Building design method and system based on bionic agent architecture

Abstract

The invention relates to a building design method based on a bionic agent architecture, and belongs to the technical field of building design, and the method comprises the following steps: S1, perception and cognition: analyzing the terrain, environment and sunlight characteristics of an open space scene, analyzing the style preference, function demand and budget constraint of a user, and generating a design constraint condition; s2, memory cognition: searching similar cases matched with design constraint conditions from a building database, wherein the matching comprises matching of visual, semantic and functional dimensions; s3, reasoning cognition: planning the spatial layout of the building based on the retrieved similar cases and design constraint conditions, balancing aesthetic and functional requirements, and evaluating technical feasibility and economic benefits; and S4, executing cognition: scheduling professional tools in the limb system to work cooperatively through the trunk system. According to the method, the pain points of low efficiency, weak intelligence, high threshold and difficult quality control of traditional building design are broken through, and multi-dimensional core values are brought from the aspects of technology, efficiency, quality, user experience and economy and society.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, and in particular to an architectural design method based on a biomimetic intelligent agent architecture. Background Technology

[0002] With the acceleration of global urbanization and the continuous increase in urban population density, the demand for architectural design has experienced explosive growth. This demand not only covers traditional scenarios such as residential, commercial, and public buildings, but also extends to diversified design needs such as green buildings, smart communities, and mixed-use developments like commercial + office + cultural tourism complexes. Against this backdrop, the architectural design industry faces both the practical challenges of a surge in project numbers, compressed design cycles, and complex functional requirements, and the transformative opportunities presented by the penetration of technologies such as artificial intelligence (AI), computer vision, and multimodal learning. The limitations of traditional design models and existing AI-assisted tools are becoming increasingly apparent, making it difficult to meet the industry's demands for high-quality and high-efficiency development.

[0003] Traditional manual design remains the industry mainstream, requiring multiple stages including "requirements communication → solution conception → sketching → CAD modeling → rendering → solution review → modification and iteration." The design process heavily relies on the designer's professional experience—for example, residential layouts require manual calculation of sunlight spacing, and commercial complexes require manual planning of pedestrian flow. Furthermore, each stage relies on fragmented tools (such as SketchUp modeling, Lumion rendering, and AutoCAD output), and the quality and efficiency of the design outcome depend entirely on the team's experience and collaboration. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the background technology by proposing a biomimetic intelligent agent architecture-based architectural design method that overcomes the pain points of low efficiency, weak intelligence, high threshold, and difficult quality control in traditional architectural design through a Brain-Trunk-Limbs biomimetic intelligent agent architecture and a six-stage cognitive closed loop. This method brings multi-dimensional core value from the perspectives of technology, efficiency, quality, user experience, and economic and social aspects.

[0005] The technical solution of this invention: A building design method based on a biomimetic intelligent agent architecture, the method comprising the following steps, S1. Perception and cognition: Analyze the terrain, environment, and sunlight characteristics of the open space scene, analyze the user's style preferences, functional requirements, and budget constraints, and generate design constraints. S2. Memory and cognition: Retrieve similar cases that match the design constraints from the building database, including matching in visual, semantic and functional dimensions; S3. Reasoning and cognition: Based on similar cases retrieved and design constraints, plan the spatial layout of the building, balance aesthetics and functional requirements, and evaluate technical feasibility and economic benefits; S4. Executive cognition: Through the trunk system, specialized tools in the limb system are coordinated to work together. These specialized tools include segmentation tools, detection tools, generation tools, editing tools, optimization tools, and retrieval tools. S5. Supervisory cognition: Evaluate the rationality of the design scheme through the brain system's quality supervision engine, identify design defects, and propose optimization suggestions; S6. Creative Cognition: Generate final renderings, animations, or videos of the architectural design, and output complete technical documents, including floor plans and architectural parameters.

[0006] Preferably, in step S1, image segmentation and feature extraction techniques are used when analyzing the features of the open-air scene, and natural language processing techniques are used when parsing user requirements to convert the user's input text requirements into structured constraint parameters.

[0007] Preferably, in step S2, when searching for similar cases, at least three cases with the highest similarity to the current design constraints are matched from the building database based on CLIP multimodal features and the Faiss fast retrieval algorithm.

