A Multi-Objective Optimization-Based Approach to Architectural Engineering Design and Layout Planning

Abstract

This invention discloses a multi-objective optimization-based architectural engineering layout planning method, belonging to the field of intelligent building design technology. The method includes: acquiring various types of data for the architectural engineering to be planned, and constructing an initial layout structure model containing candidate building units. Spatial topology analysis is performed on the model to extract multi-dimensional layout relationship features and construct a layout topology relationship network. Initial functional and environmental constraint states are constructed, and constraint coupling propagation is completed by inputting a multi-objective integrated optimization channel to obtain a layout co-evolution state matrix. After three-dimensional objective evaluation analysis, the layout topology relationship network is iteratively updated, and a layout planning scheme is output based on the convergence result. This invention solves the technical problems of existing architectural layout planning methods being unable to quantify the intrinsic relationship between space, function, and environment, and struggling to balance multi-dimensional optimization objectives. It achieves multi-dimensional collaborative optimization of architectural layout, improves planning efficiency and accuracy, and outputs stable and feasible layout schemes.

Description

Technical Field

[0001] This invention relates to the field of intelligent building design technology, and in particular to a method for architectural engineering design layout planning based on multi-objective optimization. Background Technology

[0002] Architectural design layout planning is widely used in various engineering construction projects. The rationality of its layout directly affects the spatial efficiency, functional adaptability, and environmental friendliness of the project. The scientific construction of the mechanism model is crucial for achieving high-quality planning. Existing layout planning often relies on the manual experience of designers and is only checked in a scattered manner through single indicators. This approach makes it difficult to achieve simultaneous optimization of multiple objectives such as space utilization, functional satisfaction, and environmental adaptability. It lacks constraint coupling propagation and iterative convergence mechanisms, which can easily lead to problems such as objective conflicts and poor layout coordination. The planning data is incomplete and lacks accuracy, failing to meet the control requirements of precise and intelligent layout planning in modern architectural engineering. Summary of the Invention

[0003] This application addresses the technical problem that existing building layout planning methods cannot quantify the intrinsic relationship between space, function, and environment, and are difficult to balance multi-dimensional optimization goals.

[0004] To address the aforementioned technical problems, this application proposes a multi-objective optimization-based architectural engineering design layout planning method. The method includes: acquiring site boundary data, functional requirement data, building unit type data, and environmental constraint data of the architectural engineering project to be planned; constructing an initial layout structure model containing multiple candidate building units and their spatial relationships based on the site boundary data and building unit type data; performing spatial topology analysis on each candidate building unit in the initial layout structure model to extract multi-dimensional layout relationship features characterizing spatial adjacency, functional dependency, and traffic connectivity between building units, and constructing a layout topology network based on these multi-dimensional layout relationship features; constructing initial functional constraint states and environmental constraint states using the functional requirement data and environmental constraint data respectively; inputting the multi-dimensional layout relationship features, initial functional constraint states, and environmental constraint states into a multi-objective integrated optimization channel; performing constraint coupling propagation along the layout topology network in the multi-objective integrated optimization channel to form a layout co-evolution state matrix characterizing the overall layout coordination state; performing evaluation analysis using a three-dimensional objective evaluation sub-channel based on the layout co-evolution state matrix; performing iterative updates of the layout topology network using the evaluation analysis results; and outputting a layout planning scheme based on the convergence results.

[0005] This application proposes one or more technical solutions, which have at least the following technical effects: This application constructs an initial layout structure for a building project, analyzes spatial topological relationships, and extracts multi-dimensional layout features. Through constraint coupling propagation and multi-objective evaluation analysis, it obtains the layout co-evolution state, calculates the co-evolutionary parameters of space utilization, functional adaptation, and environmental compatibility, and iteratively adjusts the layout topological relationship based on the three-dimensional objective evaluation results. This results in the accurate generation of the optimal multi-objective co-evolutionary layout planning scheme for the building project, making the design and layout planning of the building project more scientific, efficient, rational, and adaptable. It achieves the technical effect of multi-dimensional co-evolutionary optimization of building layout, improving planning efficiency and accuracy, and outputting a stable and feasible layout scheme. Attached Figure Description

[0006] To more clearly illustrate the technical solutions in 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.

