Building information intelligent management method and system based on BIM technology

Abstract

The invention relates to a building information intelligent management method and system based on a BIM technology, and the method comprises the steps: intelligently analyzing a user demand into a standardized parameter set, and generating an initial BIM model through combining with a rule engine; model parameters are automatically adjusted through parameter matching and an AI rule engine, and a standard compliance BIM model is output; performing structure, energy consumption and space utilization rate simulation analysis on the compliance model to generate a performance report containing a normalized deviation value; taking the deviation value as deep Q network state input, iteratively optimizing parameters to reach the standard through a predefined action space, and outputting a performance optimization BIM model; construction and operation and maintenance data are obtained in real time, digital twin comparison with the optimization model is carried out, deep Q network secondary optimization is triggered, a dynamic update BIM data flow is generated, and all stages of the whole life cycle are synchronized, so that closed-loop management of building whole life cycle dynamic management and control is achieved, and the design efficiency, the engineering compliance and the cross-stage cooperation capability are improved.

Description

Technical Field

[0001] The present invention relates to the field of building information technology, and particularly to a building information intelligent management method and system based on BIM technology. Background Art

[0002] With the acceleration of global urbanization and infrastructure construction, the construction industry faces both opportunities and challenges. As the project scale becomes larger and the functions become more complex, society's expectations for building quality, sustainability, and operational efficiency have increased, making the traditional working mode difficult to adapt. For example, during requirement docking, due to the lack of systematic and intelligent conversion tools and processes, the translation of user requirements is prone to errors, resulting in a mismatch between the design and the owner's expectations, and repeated modifications are required, which not only prolongs the project cycle but also wastes resources, restricting the allocation of industry resources.

[0003] At the same time, the industry's technical standards and specifications are constantly updated, and design compliance has become a difficult problem. It is difficult to integrate the specifications into the design process, and designers need to make manual adjustments, which affects efficiency and is difficult to ensure compliance, posing potential hazards to project implementation. In building performance evaluation, traditional simulation technologies are lagging and one-sided, making it difficult to accurately evaluate core indicators such as structural stability, energy consumption, and space utilization rate. There is a lack of data basis for design optimization, and it is impossible to effectively improve building performance. In addition, during the construction and operation and maintenance stages, the information interaction among the participating parties is not smooth, and there is also a lack of a real-time feedback mechanism between the physical building data and the design model, making it difficult to optimize based on on-site and operation and maintenance data, disrupting the coherence of the whole life cycle information, and restricting the improvement of industry efficiency and sustainable development.

[0004] In the existing technology, natural language processing (NLP) requirement conversion, dynamic specification adaptation, performance simulation, and digital twin technologies are mostly applied separately. For example, CN118012894A discloses a BIM parameter generation method based on NLP, but it does not solve the problems of real-time update of specifications and dynamic compliance of the model. In this context, constructing a BIM technology system integrating requirement conversion, model optimization, performance simulation, and information interaction is an effective attempt for the construction industry to break through the existing development bottlenecks and transform and upgrade to a higher level. Summary of the Invention

[0005] The main object of the present invention is to provide a building information intelligent management method and system based on BIM technology to achieve the full-process intelligent control of building information from intelligent parsing of user requirements, real-time compliance of design specifications, multi-objective performance independent optimization to full life cycle dynamic closed-loop management, and to improve the design efficiency, engineering compliance, and resource utilization efficiency.

[0006] To achieve the above object, the present invention provides a building information intelligent management method based on BIM technology, including the following steps: Convert user requirements into a standardized parameter set through a preset mapping table that supports online updates and natural language processing technology, and generate an initial BIM model in combination with a rule engine; Based on a dynamically updated design specification database, automatically adjust the parameters of the initial BIM model through parameter matching and an AI rule engine, and output a specification-compliant BIM model; Perform simulation calculations on the structural stability, energy consumption distribution, and space utilization rate of the specification-compliant BIM model to generate a performance analysis report; Use the normalized deviation value in the performance analysis report as the state input of the deep Q network, output optimization decisions through a predefined action space, iteratively adjust the parameters until the threshold is met, and output a performance-optimized BIM model; Obtain physical building data in the construction and operation and maintenance phases in real time through the Internet of Things interface, perform digital twin comparison with the performance-optimized BIM model, calculate parameter deviations and trigger secondary optimization of the deep Q network, generate a dynamically updated BIM data stream, and synchronize it to all stages of the full life cycle.

[0007] Further, the step of converting user requirements into a standardized parameter set through a preset mapping table that supports online updates and natural language processing technology, and generating an initial BIM model in combination with a rule engine includes: Analyze the natural language requirements input by the user, and extract keywords and constraint conditions; Map the keywords to BIM parameter types and value ranges through a preset mapping table, and the mapping table contains the triple relationship of user requirement semantics, BIM parameters, and associated components; If there is no matching item in the mapping table, trigger an online update request and expand the mapping table entries based on user feedback; Use a rule engine to generate the geometric structure and attribute labels of the initial BIM model according to parameter priorities and spatial topology rules.

[0008] Further, the step of automatically adjusting the parameters of the initial BIM model through parameter matching and an AI rule engine based on a dynamically updated design specification database, and outputting a specification-compliant BIM model includes: Grab the latest building specification text from an authoritative specification release platform through a data interface, extract the specification parameter requirements, and structurally store them in the design specification database; Based on the component types and spatial attributes of the initial BIM model, screen the applicable clause sets from the design specification database; Based on the selected usage clause sets, compare the model parameters with the specification thresholds item by item through a parameter matching algorithm to identify non-compliant item parameters; Call the preset replacement strategy through the AI rule engine to automatically correct non-compliant parameters and generate a standardized and compliant BIM model with adjustment logs.

[0009] Further, the step of performing simulation calculations on the structural stability, energy consumption distribution, and space utilization rate of the standardized and compliant BIM model to generate a performance analysis report includes: Perform structural stability analysis by the finite element method, calculate the member stress ratio and overall displacement angle in the standardized and compliant BIM model, and identify the over-limit areas; Perform energy consumption simulation based on the standardized and compliant BIM model, perform hourly load calculations throughout the year through the EnergyPlus engine, and output the peak heating and cooling loads and energy consumption density distribution; Evaluate the space utilization rate of the standardized and compliant BIM model, analyze the efficiency of the pedestrian flow path using space syntax, and calculate the space utilization rate index; Normalize the over-limit areas, the peak heating and cooling loads, the energy consumption density distribution, and the space utilization rate index into deviation rates, and generate a performance analysis report including component correlation marks.

