Building design method and system based on multi-objective optimization

Abstract

The invention discloses a building design method and system based on multi-objective optimization, and the method comprises the following steps: determining multi-objective optimization indexes of building design, the multi-objective optimization indexes including a human factor suitability index, a resource circulation efficiency index and a culture suitability index; quantitative data is obtained by adopting a meta-universe rehearsal technology for the human factor suitability index, quantitative data is obtained by adopting a biological simulation algorithm for the resource circulation efficiency index, and quantitative data is obtained by adopting a block chain technology for the culture suitability index; and inputting the quantized data into a multi-objective optimization algorithm, and carrying out collaborative optimization calculation to obtain building design optimization parameters, thereby breaking through the single-objective limitation of energy conservation, cost and space of traditional building design, bringing human factor suitability, resource circulation efficiency and culture suitability into a multi-objective optimization system, and realizing collaborative optimization of humanity + green + culture.

Description

Technical Field

[0001] This invention belongs to the field of architectural design technology, and in particular relates to an architectural design method and system based on multi-objective optimization. Background Technology

[0002] Multi-objective building design technologies are widely used in the construction industry, with optimization centered around three main objectives: energy conservation, cost reduction, and space utilization. Currently, mainstream technologies use BIM (Building Information Modeling) as the core platform, simulating building energy consumption, engineering quantities, and spatial layout through the construction of 3D models. These models are then combined with basic multi-objective optimization algorithms such as NSGA-II and MOPSO to iteratively calculate building shape coefficients, envelope parameters, and building material selection, outputting the Pareto optimal solution. Some technologies also incorporate artificial intelligence to assist in predicting energy consumption or collect building operation data through the Internet of Things. Application scenarios have covered residential, public, and high-rise buildings, achieving certain results in reducing building energy consumption and controlling construction costs, becoming a common technical means in modern building design.

[0003] Considering the beneficial effects of this invention, the existing technology has obvious limitations. In terms of optimization objectives, it only focuses on energy saving, cost, and space, failing to incorporate human factors adaptability, resource recycling efficiency, and cultural adaptability into the system, thus failing to meet the needs of humanistic care and cultural inheritance. At the level of technology integration, it is mostly a simple superposition of BIM and a single algorithm, without deep integration with metaverse, biological simulation, and blockchain. Data collection relies on manual labor or single simulations, resulting in insufficient accuracy and efficiency. The "method-system" synergy is poor; the method only proposes a process, and the system is mostly a single functional module, lacking a complete closed loop. The optimization process lacks a reliable traceability mechanism, making it difficult to verify data and decision-making basis. Scene adaptability is limited; the technical framework needs to be adjusted separately for different building needs, lacking universality and failing to match the diversified and multi-scenario design requirements of modern architecture. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a building design method and system based on multi-objective optimization, which solves the problems of single objective, shallow technology integration, poor coordination, lack of traceability, low universality, and difficulty in adapting to diverse needs in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A building design method based on multi-objective optimization includes the following steps: S1. Determine the multi-objective optimization indicators for architectural design, including human factors adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators; S2. For human-factor adaptability indicators, metaverse pre-simulation technology is used to obtain quantitative data; for resource recycling efficiency indicators, biological simulation algorithm is used to obtain quantitative data; and for cultural adaptability indicators, blockchain technology is used to obtain quantitative data. S3. Input the quantified data into a multi-objective optimization algorithm to perform collaborative optimization calculations and obtain the building design optimization parameters; S4. Output the architectural design scheme based on the architectural design optimization parameters.

[0006] Preferably, in step S1, the human factors adaptability index includes quantitative values ​​of physiological needs and psychological needs for different groups of people; the resource recycling efficiency index includes the recycling rate of building materials, the proportion of recycled building materials, and the material recycling rate after demolition; the cultural adaptability index includes the degree of restoration of regional cultural elements and the quantitative value of modern functional compatibility.

[0007] Preferably, in step S2, quantitative data of human-factor adaptability indicators are obtained through metaverse pre-simulation technology. Specifically, this includes: constructing a virtual metaverse model of a building, simulating the behavior of different groups such as the elderly, children, and people with disabilities in the virtual metaverse model, and collecting and converting the quantitative values ​​of physiological needs and psychological needs.

[0008] Preferably, in step S2, the quantitative data of resource recycling efficiency indicators are obtained through a biological simulation algorithm, specifically including: constructing a biological simulation optimization sub-algorithm by drawing on the material cycling laws of biological ecosystems, and converting the resource recycling efficiency indicators into quantitative parameters similar to biological cycles.

[0009] Preferably, in step S2, quantitative data of cultural compatibility indicators are obtained through blockchain technology, specifically including: using blockchain to store regional cultural element data, extracting data from the blockchain and converting it to obtain quantitative values ​​of the degree of restoration of regional cultural elements and compatibility with modern functions.

