Electromechanical system self-optimization platform and method based on BIM forward design and AI new technology, and storage medium
By combining BIM and AI technologies, a self-optimization platform for electromechanical systems was established, which solved the problem of low design efficiency of electromechanical systems in existing technologies, realized automated optimization and precise collaborative design, and improved design efficiency and building performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-04-03
AI Technical Summary
Existing BIM technology relies on human experience in the design of electromechanical systems, resulting in low efficiency and difficulty in responding quickly. It is also difficult to handle multiple objective trade-offs, especially in pipeline collision and floor height analysis, which require multiple rounds of adjustments.
By combining BIM forward design and AI technology, a self-optimization platform for electromechanical systems is established. Through data cleaning, feature extraction, and AI optimization modules, optimization solutions are generated, and physical simulation verification and manual review are conducted to achieve front-end and back-end separation and automated updates.
It improves the efficiency of electromechanical system design, reduces costs, optimizes building performance, and realizes automated optimization and precise collaborative design of electromechanical system models.
Smart Images
Figure CN121787233A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of building information modeling and artificial intelligence technology, and in particular relates to a self-optimization platform, method and storage medium for electromechanical systems based on BIM forward design and new AI technologies. Background Technology
[0002] Building Information Modeling (BIM) is a digital 3D modeling technology widely used in the fields of architecture, engineering, and construction. By integrating multi-dimensional information data, BIM technology transforms 2D data of building projects into 3D data, enabling precise and collaborative functions in the design, construction, and operation phases of a building.
[0003] Currently, BIM technology is widely used in the architectural design, construction, and operation and maintenance phases. However, issues such as pipeline collisions and clearance analysis in MEP systems still rely on manual experience, requiring multiple rounds of adjustments to achieve the desired effect. Furthermore, it is difficult to react quickly to modifications, resulting in low efficiency and difficulty in handling multiple objective trade-offs. To address these problems, this invention creatively combines BIM technology with AI. Through the learning and computational capabilities of AI, a self-optimizing platform for MEP systems is established. By analyzing project data, it quickly obtains solutions to the required objectives, improving design efficiency. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a self-optimization platform, method, and storage medium for electromechanical systems based on BIM forward design and new AI technologies. This platform aims to improve the design efficiency of electromechanical systems, reduce costs while meeting requirements, and optimize building performance. By developing software to learn the model and combining design specifications and owner needs, it generates optimized solutions for electromechanical systems (pipeline collisions, floor height analysis) within the BIM model. In the BIM forward design phase, issues such as pipeline collisions and insufficient floor height, which would otherwise require significant later adjustments, can be addressed during the design process.
[0005] The present invention achieves the above-mentioned technical objectives through the following technical means.
[0006] A self-optimization platform for electromechanical systems based on BIM forward design and new AI technologies includes: The application layer provides a user interface that allows users to initiate optimization tasks through BIM platform plugins or integrated functions. It also displays optimization results, performance comparisons, or simulation reports in the form of charts or 3D views, and supports user decision-making. The service layer acts as a bridge between the front-end and back-end and internal and external services. It receives application layer requests through internal APIs, transmits processing results, realizes the separation of front-end and back-end, and integrates external service functions, calls the computing engine, and realizes simulation-driven optimization. The core processing layer includes a data input module, a preprocessing module, an AI optimization module, a verification feedback module, and an output module; The data input module supports parsing multiple BIM file formats; The preprocessing module has a built-in data cleaning engine and graph structure converter to perform data cleaning, extract the required data information from the model, and convert the extracted data information into a graph data structure through the graph structure converter. The AI optimization module enables users or the system to define multi-objective optimization problems through the target configurator. It uses various AI algorithms from the algorithm library, drives the algorithm execution through the inference engine, trains the model using historical data, and generates optimization solutions based on the current objectives and constraints. The verification and feedback module passes the optimization scheme generated by the AI optimization module to the service layer, calls external service functions to perform physical simulation verification, and allows experts to review, mark or fine-tune the AI scheme through the manual review interface. Based on the simulation results and manual feedback, the AI model parameters are adjusted. The output module writes the final optimized solution back to the BIM model, enabling automatic updates of the design files, and records a complete optimization process log for traceability, auditing, and performance analysis. The data storage layer stores related data in layers.