[0007] Figure 1 This is a flowchart illustrating the architectural engineering design layout planning method based on multi-objective optimization provided in the embodiments of this application.

[0008] Figure 2 This is a schematic diagram of the process of performing three-dimensional target evaluation sub-channel evaluation analysis in the architectural engineering design layout planning method based on multi-objective optimization provided in the embodiments of this application. Detailed Implementation

[0009] This application provides a building engineering design layout planning method based on multi-objective optimization, which solves the technical problem that existing building layout planning methods cannot quantify the intrinsic relationship between space, function and environment, and are difficult to balance multi-dimensional optimization objectives.

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0011] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0012] like Figure 1As shown, a multi-objective optimization-based architectural engineering design layout planning method is described, wherein the method includes: Obtain site boundary data, functional requirement data, building unit type data, and environmental constraint data for the planned building project. Construct an initial layout structure model containing multiple candidate building units and their spatial relationships based on the site boundary data and building unit type data.

[0013] Specifically, the first step is to determine the target site area for the planned building project and collect corresponding site boundary data. This data includes the coordinates of the planned land boundary line, the measured geographical boundaries of the site, and the topographic elevation data within the site. After collection, the site boundary data undergoes vector digitization processing, uniformly converting it to the 2000 geodetic coordinate system. The coordinate data accuracy is set to centimeter level, and invalid and redundant boundary data is removed to form a standardized site boundary dataset. Next, functional requirement data for the planned building project is extracted from the project design brief and the owner's requirements document. This data includes the overall functional positioning of the building project, area index requirements for each functional zone, functional unit association requirements, and traffic flow organization requirements. The extracted functional requirement data undergoes parametric transformation, converting area indices into unit area threshold parameters and functional association requirements into unit adjacency priority parameters, resulting in a standardized functional requirement dataset.

[0014] Then, based on the functional requirements data, matching building unit type data is determined. This data includes the functional attributes, standard external dimensions, spatial boundary constraints, and functional carrying capacity parameters of each type of building unit. This data is integrated to form a callable building unit type dataset that corresponds one-to-one with the functional requirements dataset. Environmental constraint data is collected from site environmental assessment documents, regional planning control conditions, and regional meteorological observation data. This data includes site sunshine parameters, prevailing wind direction distribution data, the location of surrounding environmentally sensitive areas, and the coordinates of municipal infrastructure interfaces. Spatial coordinate matching processing is performed on the environmental constraint data, matching all constraint items to the unified coordinate system of the site boundary data. The control thresholds for each constraint item are then labeled, forming an environmental constraint dataset corresponding to the site boundary data.

[0015] Next, based on the standardized site boundary data, a Cartesian coordinate system corresponding to the target site is constructed. The origin of the coordinate system coincides with the southwest corner of the land boundary line in the site boundary data. According to the planning control conditions and site boundary data, the buildable and non-buildable areas within the site are delineated, and the boundary coordinates and avoidance control requirements of the buildable areas are clarified, forming the spatial base framework of the initial layout structure model. Then, the building unit type dataset is retrieved. Within the buildable area of ​​the Cartesian coordinate system, based on the unit area threshold parameters and adjacency priority parameters of the functional requirements dataset, multiple candidate building units matching the functional requirements are generated. Each candidate building unit corresponds to a unique functional attribute, spatial boundary parameters, and initial occupancy coordinates. The initial occupancy coordinates are set according to the rules of prioritizing avoidance of non-buildable areas and meeting the minimum building spacing specifications.