[0010] Further, the step of using the normalized deviation values in the performance analysis report as the state input of the deep Q-network, outputting optimization decisions through the predefined action space, iteratively adjusting the parameters until the threshold is met, and outputting the performance-optimized BIM model includes: Classify and encode the structural stability deviation, energy consumption deviation, and space utilization rate deviation in the performance analysis report according to parameter types, and use them as the state input vector of the deep reinforcement learning network; Define the output action of the deep reinforcement learning network as a set of BIM model parameter adjustment operations, including component geometric dimension adjustment, material property replacement, and space topology relationship optimization; Configure the network training reward mechanism to give positive rewards when the model parameters meet the performance threshold, give partial rewards when the optimized deviation decreases, and give penalties when structural or energy consumption constraints are violated; Select optimization actions through the exploration-exploitation balance strategy, iteratively adjust the BIM model parameters and re-perform performance simulation until the model parameters converge after continuous optimization for multiple times.

[0011] Further, the step of obtaining real-time physical building data during the construction and operation and maintenance phases through the Internet of Things interface, performing digital twin comparison with the performance-optimized BIM model, calculating parameter deviations and triggering secondary optimization of the deep Q-network, generating a dynamically updated BIM data stream, and synchronizing it to all phases of the entire life cycle includes: Obtain the three-dimensional point cloud data of the actual construction components through laser scanning, conduct position deviation analysis with the theoretical coordinates of the corresponding components in the performance-optimized BIM model, and calculate the construction deviation ratio. Collect the operation data of building equipment in real time, including air-conditioning energy consumption, lighting power, and elevator usage frequency, and conduct energy consumption and performance deviation analysis with the predicted values in the performance-optimized BIM model. When the construction deviation ratio or the operation and maintenance energy consumption deviation ratio exceeds the preset threshold, automatically trigger the secondary optimization of the deep Q network, generate a dynamically updated BIM data stream containing parameter adjustment records, and the BIM data stream is marked with version numbers, change times, and impact scope labels. Distribute the dynamically updated BIM data stream to the design, construction, and operation and maintenance ends, respectively drive design review, construction plan adjustment, and equipment parameter calibration, and conduct full life cycle management.

[0012] Furthermore, based on the BIM data stream, monitor the model operation performance through a real-time feedback system. If there are deviations, trigger the deep Q network to optimize again, dynamically update the BIM model and iterate, including: Deploy performance monitoring agents in the operation and maintenance stage, collect building operation data in real time and calculate key indicators. If the indicators in three consecutive monitoring cycles exceed 20% of the predicted values of the BIM model, it is determined as a performance deviation. Trigger the deep Q network to take the current operation and maintenance data as input and re-optimize the model parameters. Mark the updated BIM model version and synchronize it to the full life cycle data stream to form a closed-loop management.

[0013] The present invention also provides a building information intelligent management system based on BIM technology, including: A requirements modeling unit for converting user requirements into a standardized parameter set through a preset mapping table supporting online updates and natural language processing technology, and generating an initial BIM model in combination with a rule engine. A specification adaptation unit for automatically adjusting the parameters of the initial BIM model based on a dynamically updated design specification database through parameter matching and an AI rule engine, and outputting a specification-compliant BIM model. A performance simulation unit for performing simulation calculations on the structural stability, energy consumption distribution, and space utilization rate of the specification-compliant BIM model, and generating a performance analysis report. A model optimization unit for taking the normalized deviation values in the performance analysis report as the state input of the deep Q network, outputting optimization decisions through a predefined action space, iteratively adjusting parameters until the threshold is met, and outputting a performance-optimized BIM model. The twin update unit is used to obtain physical building data in the construction and operation and maintenance stages in real time through the Internet of Things interface, perform digital twin comparison with the performance optimization BIM model, calculate parameter deviations and trigger secondary optimization of the deep Q network, generate dynamically updated BIM data streams, and synchronize them to all stages of the entire life cycle.

[0014] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned building information intelligent management method based on BIM technology are implemented.

[0015] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned building information intelligent management method based on BIM technology are implemented.

[0016] The building information intelligent management method and system based on BIM technology provided by the present invention have the following beneficial effects: Through natural language processing and dynamic mapping tables, the present invention realizes the accurate conversion of unstructured user requirements into standardized BIM parameters, solves the problems of low efficiency and easy deviation in traditional manual interpretation, and improves the accuracy and efficiency of modeling; Based on a dynamically updated specification database and an AI rule engine, it can identify and correct model parameters in real time to ensure that the design complies with the latest standards throughout the process and avoid the risk of omission in manual review. By using a deep reinforcement learning network to replace manual trial and error and a multi-objective collaborative optimization mechanism, it balances energy consumption and space efficiency on the premise of ensuring structural safety and shortens the optimization cycle. The present invention integrates NLP requirement parsing, AI rule engine specification adaptation, multi-objective performance simulation, deep reinforcement learning (DQN) optimization, and digital twin feedback into a closed-loop system. For example, after the laser scan data in the construction stage triggers the secondary optimization of DQN, the updated BIM model directly drives the calibration of the operation and maintenance equipment parameters, realizing seamless data linkage of "design-construction-operation and maintenance", and solving the defect that traditional single-point optimization cannot adapt to dynamic changes. By integrating unstructured user requirements (natural language), structured specification libraries (dynamically captured), physical building data (Internet of Things), and reinforcement learning strategies, an adaptive decision-making network is formed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the building information intelligent management method based on BIM technology in an embodiment of the present invention; Figure 2 is a structural block diagram of the building information intelligent management system based on BIM technology in an embodiment of the present invention; Figure 3 is a structural schematic diagram of a computer device in an embodiment of the present invention.