[0007] A self-optimization method for electromechanical systems based on BIM forward design and AI technology, utilizing the aforementioned BIM-based forward design and AI technology platform, includes the following steps: Step 1: Initiate an optimization task through the user interface. The data input module receives and verifies the electromechanical system BIM model file input by the user. Step 2: The preprocessing module preprocesses the received BIM model data, including data cleaning, feature extraction, and data standardization. Step 3: The AI optimization module performs optimization analysis on the BIM model and outputs optimization solutions; Step 4: The verification feedback module passes the optimization plan to the service layer. The service layer calls external service functions to perform physical simulation verification. At the same time, experts manually review, mark, or fine-tune the optimization plan through a manual review interface. If the verification result does not meet expectations, the feedback data is input into the AI model for parameter adjustment and re-optimization until the expected result is achieved, then proceed to Step 5. If the verification result meets expectations, proceed directly to Step 5. Step 5: The output module outputs the expected BIM model and generates optimization records.
[0008] Furthermore, in step 2, the preprocessing specifically includes: Data cleaning: Repairing geometric errors in BIM models using a data cleaning engine, including removing redundancies and filling in missing parameters; Feature extraction: Extract key features of electromechanical system-related parameters and energy consumption indicators from the BIM model; Data standardization: The extracted key features are transformed into a data format that is suitable for the input of AI models through a graph structure converter.
[0009] Furthermore, the specific process of step 3 is as follows: Analyze the relevant data of the MEP system in the BIM model, including the dimensions, location and installation height of water pipes, air ducts and cable trays; obtain the output results required for this project, including pipeline integration and headroom analysis; Based on the required output results and combined with data from similar projects in the learning database, AI-based automated generation of comprehensive electromechanical system pipeline results that meet the requirements of headroom and performance, i.e., optimization scheme.
[0010] Furthermore, in step 5, the output BIM model includes a modification log, which records the parameter comparison before and after optimization, the AI decision-making basis, and the simulation verification results.
[0011] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the self-optimization method for electromechanical systems based on BIM forward design and new AI technologies as described in claim 2.
[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the steps of the electromechanical system self-optimization method based on BIM forward design and new AI technology as described in claim 2.
[0013] The present invention has the following beneficial effects: Efficiency Improvement: By using AI to optimize and process difficult aspects of the electromechanical system model, such as pipeline collisions and floor height analysis, in advance during the forward design process, design efficiency can be greatly improved.
[0014] Multi-objective collaboration: AI's high computing power allows for collaborative consideration of collisions and floor heights in electromechanical BIM models, making it more accurate and efficient than manual methods. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the self-optimization process of the electromechanical system based on BIM forward design and new AI technology as described in this invention. Detailed Implementation
[0016] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto.
[0017] The electromechanical system self-optimization platform based on BIM forward design and new AI technology described in this invention includes: an application layer, a service layer, a core processing layer, and a data storage layer.
[0018] Application layer (user-side entry): Provides a user interface, allowing users to initiate optimization tasks in the familiar Revit or ArchiCAD environment through BIM platform plugins or integration functions. It also displays optimization results, performance comparisons or simulation reports in the form of charts or 3D views, and supports user decision-making.
[0019] Service Layer (System Integration and Communication): Serving as a bridge between the front-end and back-end and internal and external services, it receives application layer requests through internal APIs, transmits processing results, and achieves front-end and back-end separation. At the same time, it integrates external service functions, transforms pre-processed data into the input format required by professional simulation tools, calls the computing engine, and realizes simulation-driven optimization.