[0016] Finally, based on this planar Cartesian coordinate system, the relative coordinate differences and spatial boundary intervals between each candidate building unit are calculated to clarify the adjacency relationship type of adjacent units. A spatial coordinate mapping relationship is established between each candidate building unit and the site boundary and buildable area, forming spatial relationship parameters corresponding to each candidate building unit. Integrating the spatial base frame, the functional attributes and initial occupancy coordinate data of each candidate building unit, and the spatial relationship parameters between units, an initial layout structure model containing multiple candidate building units and their spatial relationships is formed. The model output is a vector dataset with unified spatial coordinates, which can be directly connected to the data input requirements of the subsequent spatial topology analysis stage.

[0017] Spatial topology analysis is performed on each candidate building unit in the initial layout structure model to extract multi-dimensional layout relationship features that characterize the spatial adjacency, functional dependency, and traffic connectivity between building units, and a layout topology network is constructed based on the multi-dimensional layout relationship features.

[0018] Optionally, firstly, all data within the initial layout structure model is retrieved, and a unique topological node identifier is assigned to each candidate building unit. Simultaneously, the spatial boundary coordinates, functional attribute parameters, and preset entrance / exit location data corresponding to each candidate building unit are extracted to form the basic dataset for spatial topology analysis. Next, based on a planar Cartesian coordinate system, buffer analysis is performed on the spatial boundaries of each candidate building unit. The buffer range matches the minimum adjacency determination threshold of the building engineering design code. Through spatial overlay analysis, the adjacency status between each candidate building unit is determined, distinguishing between three types: direct adjacency, indirect adjacency, and no adjacency. Simultaneously, the boundary overlap length and minimum interval distance between adjacent units are calculated to extract and form the spatial adjacency relationship features corresponding to each candidate building unit.

[0019] Then, a standardized functional requirements dataset is retrieved. Based on the functional attribute parameters of each candidate building unit, a preset functional dependency mapping rule is matched. This mapping rule is based on the current building functional design specifications and the project functional requirements dataset. It clarifies the strong dependency, weak dependency, and no dependency relationship types between building units with different functional attributes. Strong dependency corresponds to unit combinations with high functional linkage requirements and need to be arranged adjacently. Weak dependency corresponds to unit combinations with indirect functional associations and can be arranged adjacently. No dependency corresponds to unit combinations with no functional linkage requirements. Combined with the adjacency priority parameter in the functional requirements data, the matching degree between the current spatial location of each candidate building unit and the functional dependency requirements is verified, and the functional dependency relationship features corresponding to each candidate building unit are extracted.

[0020] Next, based on the preset entrance and exit location data of each candidate building unit and the passable area range of the site boundary data, the path topology analysis method is used to calculate the shortest passage path length and passage path connectivity between any two candidate building unit entrances and exits, distinguishing between three types: direct connection, indirect connection, and no connection. Simultaneously, it is verified whether the passage path meets the building fire protection code and circulation organization requirements, and the traffic connectivity relationship features corresponding to each candidate building unit are extracted.

[0021] Finally, the extracted spatial adjacency features, functional dependency features, and traffic connectivity features are integrated to form a multidimensional layout relationship feature set corresponding to each candidate building unit. Each candidate building unit is an independent node in the topology network, the multidimensional layout relationship features corresponding to the node are the node attribute data, and the spatial adjacency, functional dependency, and traffic connectivity relationships between nodes are the edges of the topology network. The corresponding relationship type, quantification parameters, and weight coefficients are matched for each edge simultaneously. The weight coefficients are preset based on the optimization priority of the three-dimensional target evaluation. The weight coefficients corresponding to spatial adjacency, functional dependency, and traffic connectivity relationships range from 0 to 1, and the sum of the weights of the edges associated with the same node is 1. The default baseline values ​​are 0.3, 0.4, and 0.3, respectively. Those skilled in the art can adapt and adjust them according to the project optimization requirements to complete the construction of the layout topology relationship network. The completed layout topology relationship network can fully represent the attribute information of each candidate building unit and the relationship between units, and can directly connect to the data input requirements of the subsequent constraint coupling propagation link.