[0008] Preferably, in step S3, when planning the spatial layout, it is necessary to complete the functional zoning, traffic flow design and building density calculation, and ensure that the building density and sunlight conditions comply with the relevant national building codes.

[0009] Preferably, in step S4, when scheduling professional tools in the trunk system, a multi-objective adaptive scheduling algorithm is adopted. The algorithm dynamically allocates the execution order and resource occupancy ratio of the tools based on task characteristics, tool status and resource status.

[0010] A building design system based on a biomimetic intelligent agent architecture includes, The brain system is used to realize cognitive decision-making, including a strategic decision-making engine, a quality supervision engine, and a building knowledge base. The strategic decision-making engine is responsible for demand understanding, design strategy planning, and goal decomposition. The quality supervision engine is responsible for design quality assessment and defect identification. The trunk system, used for coordination and scheduling, includes a tool scheduler, a parameter optimizer, and an execution monitor. The tool scheduler is used to realize intelligent scheduling of specialized tools, and the parameter optimizer is used to dynamically adjust the tool execution parameters based on quality feedback. The four-limb system is used for the execution of professional tools, including a segmentation tool group, a detection tool group, a generation tool group, an editing tool group, an optimization tool group, and a retrieval tool group. The segmentation tool group is used for image segmentation and feature extraction, and the generation tool group is used for the conditional generation of building layout and effects. The cognitive closed-loop controller is used to control the six-stage closed-loop process of perceptual cognition, memory cognition, reasoning cognition, executive cognition, supervisory cognition and creative cognition.

[0011] Preferably, the strategic decision-making engine and quality supervision engine of the brain system adopt a dual-engine dynamic attention collaboration algorithm, which dynamically allocates the attention weights of the two engines based on the features of the design phase.

[0012] Preferably, the tool groups of the limb system are provided with cross-tool feature adapters to convert the output features of different tools into a unified architectural design feature vector format, thereby achieving feature sharing.

[0013] Preferably, the execution monitor of the torso system is equipped with a deviation active detection module. The module monitors the execution data of the tool in real time based on the LSTM time series prediction algorithm, and triggers an interruption correction when the data exceeds the deviation warning threshold.

[0014] Preferably, it also includes a user feedback interface module, which is used to receive modification requests from users during the design process, and convert the modification requests into a requirement update vector and embed it into the perception and cognition stage of the brain system to realize dynamic adjustment of requirements.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This invention overcomes the pain points of traditional architectural design, such as low efficiency, weak intelligence, high threshold, and difficulty in quality control, through the Brain-Trunk-Limbs biomimetic intelligent agent architecture and a six-stage cognitive closed loop, bringing multi-dimensional core value from the perspectives of technology, efficiency, quality, user experience, and economic and social aspects.

[0016] Achieving a leap from "fragmented AI assistance" to "end-to-end intelligent agent": Dual-engine dynamic collaboration and cross-tool feature adaptation solve the problems of tool fragmentation and parameter isolation, enabling the system to have autonomous cognition, decision-making and correction capabilities without the need for manual process connection.

[0017] Design cycles are significantly reduced: traditional design processes that used to take weeks, such as residential community design, are now shortened to a few hours. Repetitive tasks such as image segmentation and layout generation are automated, and multi-objective scheduling algorithms reduce resource waste, improving overall design efficiency and enabling rapid response to changing market demands.

[0018] Build a full-process assurance system: The quality supervision engine, combined with the building code library, automatically verifies indicators such as sunlight, plot ratio, and fire distance. The deviation detection module intercepts design defects in advance, reducing the cost of later modifications.

[0019] Significantly lowers the professional threshold: Natural language requirements parsing allows non-professional users, such as developers, to directly participate in the design process, and the real-time feedback interface supports dynamic adjustments to requirements, such as adding a community center midway through the process, without having to restart the entire process; novice designers can shorten their learning cycle with the help of the system's intelligent suggestions.