[0010] Preferably, in step S3, the multi-objective optimization algorithm is integrated with the biological simulation optimization sub-algorithm, using the quantitative data of resource recycling efficiency index as a constraint, and co-optimizing calculation with the quantitative data of human factor adaptability index and cultural adaptability index.

[0011] Preferably, a building design system based on multi-objective optimization includes: The indicator determination module is used to determine multi-objective optimization indicators for architectural design, including human factors adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators. The data acquisition module is used to acquire corresponding quantitative data for human-factor adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators, respectively, using metaverse pre-simulation technology, biological simulation algorithms, and blockchain technology. The algorithm optimization module is used to receive the quantized data transmitted by the data acquisition module, input the quantized data into the multi-objective optimization algorithm for collaborative optimization calculation, and obtain the building design optimization parameters. The scheme output module is used to receive architectural design optimization parameters transmitted by the algorithm optimization module and output architectural design schemes based on the architectural design optimization parameters.

[0012] Preferably, the data acquisition module includes a metaverse data acquisition unit, which is used to construct a virtual metaverse model of the building, simulate the usage behavior of different groups such as the elderly, children and people with disabilities, and collect and transform quantitative data of human-factor adaptability indicators.

[0013] Preferably, the data acquisition module includes a biological simulation data processing unit, which is used to convert resource cycle efficiency indicators into biological cycle-like quantitative parameters through a biological simulation optimization sub-algorithm.

[0014] Preferably, the data acquisition module includes a blockchain data storage unit, which is used to store regional cultural element data and extract data to obtain quantitative data of cultural adaptability indicators.

[0015] The technical effects and advantages of the architectural design method and system based on multi-objective optimization of this invention are as follows: 1. This invention breaks through the limitations of traditional architectural design's single objective of "energy saving, cost, and space," incorporating human factors adaptability, resource recycling efficiency, and cultural adaptability into a multi-objective optimization system to achieve synergistic optimization of "humanities + green + culture." Specifically, the human factors adaptability index covers the physiological and psychological needs of different groups, ensuring the building's suitability for various user groups; the resource recycling efficiency index runs through the entire life cycle of the building—"design-use-demolition-recycling"—adapting to the needs of circular economy development; and the cultural adaptability index achieves the quantitative integration of regional cultural elements, avoiding the problem of "one-size-fits-all" architecture and comprehensively meeting the diversified needs of modern architecture for humanistic care, green environmental protection, and cultural inheritance.

[0016] 2. This invention deeply integrates metaverse pre-simulation, biological simulation, blockchain technology, and multi-objective optimization algorithms to form a unique "objective-technology" adaptation system. Metaverse pre-simulation technology constructs virtual models to simulate the usage behavior of multiple user groups, accurately acquiring quantitative data on human-factor adaptability and solving the problem of large errors in traditional manual surveys. The biological simulation algorithm draws on the laws of biological ecosystems to optimize resource recycling paths and improve the calculation rationality of resource recycling efficiency indicators. Blockchain technology constructs a consortium blockchain to store regional cultural element data, ensuring data immutability and traceability, addressing the pain point of strong subjectivity in cultural adaptability optimization. Simultaneously, the integration of the biological simulation sub-algorithm and the multi-objective optimization main algorithm reduces the number of iterations and significantly improves optimization efficiency.

[0017] 3. The invention's method and system correspond one-to-one, forming a complete closed loop of "target determination - data acquisition - algorithm optimization - solution output". At the methodological level, the technical logic and parameter requirements of each step are clearly defined. At the system level, the collaborative work of the indicator determination, data acquisition, algorithm optimization, and solution output modules automates the implementation of the methodological process. Information is transmitted between modules through data interfaces, eliminating the need for secondary manual intervention, shortening the architectural design cycle, reducing design costs, and solving the problems of disconnect between "method and system" and poor implementation in traditional technologies. It can be directly applied to actual architectural design projects.

[0018] 4. This invention uses blockchain technology to record key information in the multi-objective optimization process, including objective weight settings, data sources, and algorithm iteration parameters, forming an immutable optimization record. Designers can trace the basis for each optimization step through the blockchain and verify the rationality of the optimization results; relevant parties can also confirm the authenticity of indicators such as cultural elements and human factors needs through on-chain data, significantly improving the credibility of architectural design schemes and avoiding disputes arising from "highly subjective decision-making and unverifiable data" in traditional optimization processes.