[0022] After constructing initial functional constraint states and environmental constraint states using functional requirement data and environmental constraint data respectively, the multi-dimensional layout relationship features, initial functional constraint states, and environmental constraint states are input into a multi-objective integrated optimization channel. Constraint coupling propagation is performed along the layout topology relationship network in the multi-objective integrated optimization channel to form a layout cooperative evolution state matrix that characterizes the overall layout cooperative state.

[0023] In one embodiment of this application, a standardized functional requirement dataset is first retrieved. Combined with the functional attributes of building units corresponding to each node in the layout topology network, corresponding functional constraint parameters are matched for each topology node. The functional constraint parameters include unit area threshold, upper and lower limits of functional carrying capacity, adjacency priority requirements, and traffic flow adaptation requirements. At the same time, according to building design codes and project management requirements, the functional constraint parameters are divided into mandatory constraints and optimization constraints. The compliance threshold and allowable deviation range of each constraint item are clarified. The functional constraint parameters of all topology nodes are integrated to form an initial functional constraint state that corresponds one-to-one with the nodes of the layout topology network.

[0024] Subsequently, a standardized environmental constraint dataset was retrieved, and combined with the unified coordinate system of the site boundary data, each environmental constraint item was mapped to the corresponding spatial range of the layout topology network. Corresponding environmental constraint parameters were matched for each topology node. The environmental constraint parameters include sunshine duration threshold, ventilation corridor avoidance requirements, minimum distance limit of environmentally sensitive areas, and interface adaptation distance of municipal supporting facilities. Similarly, according to planning control requirements and environmental assessment standards, the environmental constraint parameters were divided into mandatory constraints and optimization constraints. The control threshold and compliance judgment standards of each constraint item were clarified. The environmental constraint parameters of all topology nodes were integrated to form an initial environmental constraint state that corresponds one-to-one with the nodes of the layout topology network.

[0025] Then, the multidimensional layout relationship features corresponding to each node in the layout topology relationship network are extracted. The multidimensional layout relationship features, initial functional constraint states, and initial environmental constraint states are standardized in data format and unified into a structured parameter set that corresponds one-to-one with the nodes of the layout topology relationship network. The processed structured parameter set is input into the multi-objective integrated optimization channel to complete the pre-data preparation for optimization calculation.

[0026] Next, the multi-objective integrated optimization channel uses the layout topology network as the propagation carrier and each topology node as the basic computational unit for constraint coupling. First, it loads the corresponding multi-dimensional layout relationship features, functional constraint parameters, and environmental constraint parameters for each node, completing the constraint parameter initialization for a single node. Starting from the initialized node, it performs a full-network traversal constraint coupling propagation along the edges of the layout topology network. During propagation, the constraint parameters and constraint satisfaction status of the current node are passed to adjacent nodes through associated edges. Simultaneously, it receives the constraint parameters and associated relationship features passed from adjacent nodes and performs cross-node constraint coupling calculations.

[0027] During the coupling calculation process, compliance verification is first performed on mandatory constraints to identify constraints and corresponding nodes that do not meet mandatory control requirements. The constraint conflict type and deviation value are then marked. In subsequent coupling propagation and iterative calculations, the parameters of the marked nodes are prioritized for adjustment and optimization. Next, multi-dimensional collaborative adaptation calculations are performed on the optimization constraints. Based on the preset weight coefficients of the constraint items, the constraint satisfaction degree of a single node is calculated. The constraint satisfaction degree is the compliance matching ratio between the actual parameters and the constraint threshold, with a value range of 0-1. Simultaneously, the correlation characteristics transmitted by adjacent nodes are combined to calculate the inter-unit correlation adaptation degree. This adaptation degree is the degree of matching between the functional, spatial, and traffic correlation requirements of the nodes, with a value range of 0-1. The correlation adaptation parameters and constraint satisfaction degree parameters between nodes are then updated. After completing one full network traversal, the coupling calculation results of all nodes are updated. The traversal propagation and coupling calculation are repeated until the change in the constraint satisfaction degree parameters of all network nodes is lower than the preset convergence threshold. The default threshold is 0.5%, which can be adjusted within the range of 0.1%-1% according to project accuracy requirements. Finally, the entire constraint coupling propagation process is completed.