[0020] It reduces manual design and modification costs, facilitating the rapid implementation of real estate projects; it promotes the digital transformation of the construction industry, and its green building optimization capabilities can improve energy efficiency, while lowering the learning threshold for design, cultivating new talents in AI-assisted design, and providing technical support for the sustainable development of the industry. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0026] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0027] Example 1 like Figure 1 As shown, this invention proposes a building design method based on a biomimetic intelligent agent architecture, which includes the following steps: S1. Perception and cognition: Analyze the terrain, environment, and sunlight characteristics of the open space scene, analyze the user's style preferences, functional requirements, and budget constraints, and generate design constraints. S2. Memory and cognition: Retrieve similar cases that match the design constraints from the building database, including matching in visual, semantic and functional dimensions; S3. Reasoning and cognition: Based on similar cases retrieved and design constraints, plan the spatial layout of the building, balance aesthetics and functional requirements, and evaluate technical feasibility and economic benefits; S4. Executive cognition: Through the trunk system, specialized tools in the limb system are coordinated to work together. These specialized tools include segmentation tools, detection tools, generation tools, editing tools, optimization tools, and retrieval tools. S5. Supervisory cognition: Evaluate the rationality of the design scheme through the brain system's quality supervision engine, identify design defects, and propose optimization suggestions; S6. Creative Cognition: Generate final renderings, animations, or videos of the architectural design, and output complete technical documents, including floor plans and architectural parameters.

[0028] In step S1, image segmentation and feature extraction techniques are used when analyzing the features of the open field scene, and natural language processing techniques are used when parsing user requirements to convert the user's input text requirements into structured constraint parameters.

[0029] In step S2, when searching for similar cases, based on CLIP multimodal features and the Faiss fast retrieval algorithm, at least three cases with the highest similarity to the current design constraints are matched from the building database.

[0030] In step S3, when planning the spatial layout, it is necessary to complete the functional zoning, traffic flow design and building density calculation, and ensure that the building density and sunlight conditions comply with the relevant national building codes.

[0031] In step S4, when scheduling professional tools in the trunk system, a multi-objective adaptive scheduling algorithm is adopted. The algorithm dynamically allocates the execution order and resource usage ratio of tools based on task characteristics, tool status and resource status.

[0032] In this embodiment, computer vision technology such as the SAM segmentation model is used to analyze environmental features such as terrain slope, vegetation distribution, and sunlight angle of the open ground image. At the same time, natural language processing is used to convert unstructured requirements such as modern style and accommodating 100 households into structured constraint parameters such as style tags, thus constructing a basic constraint library for the design.

[0033] Multimodal retrieval technology is used to retrieve cases that match the constraint parameters from the building database, such as residential projects with the same floor area ratio and similar terrain, so as to reuse historical design experience.

[0034] By combining search cases and constraints, functional zoning is completed through spatial reasoning algorithms, such as residential / public / green space zoning, traffic flow, pedestrian-vehicle separation design, indicator calculation, building density, and sunlight spacing, to ensure that the scheme meets both aesthetic requirements and regulatory requirements.

[0035] The toolchain, coordinated by the trunk system and the limb system, works collaboratively. For example, the segmentation tool extracts terrain features, the generation tool generates the layout, the editing tool adjusts the building positions, and the optimization tool improves the quality of the renderings, thus achieving the automated generation of design schemes.

[0036] The quality monitoring engine assesses the compliance of a project by using pre-trained building code models, such as solar radiation analysis models and fire safety distance detection models.

[0037] By integrating the results from each stage, visual materials such as bird's-eye views and floor plans are generated, along with technical documents containing building height and material parameters, forming a design scheme that can be delivered directly.

[0038] This invention overcomes the pain points of traditional architectural design, such as low efficiency, weak intelligence, high threshold, and difficulty in quality control, through the Brain-Trunk-Limbs biomimetic intelligent agent architecture and a six-stage cognitive closed loop, bringing multi-dimensional core value from the perspectives of technology, efficiency, quality, user experience, and economic and social aspects.

[0039] Achieving a leap from "fragmented AI assistance" to "end-to-end intelligent agent": Dual-engine dynamic collaboration and cross-tool feature adaptation solve the problems of tool fragmentation and parameter isolation, enabling the system to have autonomous cognition, decision-making and correction capabilities without the need for manual process connection.

[0040] Design cycles are significantly reduced: traditional design processes that used to take weeks, such as residential community design, are now shortened to a few hours. Repetitive tasks such as image segmentation and layout generation are automated, and multi-objective scheduling algorithms reduce resource waste, improving overall design efficiency and enabling rapid response to changing market demands.

[0041] Build a full-process assurance system: The quality supervision engine, combined with the building code library, automatically verifies indicators such as sunlight, plot ratio, and fire distance. The deviation detection module intercepts design defects in advance, reducing the cost of later modifications.