[0020] Core processing layer (system brain): includes data input module, preprocessing module, AI optimization module, verification feedback module, and output module; Data input module: Supports parsing multiple BIM file formats; Preprocessing module: Built-in data cleaning engine and graph structure converter. The data cleaning engine repairs common geometric errors, information redundancy or inconsistency problems in the model, extracts the required data information from the model, and converts the extracted data information into a graph data structure through the graph structure converter to facilitate subsequent module processing. AI Optimization Module: Through the target configurator, users or the system define multi-objective optimization problems. It uses various AI algorithms from the algorithm library, drives the algorithm to run through the inference engine, trains the model using historical data, and generates multiple alternative solutions based on the current target and constraints. Verification and Feedback Module: The solution generated by the AI optimization module is passed to the service layer, external service functions are called to perform physical simulation verification, and experts are allowed to review, mark or fine-tune the AI solution through the manual review interface. Based on the simulation results and manual feedback, the AI model parameters are adjusted to achieve system self-improvement. Output module: Writes the final optimized solution back to the BIM model, realizes automatic updates of design documents, and records a complete optimization process log for traceability, auditing and performance analysis.
[0021] Data storage layer (system memory): Related data is stored in layers.
[0022] The self-optimization method for electromechanical systems based on BIM forward design and new AI technologies, utilizing the aforementioned BIM-based forward design and AI technology self-optimization platform, is as follows: Figure 1 As shown, the specific steps are as follows: Step 1: BIM model input and verification; The user initiates an optimization task through the user interface. The data input module receives and verifies the BIM model file of the electromechanical system input by the user. The BIM model file includes geometric data and attribute parameters.
[0023] Step 2: Data preprocessing; The preprocessing module performs preprocessing on the received BIM model data. Preprocessing includes data cleaning, feature extraction, and data standardization, as detailed below: Data cleaning: Repairing geometric errors in BIM models using a data cleaning engine, including removing redundancies and filling in missing parameters; Feature extraction: Extract key features such as electromechanical system-related parameters and energy consumption indicators from the BIM model; Data standardization: The extracted key features are transformed into a data format that is suitable for the input of AI models through a graph structure converter.
[0024] Step 3: AI optimization analysis; The AI optimization module performs the following measures: Analyze the relevant data of the MEP system in the BIM model (including the dimensions, location, and installation height of water pipes, air ducts, and cable trays); obtain the outputs required for this project, mainly pipeline integration and headroom analysis. Based on the required output results and combined with data from similar projects in the learning database, an integrated electromechanical system pipeline design is automatically generated using artificial intelligence (AI) to meet the required clearance and performance standards. AI is a computational model that simulates human intelligent behavior, mimicking the learning and processing methods of the human brain. By learning the characteristics and patterns of similar projects in the database, it completes the project optimization task.
[0025] Step 4: Verification and Feedback; The verification feedback module passes the optimization scheme generated by the AI optimization module in step 3 to the service layer. The service layer calls external service functions to perform physical simulation verification. At the same time, experts can manually review, mark, or fine-tune the optimization scheme through the manual review interface. If the verification result does not meet expectations, the feedback data is input into the AI model to adjust the parameters and re-optimize until the expected result is achieved, and then proceed to step 5. If the verification result meets expectations, proceed directly to step 5. The simulation verification includes: Import the optimized BIM model into the verification module; The verification module extracts data from the optimized model and combines it with the constraints of this project (net height analysis, pipeline collision, installation conditions, etc.) to generate verification results. The verification results are output to the feedback module. The operator judges whether the verification results meet the design requirements. If the requirements are met, the model is output. If the requirements are not met, step 3 is repeated and additional constraints are added.
[0026] Step 5: Output the results; The output module outputs a BIM model that meets the expected specifications, and simultaneously generates optimization records; The output BIM model includes a modification log, which records the parameter comparison before and after optimization, the AI decision-making basis, and the simulation verification results. Throughout the entire process described above, the data storage layer stores relevant data in real time and in layers.
[0027] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the self-optimization method for electromechanical systems based on BIM forward design and new AI technologies described in steps 1 to 5 above.
[0028] The present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory. When the processor executes the program, it implements the electromechanical system self-optimization method based on BIM forward design and new AI technology described in steps 1 to 5 above.
[0029] The embodiments described above are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention shall fall within the protection scope of the present invention.