[0019] 5. This invention adapts to different scenarios such as residential buildings, public buildings, rural buildings, high-rise buildings, and temporary emergency buildings by adjusting the weights and technical parameters of multi-objective optimization indicators. It flexibly adjusts the priority of objectives and the technical implementation methods to address the core needs of different buildings, without requiring large-scale modifications to the methods and systems, thus possessing broad application scope and promotional value. Attached Figure Description

[0020] Figure 1 This is a flowchart of a multi-objective optimization-based architectural design method and system proposed in this invention; Figure 2 This is a system flowchart of a multi-objective optimization-based architectural design method and system proposed in this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] Example 1 refer to Figure 1-2 This embodiment provides a multi-objective optimization-based architectural design method and system for urban family-friendly housing architectural design (emphasizing human factors adaptation optimization). Specific implementation details include: Purpose of implementation: Targeting the characteristics of urban family housing where "children, adults, and the elderly live together," this study takes human factors adaptability as the core optimization objective, with a weight of 0.4, and coordinates resource recycling efficiency (weight 0.3) and cultural adaptability (weight 0.3) to verify the feasibility of the entire process of determining multi-objective indicators, acquiring cross-border technical data, optimizing algorithms, and outputting solutions, as well as the feasibility of supporting system modules.

[0024] Method implementation process: S1: Determine the multi-objective optimization index: Human factors adaptability indicators include physiological and psychological needs. Physiological needs specifically include a doorknob height of 0.75 meters for children, a handrail height of 0.85 meters for the elderly, and a kitchen countertop height of 0.88 meters for adults. Psychological needs specifically include a color saturation of 35% for children's rooms, a color temperature of 3200K for elderly rooms, and a living room lighting coefficient of 2.2%. Resource recycling efficiency indicators are set at a 30% proportion of recycled building materials, a 60% recycling rate for building materials, and a 50% rainwater harvesting rate. Cultural adaptability indicators are set at a 70% reproduction rate of regional "New Chinese" cultural elements (such as the meander pattern and wooden lattice) and a 90% compatibility with modern functions.

[0025] S2: Obtaining quantitative data through cross-disciplinary technologies: For human factors adaptability indicators, data was obtained using metaverse pre-simulation technology: a 1:1 virtual residential model was built using Unity3D, including functional areas such as children's rooms and elderly rooms, with millimeter-level precision; 30 families were recruited (children aged 5-8, adults aged 25-40, and elderly aged 60-75), and behavioral data (children's door opening success rate, elderly people's comfort in using handrails, and adults' fatigue in operating countertops) were collected using OptiTrack motion capture equipment; these data were converted into quantitative values: children's door handle adaptability rate 95% (0.95), elderly people's handrail adaptability rate 92% (0.92), living room lighting coefficient 2.2% (0.9), and the overall human factors quantitative value was 0.92.

[0026] For resource recycling efficiency indicators, a biological simulation algorithm was used to obtain data: drawing on the cycle pattern of "fallen leaves-humus-plants", a "building materials-use-recycling" sub-algorithm was constructed using MATLAB; the parameters of recycled steel bars (strength 400MPa) and recycled aggregate concrete (strength C30) were input; the output quantitative values ​​were: recycled building materials ratio 31% (0.85), recycling rate 62% (0.82), rainwater recycling rate 52% (0.85), and comprehensive resource quantitative value 0.84.

[0027] To assess cultural compatibility, blockchain technology was used to acquire data: a consortium blockchain consisting of a design institute, a cultural bureau, and universities was established to store data on the new Chinese-style meander pattern (8 cm spacing) and wooden grilles (10 cm spacing); data was extracted and compared with design schemes through smart contracts; and converted into quantitative values: meander pattern reproduction 72% (0.72), wooden grille reproduction 71% (0.71), functional compatibility 91% (0.91), and a comprehensive cultural quantitative value of 0.78.

[0028] S3: Multi-objective optimization algorithm collaborative computation: The NSGA-II algorithm was selected, with a population size of 100 and 500 iterations. A biological simulation sub-algorithm was embedded into the fitness function, constrained by a resource quantification value (0.84). The fitness calculation formula was "0.4 × human factor value + 0.3 × resource value + 0.3 × culture value". Optimization parameters were output through iteration. Human factors parameters: child door handle 0.75 meters, elderly handrail 0.85 meters, living room lighting 2.2%; Resource parameters: 60% recycled steel bars, 31% recycled concrete, 5 cubic meters of rainwater tank; Cultural parameters: Spacing of the meander pattern is 8.2 cm, and spacing of the wooden grille is 10.1 cm.

[0029] S4: Output architectural design scheme: The following floor plan is generated for a 3-bedroom, 2-living room apartment: the children's room is equipped with a 0.75-meter door handle, and the elderly's room is equipped with a 0.85-meter handrail; the exterior walls are made of 31% recycled concrete, and the roof is equipped with a 5-cubic-meter rainwater pool; the entrance hall is decorated with a meander pattern, and the balcony is equipped with a wooden lattice fence.