[0018] The realization, functional features and advantages of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Referring to Figure 1 , which is a schematic flowchart of a building information intelligent management method based on BIM technology proposed by the present invention, including the following steps: S1. By means of a preset mapping table supporting online update and natural language processing technology, convert user requirements into a standardized parameter set, and generate an initial BIM model in combination with a rule engine; S2. Based on a dynamically updated design specification database, automatically adjust the parameters of the initial BIM model through parameter matching and an AI rule engine, and output a specification-compliant BIM model; S3. Perform simulation calculations on the structural stability, energy consumption distribution and space utilization rate of the specification-compliant BIM model to generate a performance analysis report; S4. Take the normalized deviation value in the performance analysis report as the state input of the deep Q network, output an optimization decision through a predefined action space, iteratively adjust the parameters until the threshold is met, and output a performance-optimized BIM model; S5. Real-time obtain physical building data in the construction and operation and maintenance stages through an Internet of Things interface, perform digital twin comparison with the performance-optimized BIM model, calculate parameter deviations and trigger secondary optimization of the deep Q network, generate a dynamically updated BIM data stream, and synchronize it to all stages of the entire life cycle.

[0021] As described in the above step S1, parse the natural language requirements input by the user and extract keywords and constraints. Use natural language processing techniques (such as fine-tuning the BERT-Base model, with a training set containing 100,000 pieces of text in the construction field, such as specifications, tender documents, etc., a cross-entropy loss function, a learning rate of 2e-5, and combining with the BiLSTM-CRF model to achieve entity recognition of component types and material attributes) to parse the unstructured requirements input by the user. When using the BERT model to parse the unstructured requirements input by the user, fine-tune the BERT model with a domain-specific corpus to improve the understanding ability of complex sentence patterns and professional terms. The domain-specific corpus collects a large number of text materials related to the construction industry, including design specifications, project documents, etc., which can enable the BERT model to more accurately extract keywords and constraints. For example, for complex requirement descriptions such as "This building should meet the three-star green building standard, and adopt prefabricated building technology, and the assembly rate of precast components is not less than 70%", the fine-tuned BERT model can accurately identify key information such as "three-star green building standard", "prefabricated building technology", and "precast component assembly rate ≥ 70%". At the same time, introduce semantic role labeling (SRL) technology to further clarify the semantic relationships between entities in the requirements. For example, in "High-rise office buildings need to meet the first-class fire protection standard, and the area of each fire compartment does not exceed 1500 square meters", through SRL technology, it can be marked that "high-rise office buildings" are the main body, and "first-class fire protection standard" and "the area of each fire compartment does not exceed 1500 square meters" are descriptions of the attributes and restrictions of the main body, helping the system to more deeply understand the user's requirements.

[0022] Map the keyword to the BIM parameter type and value range through a preset mapping table, where the mapping table contains the triple relationship of user requirement semantics, BIM parameters, and associated components; if there is no matching item in the mapping table, trigger an online update request and expand the mapping table entries based on user feedback. Among them, the preset mapping table is stored in the form of a knowledge graph and also uses a distributed storage technology to disperse the mapping table data on multiple nodes, improving the data reading speed and the fault tolerance of the system. When the system triggers the online update mechanism, to ensure the accuracy and standardization of the newly added entries, an audit process is introduced. After the administrator or user submits the newly added entries, the system automatically performs preliminary format and logic checks, and then the domain experts conduct the review. Only the entries that pass the review can be officially added to the mapping table. For example, for the requirements related to new green building materials, if the user submits a new material parameter and the corresponding BIM parameter mapping relationship, the reviewers will check its rationality and accuracy based on industry standards and actual engineering experience to avoid incorrect data from entering the mapping table and affecting subsequent modeling work. Among them, the dynamic expansion process of the mapping table triples includes that when the user inputs an unmatched requirement (such as "carbon fiber reinforced concrete exterior wall"), the system extracts the keywords "carbon fiber" and "exterior wall" through the fine-tuned BERT model, calls the Wikidata API to verify that the material compressive strength ≥ 50 MPa and the fire resistance rating is A1, and associates the component type "Structural Wall". Subsequently, based on the Neo4j graph database, a consistency check is performed. If there is no conflict with the existing entries (such as no conflict with the fire protection code), a new triple (requirement semantics: high-strength exterior wall, BIM parameter: Material.Strength = 55 MPa, associated component: Structural Wall) is added, and the version number is marked as V2.1.3 and takes effect immediately. The historical update records are traceable and support version rollback.

[0023] Use a rules engine to generate the geometric structure and attribute tags of the initial BIM model according to parameter priorities and spatial topology rules. When calling the Revit API to generate the initial BIM model based on parameter priorities and spatial topology rules, a conflict resolution algorithm is adopted to better handle conflicts between parameters. When the settings of multiple parameters conflict with each other, the algorithm processes them according to the preset priorities and logical rules. For example, when setting the floor height, it is necessary to meet the building function requirements (such as the net height requirements of office spaces) and also consider the structural design specifications (such as the impact of beam height on floor net height). The conflict resolution algorithm will comprehensively weigh these factors, prioritize meeting the requirements of more important parameters, and at the same time adjust other relevant parameters to balance various needs as much as possible. In addition, the parameter priorities are dynamically adjusted. According to the specific requirements and actual situation of the project, users or system administrators are allowed to flexibly adjust the priority order of parameters. For example, in some projects with extremely high energy consumption requirements, the priorities of energy consumption efficiency-related parameters can be raised above those of other parts of the parameters to ensure that energy consumption issues are focused on during the initial modeling stage. Use the Drools rules engine to manage parameter priorities and spatial topology rules, and integrate with Revit through the Java API. For example, for the default priority of "structural safety > energy consumption efficiency > cost control", define the rule: when the design specification database is updated (such as adding the clause "fire compartment area ≤ 1500㎡"), the system matches the thresholds in the specification text with the parameter types through regular expressions, automatically generates Drools rules and loads them into the engine; in parameter conflict handling, fuzzy logic is introduced to evaluate multi-objective trade-offs. For example, when "floor net height" conflicts with "beam height", the algorithm calculates the satisfaction degree of each parameter based on the membership function and selects the solution with the highest comprehensive membership degree (such as giving priority to meeting the net height requirements and at the same time adjusting the cross-sectional shape of the beam to reduce the impact on floor height).