[0028] Finally, the final coupling calculation results of all topological nodes in the entire network are extracted. The independent topological nodes of the layout topological relationship network are used as the row dimension of the matrix, and the core collaborative state parameters of the nodes are used as the column dimension of the matrix. The core collaborative state parameters include the node spatial coordinate parameters, functional constraint satisfaction, environmental constraint satisfaction, and inter-unit association and adaptation. The core collaborative state parameters corresponding to each node are sequentially filled into the corresponding positions of the matrix to form a layout collaborative evolution state matrix that represents the overall layout collaborative state. The parameters in the matrix can be directly retrieved for subsequent multi-objective evaluation and iterative optimization.

[0029] Based on the layout co-evolution state matrix, the evaluation analysis of the three-dimensional target evaluation sub-channel is performed. The evaluation analysis results are used to perform iterative updates of the layout topology relationship network. Based on the convergence results, the layout planning scheme is output.

[0030] Specifically, firstly, based on the layout co-evolution state matrix, it is mapped to the three evaluation layers of the three-dimensional target evaluation sub-channel: space utilization, functional satisfaction, and environmental adaptation. The corresponding evaluation values ​​are calculated and synchronized to the multi-target conflict analysis layer to establish the evaluation analysis results. This step will be explained in detail in the following content.

[0031] Then, the complete evaluation and analysis results generated in the 3D target evaluation stage are retrieved, and the layout topology relationship network, layout co-evolution state matrix, initial functional constraint data, and environmental constraint data corresponding to the current iteration are retrieved simultaneously. The layout topology relationship network is then iteratively updated. During the iterative update process, based on the constraint conflict analysis results, priority directional adjustments are performed on the topology nodes corresponding to the marked non-compliant constraints. For nodes with non-compliant functional constraints, the spatial location, boundary, and adjacency relationships of the units are adjusted. For nodes with non-compliant environmental constraints, the spatial arrangement of the units is adjusted, and environmentally sensitive areas are avoided, thus eliminating mandatory constraint non-compliance issues.

[0032] Next, based on the quantitative evaluation value of individual units in each dimension and the comprehensive target evaluation value, with maximizing the comprehensive target evaluation value of the layout scheme as the core optimization objective and mandatory constraint requirements as boundary constraints, a multi-objective optimization objective function is constructed. The multi-objective gradient descent optimization method commonly used in this field is adopted to perform iterative optimization and adjustment on the spatial location coordinates, unit association relationships, and occupation boundary parameters of each topological node in the layout topological relationship network. The corresponding collaborative state parameters of each node are updated synchronously, and the updated layout collaborative evolution state matrix is ​​generated, completing a single round of iterative update of the layout topological relationship network.

[0033] Finally, the convergence result is determined by comparing the degree of change of the layout co-evolution state matrix in continuous iteration. When the degree of change is less than the preset convergence threshold, the layout topology relationship network is determined to have reached a stable state. Based on the spatial positional relationship of candidate building units in the stable layout topology relationship network, the building engineering layout structure data is generated, and the corresponding building engineering design layout planning scheme is output. This step will also be explained in detail in the following content.

[0034] Furthermore, the method provided in this application embodiment includes: The three-dimensional target evaluation sub-channel is a processing sub-channel of the multi-target integrated optimization channel, and the evaluation items of the three-dimensional target evaluation sub-channel include space utilization items, functional satisfaction items, and environmental adaptation items.

[0035] Specifically, the multi-objective integrated optimization channel includes a three-dimensional objective evaluation sub-channel. This sub-channel uses the layout co-evolution state matrix generated by constraint coupling propagation as its core input. Its core function is to perform multi-dimensional quantitative evaluation of the target achievement of the building layout scheme, providing a basis for subsequent iterative updates. The three-dimensional objective evaluation sub-channel sets three core evaluation dimensions: space utilization evaluation, functional satisfaction evaluation, and environmental adaptability evaluation. It outputs the quantitative evaluation values ​​for each dimension and the comprehensive evaluation value of the layout scheme. For detailed explanations of the specific execution process and calculation methods for each dimension, please refer to the subsequent detailed steps.