[0030] System implementation process: The indicator determination module uses an industrial computer (configured with an i7-12700K) running Python indicator software. After inputting "family-friendly housing," it automatically generates the aforementioned indicator list, setting the weights to 0.4, 0.3, and 0.3. The data acquisition module comprises three units: a metaverse data acquisition unit (RTX4090 workstation + OptiTrack device) to construct a virtual model and collect human factors data; a bio-simulation data processing unit (AMDEPYC server) to run sub-algorithms and output resource data; and a blockchain data storage unit (4 Xeon node servers) to store cultural data and extract quantified values ​​through smart contracts. The algorithm optimization module consists of an 8-server A100 cluster, running the DEAP library, integrating sub-algorithms to complete calculations, and outputting optimized parameters. The solution output module is configured with an RTX4080 workstation, running AutoCAD and Revit software to generate construction drawings and 3D models, and then outputting paper solutions via a plotter.

[0031] Implementation results: In terms of human factors adaptability, the success rate of children opening doors is 95%, the adaptability of handrails for the elderly is 92%, and the comfort score is 8.8 out of 10. In terms of resource recycling efficiency, the proportion of recycled building materials is 31%, and the recycling rate is 62%, which are 35% and 25% higher than traditional housing, respectively. In terms of cultural adaptability, the restoration of new Chinese style elements is 71.5%, and it has been certified as a "Cultural Integration Demonstration Housing" by the local cultural bureau.

[0032] Example 2 This embodiment provides a multi-objective optimization-based architectural design method and system for urban community library design (emphasizing cultural adaptability optimization). Specific implementation details include: Purpose of implementation: To address the needs of community libraries for "cultural display + public reading," this study takes cultural adaptability as the core objective, with a weight of 0.35, and coordinates human factors adaptability (weight 0.35) and resource recycling efficiency (weight 0.3) to verify the quantitative method of cultural adaptability indicators and the feasibility of blockchain technology.

[0033] Method implementation process: S1: Determine the multi-objective optimization index: Human factors adaptability indicators are: 400 lux of light in the reading area, noise ≤40 dB, and bookshelf height 2.1 meters; resource recycling efficiency indicators are: 35% of recycled building materials and 20% of solar power generation; cultural adaptability indicators are: 75% reproduction of regional "academy culture" (flying eaves, bamboo scroll patterns) and 95% compatibility of reading functions.

[0034] S2: Obtaining quantitative data through cross-disciplinary technologies: For the human factor adaptability index, the metaverse pre-simulation technology was used to obtain data: Unreal Engine 5 was used to build a library model and 100 readers were organized to conduct VR simulation; the output human factor quantification value was 0.9 (illuminance 420 lux, noise 38 dB).

[0035] For the resource recycling efficiency index, a biological simulation algorithm is used to obtain data: drawing on the law of "plant photosynthesis", a "photovoltaic-energy storage" sub-algorithm is constructed; the output resource quantification value is 0.86 (36% recycled building materials and 22% photovoltaic).

[0036] For cultural compatibility indicators, blockchain technology is used to acquire data: the data of the academy's eaves (30° slope) and bamboo slip patterns (5 cm spacing) are stored through a consortium blockchain; the smart contract outputs a cultural quantification value of 0.82 (76% fidelity and 96% compatibility).

[0037] S3: Multi-objective optimization algorithm collaborative computation: The MOPSO algorithm was selected, with a particle count of 120 and an iteration count of 600. The output optimization parameters were: bookshelf height 2.1 meters, photovoltaic panel area 600 square meters, and eaves slope 30.2°.

[0038] S4: Output architectural design scheme: A three-layer frame structure was generated: the reading area is equipped with an intelligent lighting and sound insulation system; 600 square meters of photovoltaic panels are installed on the roof; the entrance is decorated with a 30.2° eave and the walls are carved with bamboo scroll patterns.

[0039] System implementation process: The blockchain unit in the data acquisition module has added a museum node to ensure the authenticity of the academy's cultural data; the algorithm module uses the MOPSO algorithm to adapt to the complex cultural parameter calculation requirements.

[0040] Implementation results: In terms of cultural adaptability, the academy elements are 76% faithfully reproduced, and the number of readers received annually has increased by 22%; in terms of human factors adaptability, the reading comfort score is 9.0, and the convenience rate for elderly people to pick up books is 90%; in terms of resource recycling efficiency, photovoltaic power accounts for 22%, saving 300,000 yuan in electricity costs annually.

[0041] Example 3 This embodiment provides a multi-objective optimization-based architectural design method and system for the design of distinctive rural dwellings in rural revitalization (focusing on resource recycling efficiency optimization). Specific implementation details include: Purpose of implementation: To address the demand for "low cost + local culture" in rural housing, this study takes resource recycling efficiency as the core objective, with a weight of 0.4, and coordinates human factors adaptability (weight 0.3) and cultural adaptability (weight 0.3) to verify the effectiveness of the bio-simulation algorithm in the application of locally recycled building materials.