[0024] As described in step S2 above, obtain the latest building code text from the authoritative code release platform through the data interface, extract the code parameter requirements, and store them in the design specification database in a structured manner. When obtaining the latest building code text from the authoritative code release platform through the data interface, in order to cope with the platform's anti-crawler mechanism, multiple strategies are adopted. On the one hand, set a reasonable request interval time to simulate the browsing behavior of human users and avoid being blocked from accessing the IP due to frequent requests; on the other hand, use a proxy server pool to dynamically switch the IP address for data scraping. When extracting key parameters, use the named entity recognition (NER) technology in natural language processing and combine with a professional dictionary in the construction field to improve the accuracy of parameter extraction. For some ambiguous or ambiguous code content, accurate interpretation is carried out through semantic understanding and context analysis. For example, when interpreting the clause on "number of safety exits" in the fire protection code, combine information such as different building types and building areas to accurately extract the corresponding quantity requirements.

[0025] Based on the component types and spatial attributes of the initial BIM model, filter the applicable clause sets from the design specification database; based on the filtered clause sets, compare the model parameters with the specification thresholds item by item through a parameter matching algorithm to identify non-compliant item parameters. When filtering the applicable clause sets from the specification library according to the types and spatial attributes of BIM components, a classification model based on deep learning is introduced. The classification model takes information such as component types and spatial attributes as inputs and is trained with a large amount of specification clause data, and can quickly and accurately filter out the most relevant specification clauses. Compared with traditional rule-based filtering methods, the deep learning model can better handle complex situations and implicit relationships. In terms of the parameter matching algorithm, a matching method based on cosine similarity is adopted and combined with the edit distance algorithm to perform multi-dimensional comparison of the model parameters and the specification thresholds. This can more precisely judge the similarity and differences between parameters and improve the recognition accuracy of non-compliant item parameters. For example, for the fire resistance limit parameter of a firewall, not only the specific value is compared, but also the unit, precision of the value and its relevance to other related parameters are considered to ensure accurate identification of non-compliant situations.

[0026] Call the preset replacement strategy through the AI rule engine to automatically correct non-compliant parameters and generate a specification-compliant BIM model with an adjustment log. When the AI rule engine calls the preset policy library to correct non-compliant parameters, case-based reasoning (CBR) technology is introduced. When encountering non-compliant parameters, the system first searches for similar problem cases in the case library and makes corrections by referring to historical solutions. If there is no exactly matching case in the case library, a new solution is generated by combining rule reasoning and expert experience, and the new case is stored in the case library for subsequent reference. At the same time, the preset policy library is expanded to add more correction strategies, such as component replacement, connection method adjustment, etc. For example, when it is found that the steel reinforcement of a certain beam does not meet the specification requirements, in addition to adjusting the quantity and specification of the steel reinforcement, the type of the beam can also be considered for replacement (such as replacing a rectangular beam with a T-shaped beam) or the connection method between the beam and other components can be optimized to meet the specification requirements.

[0027] As described in step S3 above, structural stability analysis is carried out by the finite element method to calculate the member stress ratio and the overall displacement angle in the specification-compliant BIM model, and the over-limit areas are identified. When using finite element analysis software (such as ANSYS) to calculate the member stress ratio and the overall displacement angle, refined modeling techniques are adopted. For key members and complex joints, more detailed finite element models are established, considering factors such as material nonlinearity, geometric nonlinearity, and contact nonlinearity, to improve the accuracy of the analysis results. For example, when analyzing the bottom frame columns of a high-rise building, the mechanical behavior of the columns under vertical and horizontal loads is more realistically simulated through refined modeling, and the stress ratio and displacement angle are accurately calculated. At the same time, a reliability analysis method is introduced to evaluate the reliability of the structural stability analysis results. By considering uncertain factors such as material properties and load values, the failure probability of the structure at different reliability levels is calculated. For example, the stress ratio and displacement angle of a certain floor beam at a 95% reliability level are calculated to evaluate whether it meets the design requirements.

[0028] Based on the specification-compliant BIM model, energy consumption simulation is carried out, and the hourly load calculation for the whole year is performed through the EnergyPlus engine to output the peak values of heating and cooling loads and the energy consumption density distribution. When performing the hourly load calculation for the whole year through the EnergyPlus engine, to improve the accuracy of the energy consumption simulation, more influencing factors are considered. In addition to the building envelope structure and equipment operation parameters, the dynamic changes of meteorological data, the patterns of human behavior, and the heat gain factors inside the building (such as lighting and heat generation of electrical equipment) are also incorporated. By establishing a human behavior model, the impact of human activities on building energy consumption at different time periods is simulated. For example, during office hours, people are active, and lighting and air conditioning equipment are used more, resulting in an increase in energy consumption; while at night or on holidays, people's activities decrease, and energy consumption is reduced. In addition, the energy consumption simulation results are analyzed in depth to identify high-energy consumption areas, and the key factors affecting energy consumption are determined through sensitivity analysis. For example, by changing parameters such as the heat transfer coefficient and shading coefficient of the exterior windows, the degree of their impact on the overall building energy consumption is analyzed.

[0029] Evaluate the space utilization rate of the compliant BIM model against the specification, analyze the efficiency of the pedestrian flow path using space syntax, and calculate the space utilization rate index; normalize the over-limit area, the peak heating and cooling load, the energy consumption density distribution, and the space utilization rate index into deviation rates, and generate a performance analysis report including component correlation marks. When analyzing the efficiency of the pedestrian flow path using space syntax, combine virtual reality (VR) and augmented reality (AR) technologies to conduct a more intuitive analysis and evaluation of the space. By creating a virtual building space model, users can take a virtual stroll to observe and feel the accessibility and visual integration of the space in real time. At the same time, use VR / AR technologies to collect the user's behavior data in the virtual space, such as walking paths and staying times, to further optimize the calculation method of the space utilization rate index. When proposing layout optimization suggestions, use intelligent optimization algorithms such as genetic algorithms to search for and optimize multiple layout schemes. The algorithm uses the space utilization rate index as the objective function, considers constraints such as building functions and structural limitations, and automatically generates the optimal layout scheme. For example, when optimizing the layout of the office area in an office building, the genetic algorithm can quickly search for the most reasonable room division and passage setting to improve the space utilization efficiency.