Claims
1. A self-optimization platform for electromechanical systems based on BIM forward design and new AI technologies, characterized in that, include: The application layer provides a user interface that allows users to initiate optimization tasks through BIM platform plugins or integrated functions. It also displays optimization results, performance comparisons, or simulation reports in the form of charts or 3D views, and supports user decision-making. The service layer acts as a bridge between the front-end and back-end and internal and external services. It receives application layer requests through internal APIs, transmits processing results, realizes the separation of front-end and back-end, and integrates external service functions, calls the computing engine, and realizes simulation-driven optimization. The core processing layer includes a data input module, a preprocessing module, an AI optimization module, a verification feedback module, and an output module; The data input module supports parsing multiple BIM file formats; The preprocessing module has a built-in data cleaning engine and graph structure converter to perform data cleaning, extract the required data information from the model, and convert the extracted data information into a graph data structure through the graph structure converter. The AI optimization module enables users or the system to define multi-objective optimization problems through the target configurator. It uses various AI algorithms from the algorithm library, drives the algorithm execution through the inference engine, trains the model using historical data, and generates optimization solutions based on the current objectives and constraints. The verification and feedback module passes the optimization scheme generated by the AI optimization module to the service layer, calls external service functions to perform physical simulation verification, and allows experts to review, mark or fine-tune the AI scheme through the manual review interface. Based on the simulation results and manual feedback, the AI model parameters are adjusted. The output module writes the final optimized solution back to the BIM model, enabling automatic updates of the design files, and records a complete optimization process log for traceability, auditing, and performance analysis. The data storage layer stores related data in layers.
2. A method for self-optimization of electromechanical systems based on BIM forward design and AI technology, utilizing the self-optimization platform for electromechanical systems based on BIM forward design and AI technology as described in claim 1, characterized in that... The process includes the following: Step 1: Initiate an optimization task through the user interface. The data input module receives and verifies the electromechanical system BIM model file input by the user. Step 2: The preprocessing module preprocesses the received BIM model data, including data cleaning, feature extraction, and data standardization. Step 3: The AI optimization module performs optimization analysis on the BIM model and outputs optimization solutions; Step 4: The verification feedback module passes the optimization plan to the service layer. The service layer calls external service functions to perform physical simulation verification. At the same time, experts manually review, mark, or fine-tune the optimization plan through a manual review interface. If the verification result does not meet expectations, the feedback data is input into the AI model for parameter adjustment and re-optimization until the expected result is achieved, then proceed to Step 5. If the verification result meets expectations, proceed directly to Step 5. Step 5: The output module outputs the expected BIM model and generates optimization records.
3. The self-optimization method for electromechanical systems based on BIM forward design and new AI technology as described in claim 2, characterized in that, In step 2, the preprocessing specifically includes: Data cleaning: Repairing geometric errors in BIM models using a data cleaning engine, including removing redundancies and filling in missing parameters; Feature extraction: Extract key features of electromechanical system-related parameters and energy consumption indicators from the BIM model; Data standardization: The extracted key features are transformed into a data format that is suitable for the input of AI models through a graph structure converter.
4. The self-optimization method for electromechanical systems based on BIM forward design and new AI technology as described in claim 2, characterized in that, The specific process of step 3 is as follows: Analyze the relevant data of the MEP system in the BIM model, including the dimensions, location and installation height of water pipes, air ducts and cable trays; obtain the output results required for this project, including pipeline integration and headroom analysis; Based on the required output results and combined with data from similar projects in the learning database, AI-based automated generation of comprehensive electromechanical system pipeline results that meet the requirements of headroom and performance, i.e., optimization scheme.
5. The self-optimization method for electromechanical systems based on BIM forward design and new AI technology as described in claim 2, characterized in that, In step 5, the output BIM model includes a modification log, which records the parameter comparison before and after optimization, the AI decision-making basis, and the simulation verification results.
6. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the electromechanical system self-optimization method based on BIM forward design and new AI technology as described in claim 2.
7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the steps of the electromechanical system self-optimization method based on BIM forward design and new AI technology as described in claim 2.