[0042] Method implementation process: S1: Determine the multi-objective optimization index: Human factors adaptability indicators are: 16 square meters for farm tool shed and 32% greening rate for courtyard; resource recycling efficiency indicators are: 70% proportion of locally recycled rammed earth, building material cost ≤ 1500 yuan / square meter, and rainwater recycling rate 55%; cultural adaptability indicators are: 80% restoration of local "sloping roof and wooden window lattice" and 90% compatibility with production functions.

[0043] S2: Obtaining quantitative data through cross-disciplinary technologies: For the human factors adaptability index, the metaverse pre-simulation technology was used to obtain data: a residential model was built using SketchUp+Enscape, and 50 villagers were organized to simulate the storage of farm tools; the output human factors quantification value was 0.87 (farm tool adaptability rate 90%, greening rate 32%).

[0044] For the resource recycling efficiency index, a biological simulation algorithm was used to obtain data: drawing on the pattern of "rice straw recycling in rural areas", a sub-algorithm of "rammed soil-use-return to the field" was constructed; the output resource quantification value was 0.88 (rammed soil 72%, cost 1260 yuan / square meter, rainwater recycling 56%).

[0045] For cultural compatibility indicators, blockchain technology is used to acquire data: local element data is stored through village committee and township government nodes; the output cultural quantification value is 0.85 (82% restoration of pitched roof and 81% restoration of window lattice).

[0046] S3: Multi-objective optimization algorithm collaborative computation: The NSGA-II algorithm was selected, with a population size of 80 and an iteration count of 400. The output optimization parameters were: 72% rammed earth, 16.2 square meters for the farm implements, and 28° slope for the sloping roof.

[0047] S4: Output architectural design scheme: A single-layer rammed earth structure is proposed: a 16.2-square-meter farm tool shed and a 22-square-meter drying platform are provided; the roof slope is 28°, and the windows are made of wooden frames; a 6-cubic-meter rainwater pool is set up in the courtyard.

[0048] System implementation process: The bio-simulation unit is adapted to local rammed earth and bamboo parameters; the solution output module generates simple construction drawings to meet the needs of rural construction teams.

[0049] Implementation results: In terms of resource recycling efficiency, the proportion of locally recycled building materials is 72%, with a cost of 1,260 yuan per square meter, which is 25% lower than that of traditional dwellings; in terms of cultural adaptability, the restoration of local elements is 81.5%, and it has been selected as a "Demonstration Dwelling for Rural Revitalization"; in terms of human factors adaptability, the villagers' living satisfaction score is 8.7 points, and the efficiency of farm tool storage has increased by 40%.

[0050] Example 4 This embodiment provides a multi-objective optimization-based architectural design method and system for the design of super high-rise green office buildings (emphasizing multi-objective collaborative optimization). Specific implementation details include: Purpose of implementation: To address the "safety + energy saving + landmark culture" requirements of super high-rise office buildings, we conducted collaborative optimization based on four objectives: "human factors + resources + culture + safety" (weights 0.25 / 0.3 / 0.25 / 0.2), and verified the multi-objective collaboration and adaptability to super high-rise scenarios.

[0051] Method implementation process: S1: Determine the multi-objective optimization index: Human factors adaptability indicators are 10.5 square meters of office space per person and elevator waiting time ≤28 seconds; resource recycling efficiency indicators are 70% recycled steel and 25% renewable energy; cultural adaptability indicators are 80% reproduction of the city's "technological landmark" (gradient glass, streamlined top); safety indicators are wind resistance of level 12 and earthquake resistance of level 8.

[0052] S2: Obtaining quantitative data through cross-disciplinary technologies: For human factors and safety indicators, the Metaverse Pre-simulation technology was used to obtain data: a 60-story model was built using Bentley software to simulate wind load and earthquake effects, and 200 employees were organized to simulate office work; the output human factors quantification value was 0.91 (10.5 square meters per person, 27 seconds elevator waiting time) and safety quantification value was 0.93.

[0053] For the resource recycling efficiency index, a biological simulation algorithm was used to obtain data: drawing on the pattern of "high-altitude bird migration", an "energy-building materials" sub-algorithm was constructed; the output resource quantification value was 0.89 (72% recycled steel and 26% renewable energy).

[0054] For cultural compatibility indicators, blockchain technology is used to acquire data: landmark data is stored through nodes of the planning bureau and real estate company; the output cultural quantification value is 0.83 (glass reproduction degree 82%, top reproduction degree 81%).