[0042] Significantly lowers the professional threshold: Natural language requirements parsing allows non-professional users, such as developers, to directly participate in the design process, and the real-time feedback interface supports dynamic adjustments to requirements, such as adding a community center midway through the process, without having to restart the entire process; novice designers can shorten their learning cycle with the help of the system's intelligent suggestions.

[0043] It reduces manual design and modification costs, facilitating the rapid implementation of real estate projects; it promotes the digital transformation of the construction industry, and its green building optimization capabilities can improve energy efficiency, while lowering the learning threshold for design, cultivating new talents in AI-assisted design, and providing technical support for the sustainable development of the industry.

[0044] Example 2 like Figure 1 As shown, the present invention proposes a building design system based on a biomimetic intelligent agent architecture, comprising, The brain system is used to realize cognitive decision-making, including a strategic decision-making engine, a quality supervision engine, and a building knowledge base. The strategic decision-making engine is responsible for demand understanding, design strategy planning, and goal decomposition. The quality supervision engine is responsible for design quality assessment and defect identification. The trunk system, used for coordination and scheduling, includes a tool scheduler, a parameter optimizer, and an execution monitor. The tool scheduler is used to realize intelligent scheduling of specialized tools, and the parameter optimizer is used to dynamically adjust the tool execution parameters based on quality feedback. The four-limb system is used for the execution of professional tools, including a segmentation tool group, a detection tool group, a generation tool group, an editing tool group, an optimization tool group, and a retrieval tool group. The segmentation tool group is used for image segmentation and feature extraction, and the generation tool group is used for the conditional generation of building layout and effects. The cognitive closed-loop controller is used to control the six-stage closed-loop process of perceptual cognition, memory cognition, reasoning cognition, executive cognition, supervisory cognition and creative cognition.

[0045] The strategic decision-making engine and quality supervision engine of the brain system adopt a dual-engine dynamic attention collaboration algorithm, which dynamically allocates the attention weights of the two engines based on the features of the design phase.

[0046] Dual-engine dynamic attention collaboration algorithm, attention weight calculation: S: Stage feature vector; D: Output characteristics of the strategic decision-making engine; dk: Feature dimension, controls weight scaling; The attention weights of the decision / evaluation engine are dynamically allocated to ensure stage adaptation.

[0047] The four-limb system has cross-tool feature adapters between its various tool groups to convert the output features of different tools into a unified architectural design feature vector format, thereby enabling feature sharing.

[0048] Eigenvector transformation: F_original: The original features output by the tool, such as the mask matrix of the segmentation tool; M: The feature transformation matrix is ​​learned through a pre-trained adaptation model; b: Bias term; F-unification: The converted standard feature vector, such as the terrain feature vector, enables feature compatibility between tools.

[0049] The execution monitor of the torso system is equipped with a deviation active detection module. The module monitors the execution data of the tool in real time based on the LSTM time series prediction algorithm. When the data exceeds the deviation warning threshold, it triggers an interruption correction.

[0050] It also includes a user feedback interface module, which is used to receive user modification requests during the design process and convert the modification requests into requirement update vectors and embed them into the perception and cognition stage of the brain system to achieve dynamic adjustment of requirements.

[0051] In this embodiment, the brain system consists of a dual-engine architecture and a knowledge base. The strategic decision-making engine is responsible for breaking down user needs into actionable tasks, such as converting modern style into straight lines and glass curtain wall design elements. The quality supervision engine detects defects in the scheme through a pre-trained evaluation model. The architectural knowledge base stores knowledge in fields such as specifications, materials, and case studies. Trunk System: As the "central coordinator", the tool scheduler calls the limb tools through the function calling mechanism. The parameter optimizer dynamically adjusts the parameters of the generated tools based on supervision feedback, such as insufficient sunlight, such as increasing the building spacing parameter to 1.2 times. The execution monitor tracks the tool's running status in real time, such as calculation time and output quality. The limb system includes six tool groups: the segmentation tool group processes image features, the detection tool group locates key objects, the generation tool group generates layouts and effects, the editing tool group adjusts local elements, the optimization tool group repairs details, and the retrieval tool group matches cases. Cognitive closed-loop controller: It controls the flow of the six-stage process through a state machine. For example, when the supervisory cognition discovers a defect, it triggers the reasoning cognition to replan, ensuring the continuity and self-correction capability of the design process.

