adaptive Process Simulation and Conflict Pre-simulation

[0066] The construction schedule, labor allocation, team technical capability rating, component arrival plan, material arrival plan, and on-site environmental constraints are imported into a locked digital twin model to construct a 4D construction simulation environment.

[0067] An initial process scheme population is generated based on a genetic algorithm. The fitness of each scheme under multiple objectives such as schedule, cost, safety risk and cross-operation conflict is evaluated by a reinforcement learning model. After iterative optimization, the comprehensive optimal process scheme is output.

[0068] For complex areas such as the central atrium of the commercial complex, the focus was on simulating the spatial and temporal conflicts of multi-disciplinary cross-operations to optimize the division of work areas and the rhythm of work processes. Emergency construction simulations were conducted to address unforeseen circumstances such as delayed component arrivals and extreme weather, generating contingency plans for process adjustments.

[0069] Based on the optimal process plan, a visual dynamic simulation of the entire construction process is carried out to predict risks such as hoisting path conflicts, insufficient personnel operating space, and blind spots in safety protection. After targeted optimization, the final plan is synchronized to the digital twin cloud platform.

[0070] S5, Human-Machine Collaborative Precise Disclosure

[0071] Based on the personnel configuration, technical level certificates, and historical construction quality inspection data of construction teams, a capability profile of construction personnel is established. For teams with strong technical capabilities, the briefing content focuses on the requirements for precision control of key nodes; for teams with less experience, basic operation points and warnings of common errors are added.

[0072] Using AR glasses, a digital twin model is overlaid on the actual construction site at a 1:1 scale, intuitively displaying the component installation location, node structure, and precision requirements. A VR system allows construction personnel to simulate the entire process of component hoisting, positioning, splicing, and anchoring, with the system providing real-time corrections for any operational deviations.

[0073] For complex details such as the curved surface design of the atrium, standardized operation animations were created for specific training. After the training, online theoretical assessments and VR simulation operation evaluations were conducted. Those who did not meet the standards were required to undergo a second training session, forming a closed-loop training record.

[0074] S6, Real-time Closed-Loop Iteration and Dynamic Optimization

[0075] During construction, portable 3D scanning equipment is used to perform precision testing on installed components, IoT sensors are used to monitor on-site environmental parameters, and wearable devices such as smart safety helmets are used to collect construction progress data, which is then transmitted back to the digital twin cloud platform in real time.

[0076] The platform automatically compares the on-site measured data with the model data. When the component installation deviation exceeds the allowable threshold, it generates a deviation warning and rectification plan, and simulates the impact of the deviation on subsequent processes to adjust the subsequent construction accuracy control requirements.

[0077] When design changes or schedule delays occur on-site, the digital twin model is automatically updated and a new process optimization simulation is conducted to dynamically adjust the construction plan and ensure that the simulation plan is consistent with the actual situation on site.

[0078] S7, As-built Digital Twin Delivery and Operation Simulation

[0079] After the project is completed, the acceptance and testing data, the entire construction record, and the measured parameters of the components will be integrated into the digital twin model to form an as-built digital twin model that is consistent with the completed entity and delivered to the construction unit.

[0080] Operation and maintenance phase simulation based on as-built model: establish a wear and aging prediction model for decorative components, calculate the maintenance cycle of different components; simulate repair and replacement procedures, and rehearse renovation and transformation plans to provide data support for later operation and maintenance decisions.

[0081] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for simulating construction of architectural decoration, characterized in that: Includes the following steps: S1. Construction of Dynamic Digital Twin Base: Multi-source heterogeneous data collection at the construction site, including geometric data acquisition through 3D laser scanning, and simultaneous collection of dynamic data on structural stress, environmental parameters, and construction progress; the collected data is fused and processed to construct a digital twin base model that is dynamically updated and mapped in real time to the physical entities of the construction site. S2. Construction of Multi-Physics Coupled Simulation Model: Based on the decoration construction drawings and the digital twin base model, an initial three-dimensional information model of the decoration project is built, and a material deformation simulation module, an installation stress simulation module and a multi-professional coupled simulation module are embedded in the model to construct a multi-physics coupled decoration digital twin simulation model, which is used to predict the deformation of decoration components, installation risks and conflicts with other disciplines. S3. Full-chain error pre-simulation and closed-loop correction: Based on the digital twin simulation model, error pre-simulation is performed sequentially in the design stage, prefabrication stage and assembly stage to quantify the error transmission law and cumulative effect. The model and drawings are iteratively corrected according to the simulation results until the full-chain error is controlled within the preset threshold. S4. Adaptive Process Simulation and Conflict Prediction: Import the construction plan parameters into the modified digital twin model, use optimization algorithms to adaptively optimize and simulate the construction process under different working conditions, generate and output the optimal process plan, and perform a visual dynamic simulation of the entire construction process of the plan to predict spatial collisions and safety risks. S5. Human-machine collaborative precise briefing: Based on the construction personnel's capability profile, combined with the digital twin model, differentiated technical briefing content is generated, and immersive operation simulation and real-time error correction are carried out through augmented reality / virtual reality technology; S6. Real-time closed-loop iteration and dynamic optimization: During the construction process, on-site construction data is collected in real time and transmitted back, and automatically compared with the digital twin model. When deviations occur, early warning and rectification plans are generated, and the construction simulation model is automatically updated based on on-site dynamic data to dynamically adjust subsequent construction plans and procedures.