[0030] As described in step S4 above, classify and code the structural stability deviation, energy consumption deviation, and space utilization deviation in the performance analysis report according to parameter types, and use them as the state input vector of the deep reinforcement learning network. The deep Q network adopts a three-layer fully connected structure. The dimension of the input layer is 128 (corresponding to normalized parameters such as structural stability deviation, energy consumption deviation, and space utilization deviation), the number of hidden layer nodes is 512, 256, and 128, the activation function is selected as ReLU to enhance the non-linear modeling ability, the dimension of the output layer is 50 (corresponding to predefined actions such as component size adjustment and material replacement), and a linear activation function is used. During training, set the capacity of the experience replay buffer to 10,000, adopt a priority sampling strategy based on TD error, and the initial exploration rate ε = 0.9 and linearly decay to 0.1. The reward mechanism is specifically as follows: +10 for meeting the structural deviation standard, +5 for every 5% reduction in energy consumption, -20 for violating the structural constraint, and -5 for cost overrun. The training process includes steps such as initializing the network, ε-greedy action selection, experience storage, and batch parameter update. The convergence condition is that the total deviation change rate < 2% after 10 consecutive optimizations.

[0031] In the Deep Q-Network (DQN), the Double Q-Network and Prioritized Experience Replay techniques are adopted to improve the training efficiency and convergence speed of the network. The Double Q-Network reduces the overestimation problem of Q-value estimation by decoupling action selection and action evaluation, making the training more stable. Prioritized Experience Replay samples according to the importance of experience samples, preferentially selecting samples that contribute more to network training for learning, thus accelerating the convergence speed of the network. Define the output action of the deep reinforcement learning network as the set of BIM model parameter adjustment operations, including component geometric dimension adjustment, material property replacement, and spatial topology relationship optimization. Further expand the predefined action space to add more parameter adjustment operations. For example, in terms of material property replacement, in addition to replacing the outer window glass type and increasing the wall insulation layer thickness, operations such as replacing the building facade material and adjusting the thermal conductivity of the insulation material can also be considered; in terms of spatial topology optimization, actions such as re-planning the internal functional zones of the building and adjusting the public space layout are added. At the same time, perform a hierarchical design on the action space. According to different optimization goals and difficulty levels, divide the actions into different levels. In the initial stage of optimization, preferentially select simple, easy-to-implement actions that have a greater impact on performance improvement; as the optimization progresses, gradually try more complex actions. For example, when optimizing the building structure performance, first try simple actions such as adjusting the cross-sectional dimensions of beams and columns. If the effect is not obvious, then consider complex actions such as adjusting the structural system.

[0032] Configure the network training reward mechanism to give positive rewards when the model parameters meet the performance threshold, partial rewards when the deviation decreases after optimization, and penalties when structural or energy consumption constraints are violated; select optimization actions through the exploration-exploitation balance strategy, iteratively adjust the BIM model parameters and re-perform performance simulations until the model parameters reach the convergence condition after continuous optimization for multiple times. Refine the design of the reward mechanism. In addition to considering whether the parameters meet the threshold and whether the deviation rate decreases, more factors are also incorporated. For example, consider the implementation cost of the optimization action, its impact on other performance indicators, and the timeliness of optimization. If an optimization action can reduce the energy consumption deviation rate but has too high an implementation cost or has an adverse impact on structural stability, then a lower reward or even a penalty is given. At the same time, set different reward weights according to different optimization stages. In the initial stage of optimization, encourage exploring more actions and give certain rewards for trying new actions; in the later stage of optimization, pay more attention to the quality of the optimization results and give higher rewards to actions that meet the performance threshold and significantly reduce the deviation rate. For example, in the first half of the optimization stage, set the reward value for exploring new actions to +0.3; in the second half, increase the reward value for meeting the performance threshold to +1.5 to guide the network to find the optimal solution faster.

[0033] As described in step S5 above, three-dimensional point cloud data of the actual construction components is obtained through laser scanning, and position deviation analysis is carried out with the theoretical coordinates of the corresponding components in the performance-optimized BIM model to calculate the construction deviation ratio. The operating data of building equipment, including air-conditioning energy consumption, lighting power, and elevator usage frequency, is collected in real time, and energy consumption and performance deviation analysis is carried out with the predicted values of the performance-optimized BIM model. When obtaining the on-site component point cloud data through laser scanning, multi-sensor fusion technology is adopted. Combining multiple sensors such as lidar and cameras to obtain more comprehensive and accurate component information. The position and shape of the component are measured by lidar, and the surface texture and color information of the component (defects such as cracks and corrosion on the component surface) are obtained by the camera. The combination of the two can more intuitively judge the state and quality of the component. When collecting the equipment operating data in real time, edge computing technology is adopted to perform preliminary processing and analysis of the data at the equipment end, and only the key and compressed data is transmitted to the cloud, reducing the data transmission volume and latency. For example, when collecting the operating data of air-conditioning equipment, indicators such as the energy consumption and COP value of the air-conditioning are calculated in real time through edge computing, and the data is subjected to anomaly detection. When the data is abnormal or further analysis is required, the data is uploaded to the cloud to improve the efficiency and real-time performance of data processing.

[0034] When the construction deviation ratio or the operation and maintenance energy consumption deviation ratio exceeds the preset threshold, the secondary optimization of the deep Q network is automatically triggered to generate a dynamically updated BIM data stream containing parameter adjustment records. The BIM data stream is marked with a version number, a change time, and an impact scope label. When performing digital twin comparison with the BIM model, image matching and feature recognition technologies based on deep learning are adopted to improve the comparison accuracy and efficiency. By training a convolutional neural network (CNN) model, feature extraction and matching are carried out on the point cloud data obtained by laser scanning and the three-dimensional model in the BIM model to calculate the position deviation and shape deviation. When triggering the DQN secondary optimization, model order reduction technology is adopted to simplify the complex BIM model, extract key parameters and features, and reduce the computational complexity and accelerate the optimization speed while ensuring the optimization accuracy.