[0052] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for architectural design based on a biomimetic agent architecture, characterized by: The method comprises the following steps, S1. Perception cognition: analyze the terrain, environment, and sunlight characteristics of the site, analyze the user's style preferences, functional requirements, and budget constraints, and generate design constraints; S2. Memory cognition: retrieve similar cases from the building database that match the design constraints, including visual, semantic, and functional dimensions; S3. Reasoning cognition: based on the retrieved similar cases and design constraints, plan the spatial layout of the building, balance aesthetic and functional requirements, and evaluate technical feasibility and economic benefits; S4. Execution cognition: through the torso system, dispatch professional tools in the limb system to work together, including segmentation tools, detection tools, generation tools, editing tools, optimization tools, and retrieval tools; S5. Supervision cognition: through the quality supervision engine of the brain system, evaluate the rationality of the design scheme, identify design defects and propose optimization suggestions; S6. Creative cognition: generate the final rendering, animation or video of the architectural design, and output complete technical documents, including floor plan and building parameters.

2. The method of architectural design based on the bionic agent architecture according to claim 1, characterized in that, In step S1, when analyzing the site characteristics, image segmentation and feature extraction techniques are used, and when analyzing user requirements, natural language processing techniques are used to convert user input text requirements into structured constraint parameters.

3. The method of architectural design based on the bionic agent architecture according to claim 2, characterized in that, In step S2, when retrieving similar cases, based on CLIP multi-modal features and Faiss fast retrieval algorithm, at least 3 cases with the highest similarity to the current design constraints are matched from the building database.

4. The method of architectural design based on the bionic agent architecture according to claim 3, characterized in that, In step S3, when planning the spatial layout, functional zoning, traffic flow line design and building density calculation are required, and the building density and sunlight conditions must comply with relevant national building standards.

5. The method of architectural design based on the bionic agent architecture according to claim 4, characterized in that, In step S4, when the torso system dispatches professional tools, a multi-objective adaptive scheduling algorithm is used, which dynamically allocates the execution order and resource occupation ratio of the tools based on task characteristics, tool state and resource state.

6. A building design system based on a bionic agent architecture, based on the building design method based on a bionic agent architecture according to claim 9, characterized in that, It includes, The brain system is used to implement cognitive decision-making, including a strategic decision engine, a quality supervision engine, and a building professional knowledge base. The strategic decision engine is responsible for requirement understanding, design strategy planning and target decomposition. The quality supervision engine is responsible for design quality evaluation and defect identification. The torso system is used for coordination and scheduling, including a tool scheduler, a parameter optimizer, and an execution monitor. The tool scheduler is used to implement intelligent scheduling of professional tools. The parameter optimizer is used to dynamically adjust tool execution parameters based on quality feedback. The limb system is used for professional tool execution, including a segmentation tool group, a detection tool group, a generation tool group, an editing tool group, an optimization tool group, and a retrieval tool group. The segmentation tool group is used for image segmentation and feature extraction. The generation tool group is used for building layout and effect generation. The cognitive closed-loop controller controls the six-stage closed-loop process of perception cognition, memory cognition, reasoning cognition, execution cognition, supervision cognition, and creative cognition.

7. The building design system based on the bionic agent architecture according to claim 6, characterized in that, The strategic decision engine and the quality supervision engine of the brain system use a dual-engine dynamic attention collaboration algorithm that dynamically allocates attention weights for the two engines based on design stage characteristics.

8. The building design system based on the bionic agent architecture according to claim 7, characterized in that, Cross-tool feature adapters are provided between each tool group of the limb system to convert output features of different tools into a unified architectural design feature vector format, enabling feature sharing.

9. The building design system based on the bionic agent architecture according to claim 8, characterized in that, An execution monitor of the torso system is provided with a deviation active detection module, which monitors tool execution data in real time based on an LSTM time series prediction algorithm, and triggers an interruption correction when the data exceeds a deviation warning threshold.

10. The building design system based on the bionic agent architecture according to claim 9, characterized in that, A user feedback interface module is also included to receive user modification requirements during the design process and convert the modification requirements into requirement update vectors embedded into the perception and cognition stage of the brain system, enabling dynamic adjustment of requirements.

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