[0036] Furthermore, such as Figure 2As shown, the method provided in this application embodiment includes: The layout co-evolution state matrix is ​​mapped to the space utilization evaluation layer, functional satisfaction evaluation layer, and environmental adaptation evaluation layer in the three-dimensional target evaluation sub-channel. The space utilization evaluation layer calculates the space utilization evaluation value based on the spatial adjacency relationship and land distribution between candidate building units. The functional satisfaction evaluation layer calculates the functional satisfaction evaluation value based on the functional requirement data and the functional dependency relationship between candidate building units. The environmental adaptation evaluation layer calculates the environmental adaptation evaluation value based on the environmental constraint data and the spatial location of candidate building units. The space utilization evaluation value, functional satisfaction evaluation value, and environmental adaptation evaluation value are synchronized to the multi-objective conflict analysis layer to establish the evaluation analysis results.

[0037] Optionally, the three-dimensional target evaluation sub-channel performs evaluation calculations according to the following process: First, the layout co-evolution state matrix generated by constraint coupling propagation is retrieved, and the core co-evolution state parameters, site boundary data parameters associated with the nodes, and basic attribute parameters of building units corresponding to each topological node in the matrix are extracted. Based on the preset mapping rules of evaluation dimensions and parameters, parameters related to site space occupancy, building unit arrangement, and buildable area utilization are mapped and matched to the space utilization evaluation layer; parameters related to functional constraint satisfaction, inter-unit functional association, and traffic flow adaptation are mapped and matched to the functional satisfaction evaluation layer; and parameters related to environmental constraint satisfaction and site environmental condition adaptation are mapped and matched to the environmental adaptation evaluation layer. During the mapping process, all assigned parameters are simultaneously processed for format standardization and dimensional unification to complete the dimensional matching and pre-allocation of evaluation data, providing standardized input data for each dimension's specialized evaluation.

[0038] Subsequently, through the space utilization evaluation layer, the functional satisfaction evaluation layer, and the environmental adaptation evaluation layer, quantitative evaluation calculations for the corresponding dimensions are performed respectively, and the corresponding outputs are space utilization evaluation values, functional satisfaction evaluation values, and environmental adaptation evaluation values. For details on the specific calculation methods of each dimension, please refer to the detailed explanation of the corresponding steps in the following sections.

[0039] Then, the single-dimensional quantitative evaluation values ​​of each candidate building unit and the overall site-wide evaluation values ​​output by the space utilization evaluation layer, functional satisfaction evaluation layer, and environmental adaptation evaluation layer are retrieved. Simultaneously, the mandatory constraints in the initial functional and environmental constraint states, as well as the topological node identifiers and associated attribute data corresponding to each candidate building unit in the layout co-evolution state matrix, are retrieved. When performing multi-dimensional constraint conflict analysis, the compliance of mandatory constraints is first determined. Red-line control clauses in the mandatory constraint requirements are extracted, including minimum area thresholds, strong dependency adjacency requirements, and prohibited adjacency restrictions in functional constraints, and minimum avoidance distances, minimum sunshine duration requirements, and ecological control red-line requirements in environmental constraints. Using topological nodes as units, the core parameters corresponding to the single-dimensional evaluation values ​​are compared one by one with the mandatory constraint requirements to determine the compliance of single-node constraints and identify non-compliant constraints that do not meet the mandatory constraint requirements.