[0055] S3: Multi-objective optimization algorithm collaborative computation: The MOPSO algorithm was selected, with a particle count of 150 and an iteration count of 700, and safety parameters were incorporated. The output optimization parameters were: 72% recycled steel, 1200 square meters of photovoltaic panels, and 15% glass gradient.

[0056] S4: Output architectural design scheme: A 60-story steel-concrete structure was designed: the exterior walls are made of 72% recycled steel and gradient glass; the roof is equipped with 1,200 square meters of photovoltaic panels and a wind turbine; and the top features a streamlined design.

[0057] System implementation process: The metaverse unit has added a mechanical simulation module; the algorithm module incorporates safety constraints to ensure the performance of ultra-high-rise buildings.

[0058] Implementation results: In terms of safety, it can withstand winds of level 12 and earthquakes of level 8, and has passed structural safety certification; in terms of resource recycling efficiency, renewable energy accounts for 26%, saving 1.5 million yuan in energy costs annually; in terms of cultural compatibility, it has become a landmark on the city skyline, and the occupancy rate of enterprises has increased by 25%.

[0059] Example 5 This embodiment provides a multi-objective optimization-based architectural design method and system for the design of temporary emergency housing (emphasizing rapid resource recycling optimization). Specific implementation details include: Purpose of implementation: To address the need for "rapid construction and high recycling" in emergency housing, this study focuses on resource recycling efficiency as the core objective, with a weight of 0.45. It also considers human factors adaptability (weight 0.35) and cultural adaptability (weight 0.2) to verify the application of a biosimulation algorithm in rapid recycling scenarios.

[0060] Method implementation process: S1: Determine the multi-objective optimization index: Human factors suitability indicators are 6.2 square meters of living space per person, a sanitation ratio of 1:18, and an indoor temperature of 22℃; resource recycling efficiency indicators are construction time ≤ 3.5 hours / building, recycling rate ≥ 90%, and reuse ≥ 5 times; cultural suitability indicators are 70% restoration of "auspicious patterns" in disaster areas and 85% compatibility in psychological comfort.

[0061] S2: Obtaining quantitative data through cross-disciplinary technologies: For the human factor adaptability index, the metaverse pre-simulation technology was used to obtain data: a modular model was built using 3ds Max, and 100 simulated disaster victims were organized for testing; the output human factor quantification value was 0.86 (6.2 square meters per person, temperature 22℃).

[0062] For the resource recycling efficiency index, a biological simulation algorithm was used to obtain data: drawing on the "bee nest building" pattern, a "modular prefabrication-recycling" sub-algorithm was constructed; the output resource quantification value was 0.91 (building time 3.4 hours, recycling 92%, repeating 5.5 times).

[0063] For cultural compatibility indicators, blockchain technology is used to acquire data: auspicious pattern data is stored through nodes of the Emergency Management Bureau and the Red Cross Society; the output cultural quantification value is 0.77 (71% fidelity and 86% compatibility).

[0064] S3: Multi-objective optimization algorithm collaborative computation: The NSGA-II algorithm was selected, with a population size of 60 and an iteration count of 300. The output optimization parameters were: 5 modules / building and auspicious pattern printing area of ​​2 square meters / building.

[0065] S4: Output architectural design scheme: The modular light steel structure solution is generated: 5 prefabricated modules are assembled (3.4 hours / building); each building is equipped with 3 bathrooms; the exterior walls are painted with auspicious patterns for disaster areas.

[0066] System implementation process: The data acquisition module simplifies the process and ensures rapid response in emergency scenarios; the solution output module generates assembly instructions to support rapid on-site construction.

[0067] Implementation results: In terms of resource recycling efficiency, it takes 3.4 hours to build one building and 92% to recycle, which are 60% and 45% higher than traditional emergency housing, respectively. In terms of human factors adaptability, the residents' satisfaction score is 8.5 points and their psychological anxiety score is reduced by 30%. In terms of emergency performance, 100 buildings can be built within 24 hours, accommodating 1,000 people.

[0068] Comparative Example 1 This comparative model provides a traditional multi-objective architectural design methodology, including: Comparative scheme: Using traditional methods, with the goals of "energy saving (≤60kWh / ㎡・a) and cost reduction (≤3000 yuan / ㎡)," the technology only uses BIM (Revit) to simulate energy consumption and cost, without any cross-disciplinary technology integration. Specifically, the optimization objectives focus solely on energy saving and cost reduction, without considering human factors adaptability, resource recycling efficiency, or cultural compatibility; the technical means only calculate building energy consumption through BIM models, combined with basic genetic algorithms to optimize building material selection; the output solution uses ordinary reinforced concrete building materials, without incorporating any regional cultural elements, and the human factors design only meets basic specifications (such as per capita living area ≥5 square meters), without considering the differentiated needs of multiple groups.