2. The architectural decoration simulation construction method according to claim 1, characterized in that, The multi-source heterogeneous data mentioned in S1 also includes the accuracy verification data of key structural control points and pre-embedded points obtained by total station, the stress, strain, settlement and deformation data of the main structure obtained by structural health sensor, and the on-site temperature, humidity, wind speed and illuminance data obtained by environmental sensor; the fusion processing includes denoising, point cloud registration and spatiotemporal coordinate unification of multi-source data.

3. The method for simulating architectural decoration construction according to claim 1, characterized in that, The multiphysics coupled decorative digital twin simulation model described in S2 also includes: Material Deformation Simulation Module: Based on the constitutive properties of materials and on-site environmental data, this module simulates the deformation patterns of decorative components under different working conditions. Installation stress simulation module: Simulates anchoring stress, splicing stress and hoisting deformation during the installation of decorative components; Multidisciplinary Coupled Simulation Module: Combining computational fluid dynamics simulation, acoustic simulation, and optical simulation, it verifies the indoor wind environment, acoustic environment, and light environment of the decoration scheme, and predicts the resonance and thermal expansion and contraction conflicts between the decoration components and the electromechanical system.

4. The architectural decoration simulation construction method according to claim 1, characterized in that, The full-chain error pre-simulation and closed-loop correction described in S3 specifically include: Design phase error pre-simulation: The decoration information model is compared with the digital twin base model in terms of all elements such as geometric dimensions, structural deformation, and pipeline routing, in order to eliminate systematic deviations in the design phase; Prefabrication production error pre-simulation: Based on component design parameters and factory prefabrication process capabilities, simulate component production deviations to optimize component breakdown design and tolerance allocation; Assembly process error pre-simulation: The cumulative assembly error of single component units, node assembly and overall decoration system is simulated in stages, and the assembly sequence and node adjustment margin are optimized by combining the simulation results of material deformation and installation stress.

5. The method for simulating architectural decoration construction according to claim 1, characterized in that, The optimization algorithm described in S4 is used to adaptively optimize and simulate construction procedures under different working conditions. Specifically, it includes: based on genetic algorithms and reinforcement learning models, automatically generating multiple alternative procedure schemes according to on-site working conditions, schedule requirements and cross-operation situations, and simulating the schedule, cost, safety risks and conflict situations under each scheme, so as to output the comprehensive optimal procedure scheme.

6. The method for simulating architectural decoration construction according to claim 1, characterized in that, The adaptive process simulation and conflict rehearsal described in S4 also include: conducting emergency construction simulations and generating corresponding process adjustment plans for extreme working conditions such as component delays, personnel adjustments, sudden environmental changes, or design changes.

7. The architectural decoration simulation construction method according to claim 1, characterized in that, The precise human-machine collaborative handover described in S5 specifically includes: A capability profile of construction personnel is constructed based on the personnel configuration, technical capabilities, and historical construction quality data of construction teams. Based on the different capabilities of different work teams, differentiated technical briefing content is generated, and high-difficulty processes and complex nodes are broken down and explained. By using augmented reality overlay technology, a digital twin model is superimposed on the actual construction site in a 1:1 ratio. The entire process is simulated and rehearsed through a virtual reality system, allowing for real-time prediction and correction of operational deviations.

8. The architectural decoration simulation construction method according to claim 7, characterized in that, S5 also includes: after the briefing is completed, verifying the construction personnel's mastery through online theoretical assessment and simulated operation evaluation, forming a closed-loop briefing system of "briefing-simulation-evaluation-re-briefing".

9. The architectural decoration simulation construction method according to claim 1, characterized in that, The real-time acquisition of on-site construction data described in S6 specifically includes collecting construction data of completed processes, component installation accuracy data, on-site environmental data, and construction progress data through on-site 3D scanning, IoT sensors, and smart wearable devices.

10. The method for simulating architectural decoration construction according to claim 1, characterized in that, The method further includes: S7. As-built digital twin delivery and operation and maintenance simulation: After all construction is completed, the completion acceptance data, the whole process construction data and component parameters are integrated into the digital twin simulation model to form an as-built digital twin model and deliver it; based on the as-built model, the operation and maintenance simulation of the entire life cycle of building decoration is carried out, including the simulation of wear and aging of decorative components, the calculation of maintenance cycle and the pre-simulation of renovation and transformation scheme.

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