[0040] Next, multi-objective optimization conflict identification was carried out. For individual candidate building units, the matching of their evaluation values ​​across various dimensions was compared to identify optimization conflicts where one dimension met the standard but other dimensions showed a trade-off, as well as overall layout multi-objective conflicts caused by the relationships between units. For all identified non-compliant constraints and multi-objective optimization conflicts, a unique identifier was marked on the corresponding topology node. The conflict type, core content, control requirements, and severity level were simultaneously clarified. Conflict types were divided into three categories: functional constraint non-compliance, environmental constraint non-compliance, and multi-objective optimization conflict. Severity levels were divided into two levels: mandatory non-compliance and optimization-level conflict.

[0041] Then, the overall space utilization evaluation value, overall functional satisfaction evaluation value, and overall environmental adaptability evaluation value of the site are retrieved. Combined with the constraint conflict analysis results, the comprehensive target evaluation value is calculated. First, according to the preset multi-objective weight ratio, the overall evaluation values ​​of the three dimensions are weighted and fused. The default baseline weight ratio is 0.3 for space utilization evaluation value, 0.4 for functional satisfaction evaluation value, and 0.3 for environmental adaptability evaluation value. The weight values ​​can be adjusted within the range of 0-1 according to the core objectives of the overall project plan. The sum of the weights of the three parameters is 1, generating the basic comprehensive evaluation value of the layout scheme. Then, based on the results of the constraint conflict analysis, a correction calculation is performed. For the existing mandatory non-compliance items, a correction coefficient is set in the range of 0.5-1.0 according to the number and severity of the conflict items, and the basic comprehensive evaluation value is reduced accordingly. For the optimization-level conflict items, a correction coefficient is set in the range of 0.9-1.0 according to the scope of the conflict impact, and a fine-tuning correction is performed. Finally, a comprehensive target evaluation value of the layout scheme is generated. The comprehensive target evaluation value ranges from 0 to 1. The higher the value, the better the multi-objective optimization effect and constraint compliance of the layout scheme. This comprehensive target evaluation value will serve as the core judgment basis for subsequent layout scheme iteration and updates.

[0042] Finally, the full quantitative results output from the space utilization evaluation layer, functional satisfaction evaluation layer, and environmental adaptation evaluation layer are used as the basic dimension evaluation data. The constraint non-compliance items and multi-objective optimization conflict items identified based on mandatory constraints, along with their corresponding conflict node markers, conflict types, severity levels, and compliance judgment criteria, are used as the constraint conflict analysis results. These are combined with the comprehensive objective evaluation value obtained through weighted calculation and correction. Integrating these three levels of data into a complete dataset constitutes the final evaluation and analysis result.

[0043] Furthermore, the method provided in this application embodiment includes: Extract the land boundary data of each candidate building unit and the spatial distance data between it and adjacent candidate building units. Calculate the effective usable space range of the corresponding candidate building unit based on the extraction results. Generate a spatial density parameter based on the land area ratio of the candidate building unit within the land boundary and the spatial distance data between adjacent candidate building units. Combine the effective usable space range and spatial density parameter to calculate the space utilization efficiency coefficient of each candidate building unit, and output the space utilization efficiency coefficient as a space utilization evaluation value.

[0044] Specifically, the space utilization evaluation layer retrieves standardized input data that has been mapped and matched, extracting the coordinates of the closed boundary of the candidate building unit corresponding to each topological node, the spatial adjacency characteristics of adjacent building units, and the boundary data of the buildable area of ​​the site. Based on the planar spatial coordinate system, the minimum spatial distance between the target candidate building unit and each adjacent candidate building unit is calculated. Combined with the building spacing control requirements stipulated in the current building engineering design code, the exclusive space control range corresponding to each candidate building unit is delineated. The exclusive space control range is overlaid with the boundary of the buildable area of ​​the site, and the non-buildable areas and fixed space ranges already occupied by other candidate building units are eliminated, finally determining the effective usable space range corresponding to each candidate building unit.