[0035] Distribute the dynamically updated BIM data stream to the design, construction, and operation and maintenance ends, respectively driving design review, construction plan adjustment, and equipment parameter calibration for full life cycle management. When synchronizing the full life cycle data stream, a combination of blockchain technology and distributed ledger is adopted. Each data update operation is recorded in the distributed ledger and encrypted through the encryption algorithm of the blockchain to prevent data tampering and forgery. And establish a data sharing and collaboration platform to achieve data interaction and collaborative work in each stage of design, construction, and operation and maintenance. For example, designers can view the data feedback from the construction and operation and maintenance stages in real time and adjust the design plan; construction personnel can adjust the construction plan and methods according to design changes and operation and maintenance requirements; operation and maintenance personnel can feedback the equipment operation data and fault information to the design and construction parties in real time to jointly solve problems. To improve the compatibility and openness of the system, standardized data interfaces and formats are adopted to support data interaction and integration between different software and systems.

[0036] In one embodiment, taking the comprehensive office building project in a small cultural and creative park as an example, natural language processing technology combining a word vector model (such as Word2Vec) and a BERT model is used to analyze user requirements. For requirements such as "the office building needs to have sufficient natural lighting, and the creative studio should have a flexible space layout", the Word2Vec first maps the words in the text to the vector space to capture the semantic similarity between words, and the BERT model further conducts in-depth semantic understanding of the sentence, accurately extracting keywords ("natural lighting", "flexible space layout") and constraints (such as the proportion of lighting area, requirements for space transformability). The grammatical relationships of each part in the requirements are clarified. For example, "office building" is the main body, and "sufficient natural lighting" is the conditional limitation. An efficient in-memory database is used to store the preset mapping table. For the new requirement "using environmentally friendly soundproof materials for studio partitions" that appears, an online update mechanism is triggered. With the help of a knowledge graph expansion tool, the BIM parameters (such as the sound insulation coefficient, environmental protection level, thickness, etc.) and value ranges corresponding to "environmentally friendly soundproof materials" are found. After auditing and confirming that there are no errors, the new entry is added to the mapping table, and the relevant semantic associations are updated. The parametric-driven geometric modeling technology is used to call the Revit API to generate an initial BIM model. According to the building functions and spatial topology rules, multiple initial layout schemes that meet the basic requirements are automatically generated. For example, for the lighting requirement, different combinations of window orientations, sizes, and positions are considered; for the flexible space layout, different studio partition layout schemes are generated. These schemes are screened by the NSGA-II multi-objective optimization algorithm, with the lighting effect, space flexibility, and structural rationality as the optimization objectives to determine the optimal initial layout. Combining the parameter priorities (such as natural lighting > space flexibility > structural cost), the geometric structure (such as wall thickness, window size, floor height) and attribute tags (such as material sound insulation performance, fire protection level) of the model are determined. The final output initial BIM model in IFC format comes with a detailed design parameter description.

[0037] The anti-crawler mechanism is bypassed by using random request intervals and disguised request headers to obtain the latest specifications. A rule engine is used to identify common specification parameters (such as evacuation passage width, fire compartment area), and then a text classification model trained with an LSTM-based model is used to process clauses with complex semantics, such as the usage specifications of new environmentally friendly materials. According to the types, spatial attributes, and functional areas of BIM components, a decision tree algorithm is used to screen applicable specification clauses. In the parameter matching process, a grey relational analysis algorithm is introduced to handle the complex relationship between model parameters and specification thresholds to judge compliance. The adjusted specification-compliant BIM model uses blockchain technology to record the modification history, and each modification operation is recorded as a blockchain transaction, including information such as the modification time, modification content, operator, and basis clause. At the same time, a detailed adjustment report is generated to show the changes in the model before and after adjustment in the form of a table and a 3D model comparison.

[0038] ANSYS was used to analyze the structural stability, and the finite element substructure method was used to simulate the key parts (such as staircases and cantilever structures) in a refined manner. Based on the overall model analysis, detailed submodels were established for key parts, taking into account the nonlinear characteristics of the materials and complex boundary conditions, including the accurate calculation of the stress distribution and displacement of the staircase and platform when simulating the staircase structure. Energy consumption simulation was performed based on the EnergyPlus engine, and the terrain, buildings and vegetation information around the building were obtained through the Geographic Information System (GIS). The radiosity algorithm was used to simulate the distribution of solar radiation on the building surface and calculate the heat gain of the building envelope. In terms of simulating the impact of personnel behavior on energy consumption, a personnel behavior pattern recognition method based on cluster analysis was used. According to historical personnel activity data, personnel behavior was divided into different modes (weekday office mode, weekend exhibition mode), and the personnel density and equipment usage time were predicted for different modes to simulate the dynamic energy consumption of the building. For example, during office hours on weekdays, the energy consumption of office area lighting and equipment is high. The efficiency of pedestrian paths is analyzed using space syntax, and combined with virtual reality (VR) technology, a virtual walk is conducted in a virtual building model from a first-person perspective. The system records the user's walking path, stop location, time and other data in real time to optimize the calculation of space utilization indicators.

[0039] In the process of deep Q network optimization, the Rainbow algorithm is used to integrate multiple technologies to improve network performance. The Rainbow algorithm combines dual Q networks, priority experience playback, multi-step learning and distributed reinforcement learning technology to reduce the deviation of Q value estimation and improve training efficiency and stability. In terms of state space construction, in addition to structural stability deviation, energy consumption deviation and space utilization deviation, factors such as cost deviation and construction cycle deviation are also included to integrate network decision-making. Action adjustment operations for intelligent control and energy management strategies of building equipment are added, including adjusting the intelligent dimming strategy of the lighting system (automatically adjusting the brightness according to different time periods and light intensity) and optimizing the zoning control strategy of the air-conditioning system (controlling according to the population density and temperature requirements of different areas). The reward mechanism is designed by comprehensively considering multiple factors. In addition to the basic parameter compliance reward, deviation reduction reward and violation penalty, rewards for the sustainability and innovation of optimization actions are also added. Additional rewards are given to optimization actions that use energy-saving technologies and improve the sustainability of buildings without affecting other performance; rewards are given to actions that use solar photovoltaic power generation and reduce the carbon emissions of buildings.