[0069] Comparative implementation results: In terms of human factors adaptability, due to the lack of consideration for the needs of multiple groups, the convenience rate for elderly people to retrieve books is only 50%, and there is insufficient activity space for children, resulting in an overall comfort score of only 6.0 out of 10. In terms of resource recycling efficiency, the proportion of recycled building materials is only 5%, the recycling rate of building materials is 35%, and there is no energy recycling design, which is 30%-65% lower than the indicators of Examples 1-5. In terms of cultural adaptability, no regional cultural elements are incorporated, the cultural restoration degree is 0%, and it cannot meet the needs of cultural inheritance and display. The comprehensive score is only 6.2, which is 25%-30% lower than the scores of 8.3-9.0 in Examples 1-5.

[0070] Optimization target comparison: The optimization objectives of Examples 1-5 cover three categories: human factors adaptability, resource recycling efficiency, and cultural adaptability, encompassing 9-12 specific indicators. These indicators cover multiple fields, including humanities, recycling, culture, and safety, and achieve multi-objective synergistic optimization through a weighted approach. The comparative examples only include two objectives: energy saving and cost, with a total of two specific indicators, covering only the economic and energy-saving fields, and employ a single-objective-priority optimization logic. In comparison, the examples increase the objective dimensions by 50%, the coverage by 60%, and the synergy by 80%.

[0071] Comparison of technical methods: Examples 1-5 employ a technical solution that deeply integrates metaverse pre-simulation, biological simulation, blockchain, and multi-objective optimization algorithms. Data acquisition is achieved through virtual simulation and trusted storage, with an error ≤5%. The algorithm adopts a sub-algorithm fusion mode, requiring only 300-700 iterations. In contrast, the comparative examples use only a single technology combination of BIM and basic genetic algorithms. Data is obtained through BIM simulation and manual verification, resulting in an error of 20%-30%. The algorithm operates in a single mode, requiring at least 1000 iterations. The technical integration of the examples is 100% higher than that of the comparative examples, data accuracy is improved by 75%, and algorithm efficiency is improved by 30%-50%.

[0072] Comparison of implementation results: In terms of human factors adaptability, the comfort scores of Examples 1-5 reached 8.5-9.0 points, an improvement of 25%-30% compared to the comparative example's 6.0 points; in terms of resource recycling efficiency, the proportion of recycled building materials in the examples was 31%-72%, and the recycling rate of building materials was 62%-92%, an improvement of 30%-65% compared to the comparative example's 5% and 35%; in terms of cultural adaptability, the cultural fidelity of the examples was 70%-82%, an improvement of 70%-82% compared to the comparative example's 0%; in terms of overall score, the scores of the examples were 8.3-9.0 points, an improvement of 25%-30% compared to the comparative example's 6.2 points.

[0073] Comparing Examples 1-5 with Comparative Example 1, Examples 1-5 (the present invention solution) are compared with Comparative Example 1 (the conventional solution) in terms of optimization objectives, technical means, and implementation effects, as follows: At the optimization level, Examples 1-5 achieve multi-dimensional coverage and synergy, focusing on human factors adaptability, resource recycling efficiency, and cultural adaptability, encompassing 9-12 specific indicators covering areas such as humanities, recycling, culture, and safety. Multi-objective synergy is achieved through weighted allocation (e.g., 0.4 weight for human factors in family-friendly housing, and 0.4 weight for resources in rural dwellings). Comparative Example 1, however, only focuses on "energy saving and cost," with only two indicators, covering only the economic and energy-saving fields, employing a single-objective priority logic and failing to consider human factors, culture, and other needs. In comparison, Examples 1 increase the number of objective dimensions by 50%, the coverage by 60%, and the synergy by 80%.

[0074] In terms of technical means, Examples 1-5 employ a deep integration of cross-disciplinary technologies, adapting metaverse pre-simulation, biological simulation, and blockchain technology to the three main objectives respectively, combined with multi-objective optimization algorithms (NSGA-II, MOPSO), and embedding a biological simulation sub-algorithm. Data is acquired through virtual simulation and trusted storage, with an error ≤5%, and the algorithm requires only 300-700 iterations. Comparative Example 1 relies solely on BIM technology and basic genetic algorithms; data is acquired through BIM simulation and manual verification, resulting in an error of 20%-30%, and requiring ≥1000 algorithm iterations. The technical integration of the examples is 100% higher than that of the comparative example, data accuracy is improved by 75%, and algorithm efficiency is improved by 30%-50%.