[0040] During the construction and operation and maintenance phases, low-power and high-precision sensors are used in combination with narrowband Internet of Things (NB-IoT) technology for data collection and transmission. Temperature and humidity sensors, energy consumption sensors, displacement sensors, etc. are arranged in the building to collect physical building data in real time. On the edge computing device, lightweight machine learning models (such as decision trees for equipment status monitoring and K-means clustering for energy consumption anomaly detection) are used to perform real-time analysis and anomaly judgment on the data. After obtaining the three-dimensional point cloud data of the actual construction components through laser scanning, a fast registration algorithm based on feature point matching is used to compare with the BIM model to calculate the position deviation and shape deviation, trigger DQN secondary optimization, and combine the real-time feedback mechanism of digital twin technology to feedback the real-time status information of the physical building (such as actual displacement, temperature change) into the BIM model for dynamic synchronization between the model and the actual building. In specific implementation, it is found that there is a deviation between the actual position and the designed position of a certain wall, and this information is transmitted to the BIM model through the real-time feedback mechanism. DQN re-optimizes the layout and structure of the corresponding area according to the new information. A full-life-cycle data stream management platform is built using blockchain technology, and each data update operation is recorded as a block on the blockchain, including information such as the time, content, source, and target of the data update. Automatic synchronization and collaboration of data in different stages are achieved through smart contracts.

[0041] In this embodiment, the dynamic specification adaptation module captures and parses more than 97% of the newly released specification clauses in real time, and the design compliance review time is shortened from 32 hours of traditional manual verification to 9 hours; the DQN optimization module balances structural safety and energy consumption efficiency through the reward mechanism, compressing the optimization cycle from 2 weeks of manual trial and error to 48 hours, and verifying the non-obviousness of the technology combination through the synergistic effect of each step.

[0042] Refer to Figure 2 , which is the structural block diagram of the building information intelligent management system based on BIM technology in an embodiment of the present invention, including: A requirements modeling unit, which is used to convert user requirements into a standardized parameter set through a preset mapping table that supports online updates and natural language processing technology, and generate an initial BIM model in combination with a rule engine; A specification adaptation unit, which is used to automatically adjust the parameters of the initial BIM model based on a dynamically updated design specification database through parameter matching and an AI rule engine, and output a specification-compliant BIM model; A performance simulation unit, which is used to perform simulation calculations on the structural stability, energy consumption distribution, and space utilization rate of the specification-compliant BIM model, and generate a performance analysis report; A model optimization unit, configured to use the normalized deviation value in the performance analysis report as the state input of the deep Q-network, output an optimization decision through a predefined action space, iteratively adjust the parameters until the threshold is met, and output a performance-optimized BIM model; A twin update unit, configured to obtain physical building data in the construction and operation and maintenance phases in real time through an Internet of Things interface, perform digital twin comparison with the performance-optimized BIM model, calculate parameter deviations, trigger secondary optimization of the deep Q-network, generate a dynamically updated BIM data stream, and synchronize it to all phases of the whole life cycle.

[0043] For the specific implementation of each unit in the above device instance, please refer to that described in the above method embodiment, and details are not described herein again.

[0044] Refer to Figure 3 , an embodiment of the present invention further provides a computer device, which may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art can understand that Figure 3 the structure shown in

[0046] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0047] In summary, through the preset mapping table that supports online updates and natural language processing technology, the present invention converts user requirements into a standardized parameter set, combines a rule engine to generate an initial BIM model; based on a dynamically updated design specification database, through parameter matching and an AI rule engine, automatically adjusts the parameters of the initial BIM model to output a specification-compliant BIM model; performs simulation calculations on the structural stability, energy consumption distribution, and space utilization rate of the specification-compliant BIM model to generate a performance analysis report; uses the normalized deviation value in the performance analysis report as the state input of the deep Q network, outputs an optimization decision through a predefined action space, iteratively adjusts the parameters until the threshold is met, and outputs a performance-optimized BIM model; obtains real-time physical building data during the construction and operation and maintenance phases through an IoT interface, performs digital twin comparison with the performance-optimized BIM model, calculates parameter deviations and triggers secondary optimization of the deep Q network, generates a dynamically updated BIM data stream, and synchronizes it to all stages of the entire life cycle to achieve full-process intelligent control of building information from intelligent parsing of user requirements, real-time compliance with design specifications, autonomous optimization of multi-objective performance to dynamic closed-loop management of the entire life cycle, and to improve design efficiency, engineering compliance, and resource utilization efficiency.

[0048] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0049] It should be noted that in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article or method comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0050] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for intelligent management of building information based on BIM technology, characterized in that: The following steps are involved: Through the preset mapping table that supports online update and natural language processing technology, user requirements are converted into standardized parameter sets, and the initial BIM model is generated in combination with the rule engine; Based on the dynamically updated design specification database, the initial BIM model parameters are automatically adjusted through parameter matching and AI rule engine to output a specification-compliant BIM model; Perform simulation calculations on the structural stability, energy consumption distribution and space utilization of the BIM model that complies with the regulations, and generate a performance analysis report; The normalized deviation value in the performance analysis report is used as the state input of the deep Q network, the optimization decision is output through the predefined action space, the parameters are iteratively adjusted until the threshold is met, and the performance optimization BIM model is output; The physical building data of the construction and operation and maintenance stages are acquired in real time through the IoT interface, and compared with the performance-optimized BIM model through a digital twin. The parameter deviation is calculated and the secondary optimization of the deep Q network is triggered to generate a dynamically updated BIM data stream that is synchronized to all stages of the life cycle.

2. The method for intelligent management of building information based on BIM technology according to claim 1 is characterized in that: The steps of converting user requirements into standardized parameter sets by using a preset mapping table that supports online updating and natural language processing technology, and generating an initial BIM model in combination with a rule engine include: Parse the natural language requirements entered by users and extract keywords and constraints; Mapping the keywords to BIM parameter types and value ranges through a preset mapping table, wherein the mapping table includes a user requirement semantics, BIM parameters, and a triple relationship of associated components; If there is no match in the mapping table, an online update request is triggered and the mapping table entries are expanded based on user feedback; A rule engine is used to generate the geometric structure and attribute labels of the initial BIM model according to parameter priorities and spatial topology rules.