[0075] In terms of implementation effectiveness, regarding human factors adaptability, the implementation examples scored 8.5-9.0 points in comfort, such as a 95% success rate for children to open doors in family-friendly housing and a 9.0 point comfort level in library reading, compared to only 6.0 points in the comparative example and only 50% convenience rate for the elderly to retrieve books. Regarding resource recycling efficiency, the implementation examples showed a 31%-72% proportion of recycled building materials and a 62%-92% recycling rate, such as a 72% proportion of recycled building materials in rural dwellings and a 92% recycling rate in emergency housing, compared to only 5% recycled building materials and a 35% recycling rate in the comparative example. Regarding cultural adaptability, the implementation examples showed a 70%-82% cultural restoration rate, such as a 76% restoration rate of academy culture and an 81.5% restoration rate of local elements, compared to 0% in the comparative example. The overall score for the implementation examples was 8.3-9.0 points, compared to only 6.2 points in the comparative example, representing an improvement of 25%-30%.

[0076] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0078] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A building design method based on multi-objective optimization, characterized in that, Includes the following steps: S1. Determine the multi-objective optimization indicators for architectural design, including human factors adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators; S2. For human-factor adaptability indicators, metaverse pre-simulation technology is used to obtain quantitative data; for resource recycling efficiency indicators, biological simulation algorithm is used to obtain quantitative data; and for cultural adaptability indicators, blockchain technology is used to obtain quantitative data. S3. Input the quantified data into a multi-objective optimization algorithm to perform collaborative optimization calculations and obtain the building design optimization parameters; S4. Output the architectural design scheme based on the architectural design optimization parameters.

2. The architectural design method based on multi-objective optimization as described in claim 1, characterized in that, In step S1, the human factors adaptability index includes quantitative values ​​of physiological needs and psychological needs for different groups of people; the resource recycling efficiency index includes the recycling rate of building materials, the proportion of recycled building materials, and the material recycling rate after demolition; the cultural adaptability index includes the degree of restoration of regional cultural elements and the quantitative value of modern functional compatibility.

3. The architectural design method based on multi-objective optimization as described in claim 1, characterized in that, In step S2, quantitative data of human-factor adaptability indicators are obtained through metaverse pre-simulation technology. Specifically, this includes: constructing a virtual metaverse model of a building, simulating the behavior of different groups such as the elderly, children, and people with disabilities in the virtual metaverse model, and collecting and converting the quantitative values ​​of physiological needs and psychological needs.

4. The architectural design method based on multi-objective optimization as described in claim 1, characterized in that, In step S2, quantitative data of resource recycling efficiency indicators are obtained through a biological simulation algorithm. Specifically, this includes: constructing a biological simulation optimization sub-algorithm by drawing on the material cycling laws of biological ecosystems, and converting the resource recycling efficiency indicators into quantitative parameters similar to biological cycles.

5. The architectural design method based on multi-objective optimization as described in claim 1, characterized in that, In step S2, quantitative data of cultural adaptability indicators are obtained through blockchain technology, specifically including: using blockchain to store regional cultural element data, extracting data from the blockchain and converting it to obtain quantitative values ​​of the degree of restoration of regional cultural elements and compatibility with modern functions.

6. The architectural design method based on multi-objective optimization as described in claim 4, characterized in that, In step S3, the multi-objective optimization algorithm is integrated with the biological simulation optimization sub-algorithm, using the quantitative data of resource recycling efficiency index as a constraint, and co-optimizing the calculation with the quantitative data of human factor adaptability index and cultural adaptability index.

7. A building design system based on multi-objective optimization according to any one of claims 1-6, characterized in that, include: The indicator determination module is used to determine multi-objective optimization indicators for architectural design, including human factors adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators. The data acquisition module is used to acquire corresponding quantitative data for human-factor adaptability indicators, resource recycling efficiency indicators, and cultural adaptability indicators, respectively, using metaverse pre-simulation technology, biological simulation algorithms, and blockchain technology. The algorithm optimization module is used to receive the quantized data transmitted by the data acquisition module, input the quantized data into the multi-objective optimization algorithm for collaborative optimization calculation, and obtain the building design optimization parameters. The scheme output module is used to receive architectural design optimization parameters transmitted by the algorithm optimization module and output architectural design schemes based on the architectural design optimization parameters.

8. The architectural design system based on multi-objective optimization as described in claim 7, characterized in that, The data acquisition module includes a metaverse data acquisition unit, which is used to construct a virtual metaverse model of the building, simulate the usage behavior of different groups such as the elderly, children and people with disabilities, and collect and transform quantitative data of human-factor adaptability indicators.

9. A building design system based on multi-objective optimization as described in claim 7, characterized in that, The data acquisition module includes a biological simulation data processing unit, which is used to convert resource cycle efficiency indicators into biological cycle-like quantitative parameters through a biological simulation optimization sub-algorithm.

10. A building design system based on multi-objective optimization as described in claim 7, characterized in that, The data acquisition module includes a blockchain data storage unit, which is used to store regional cultural element data and extract data to obtain quantitative data of cultural adaptability indicators.

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