3. The intelligent management method of building information based on BIM technology according to claim 1 is characterized in that: The step of automatically adjusting the initial BIM model parameters based on the dynamically updated design specification database through parameter matching and AI rule engine to output a specification-compliant BIM model includes: Capture the latest building specification text from the authoritative specification publishing platform through the data interface, extract the specification parameter requirements and store them in a structured manner in the design specification database; Based on the component types and spatial properties of the initial BIM model, selecting an applicable clause set from the design specification database; Based on the selected set of terms of use, the model parameters are compared with the regulatory thresholds one by one through the parameter matching algorithm to identify non-compliant parameters; The preset replacement strategy is called through the AI ​​rule engine to automatically correct non-compliant parameters and generate a standard-compliant BIM model with an adjustment log.

4. The method for intelligent management of building information based on BIM technology according to claim 1 is characterized in that: The step of simulating and calculating the structural stability, energy consumption distribution and space utilization of the standard-compliant BIM model to generate a performance analysis report includes: Perform structural stability analysis using the finite element method, calculate component stress ratios and overall displacement angles in the code-compliant BIM model, and identify over-limit areas; Energy consumption simulation is performed based on the BIM model that complies with the regulations. The EnergyPlus engine is used to calculate the hourly load throughout the year and output the peak cooling and heating loads and energy consumption density distribution. Evaluate the space utilization of the BIM model that complies with the regulations, use space syntax to analyze the efficiency of pedestrian flow paths, and calculate the space utilization index; The over-limit area, the cooling and heating load peaks, the energy consumption density distribution and the space utilization rate index are normalized into a deviation rate, and a performance analysis report including component association marks is generated.

5. The method for intelligent management of building information based on BIM technology according to claim 1 is characterized in that: The step of using the normalized deviation value in the performance analysis report as the state input of the deep Q network, outputting the optimization decision through a predefined action space, iteratively adjusting the parameters until the threshold is met, and outputting the performance optimization BIM model includes: The structural stability deviation, energy consumption deviation and space utilization deviation in the performance analysis report are classified and encoded according to parameter types, and used as state input vectors of the deep reinforcement learning network; The output action of the deep reinforcement learning network is defined as a set of BIM model parameter adjustment operations, including component geometry adjustment, material property replacement, and spatial topological relationship optimization; Configure the network training reward mechanism, give positive rewards when the model parameters meet the performance threshold, give partial rewards when the deviation is reduced after optimization, and give penalties when the structure or energy consumption constraints are violated; The optimization actions are selected through the exploration-exploitation balance strategy, the BIM model parameters are iteratively adjusted and the performance simulation is re-performed until the model parameters reach the convergence condition after multiple consecutive optimizations.

6. The intelligent management method of building information based on BIM technology according to claim 1 is characterized in that: The steps of acquiring the physical building data in the construction and operation and maintenance stages in real time through the IoT interface, performing digital twin comparison with the performance optimization BIM model, calculating parameter deviations and triggering secondary optimization of the deep Q network, generating a dynamically updated BIM data stream, and synchronizing it to each stage of the life cycle include: Acquire three-dimensional point cloud data of actual construction components through laser scanning, perform position deviation analysis with theoretical coordinates of corresponding components in the performance optimization BIM model, and calculate the construction deviation ratio; Collect building equipment operation data in real time, including air conditioning energy consumption, lighting power, and elevator usage frequency, and analyze energy consumption and performance deviations with the predicted values ​​of the performance optimization BIM model; When the construction deviation ratio or the operation and maintenance energy consumption deviation ratio exceeds the preset threshold, the secondary optimization of the deep Q network is automatically triggered to generate a dynamically updated BIM data stream containing parameter adjustment records, and the BIM data stream is marked with version number, change time and impact range label; The dynamically updated BIM data stream is distributed to the design, construction, and operation and maintenance ends, respectively driving design review, construction plan adjustment, and equipment parameter calibration for full life cycle management.

7. The method for intelligent management of building information based on BIM technology according to claim 1 is characterized in that: Based on the BIM data stream, the model operation performance is monitored through a real-time feedback system. If there is a deviation, the deep Q network is triggered to optimize again, dynamically update the BIM model and iterate in a loop, including: Deploy performance monitoring agents during the operation and maintenance phase to collect building operation data in real time and calculate key indicators; If the indicator exceeds the BIM model prediction value by 20% for three consecutive monitoring cycles, it is judged as a performance deviation; Trigger the deep Q network to use the current operation and maintenance data as input and re-optimize the model parameters; The updated BIM model version is marked and synchronized to the full life cycle data flow to form a closed-loop management.

8. A building information intelligent management system based on BIM technology, characterized in that: include: The demand modeling unit is used to convert user requirements into standardized parameter sets through preset mapping tables that support online updates and natural language processing technology, and generate the initial BIM model in combination with the rule engine; A specification adaptation unit, which is used to automatically adjust the initial BIM model parameters based on a dynamically updated design specification database through parameter matching and an AI rule engine, and output a specification-compliant BIM model; A performance simulation unit is used to simulate and calculate the structural stability, energy consumption distribution and space utilization of the code-compliant BIM model and generate a performance analysis report; A model optimization unit, used to use the normalized deviation value in the performance analysis report as a state input of the deep Q network, output an optimization decision through a predefined action space, iteratively adjust the parameters until a threshold is met, and output a performance optimized BIM model; The twin update unit is used to obtain the physical building data of the construction and operation and maintenance stages in real time through the Internet of Things interface, compare the digital twin with the performance optimization BIM model, calculate the parameter deviation and trigger the secondary optimization of the deep Q network, generate a dynamically updated BIM data stream, and synchronize it to all stages of the entire life cycle.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the building information intelligent management method based on BIM technology described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the building information intelligent management method based on BIM technology described in any one of claims 1 to 7 are implemented.

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