[0045] Next, based on the extracted closed boundary of the candidate building units, the ratio of the actual footprint of each candidate building unit to the planned usable area within the corresponding boundary is calculated to generate the footprint ratio parameter for a single unit. For the target candidate building unit, adjacent candidate building units that have a spatial adjacency relationship with the target unit and whose minimum spatial distance between units does not exceed the maximum proximity judgment threshold preset in the site plan are first screened, and the number of effective adjacent units is counted. Using the minimum building spacing of similar buildings specified in the current building engineering design code as the benchmark value, for each effective adjacent unit, the ratio of the benchmark value to the actual minimum spatial distance between the units in that group is calculated to obtain the distance influence coefficient of a single group of adjacent units. At the same time, the upper limit of the distance influence coefficient is limited to 1.2 to avoid interference from extreme values. The arithmetic mean of the distance influence coefficients of all effective adjacent units of the target unit is calculated, and normalization is performed in combination with the number of effective adjacent units. The calculation result is mapped to the 0-1 interval to generate the spatial agglomeration parameter of the target candidate building unit. The higher the value, the higher the spatial agglomeration around the building unit. According to the preset weight ratio, the land area ratio parameter of a single unit and the spatial agglomeration parameter are weighted and fused together. The default benchmark weight ratio is 0.6 for the land area ratio parameter of a single unit and 0.4 for the spatial agglomeration parameter. The weight values ​​can be adjusted within the range of 0-1 according to the density control requirements of the site planning. The sum of the weights of the two parameters is 1, and finally the spatial density parameters corresponding to each candidate building unit are generated.

[0046] Then, based on the effective usable space range of each candidate building unit, the effective usable area value within that range is calculated. The effective usable area value is the net planar projection area formed by the closed boundary of the effective usable space range. Combined with the spatial density parameters of the corresponding candidate building unit, a preset space utilization efficiency benchmark threshold is matched. The benchmark threshold is set based on current urban construction land planning control standards and project plot ratio and building density control requirements, with a benchmark threshold range of 0.3-0.7, which can be adjusted according to the specific planning conditions of the project. A basic space utilization coefficient is calculated based on the ratio of the actual land area of ​​the candidate building unit to the corresponding effective usable area value. The upper limit of the basic space utilization coefficient is fixed at 1; if the ratio exceeds 1, it is directly taken as 1. A weighted fusion calculation is performed on the basic space utilization coefficient and the spatial density parameters of the corresponding candidate building unit. The default weight ratio is 0.7 for the basic space utilization coefficient and 0.3 for the spatial density parameter. The weight values ​​can be adjusted within the range of 0-1 according to the project's space control requirements. The sum of the weights of the two parameters is 1, and the initial value of space utilization efficiency is calculated. The initial value of space utilization efficiency is compared and corrected with the benchmark threshold of space utilization efficiency to ensure that the space utilization efficiency coefficient of each candidate building unit in the final output is stable within the range of 0-1. The higher the value, the better the rationality and efficiency of space utilization of the building unit.

[0047] Finally, the space utilization efficiency coefficients of all candidate building units are integrated. Using the floor area of ​​each candidate building unit as the weight, a weighted average calculation is performed on the space utilization efficiency coefficients of all individual units. The calculation is then corrected by combining the overall space coverage of the buildable area and the proportion of ineffective space parameters to generate an overall space utilization evaluation value for the site. The space utilization efficiency coefficients of individual units and the overall space utilization evaluation value are output simultaneously for subsequent constraint conflict analysis and layout scheme iteration optimization.

[0048] Furthermore, the method provided in this application embodiment includes: The functional requirement data is parsed to generate functional capacity requirement parameters and functional adjacency requirement parameters for each candidate building unit. The functional adjacency status between candidate building units is extracted based on the layout topology network, and a functional dependency mapping table is constructed between candidate building units in conjunction with the functional adjacency requirement parameters. Based on the functional dependency mapping table, the degree of functional dependency satisfaction between each candidate building unit is determined, and functional dependency matching parameters characterizing the functional synergy between candidate building units are generated. A functional capacity satisfaction parameter is calculated by combining the functional capacity requirement parameters with the actual carrying capacity of the candidate building units, and the functional capacity satisfaction parameter is coupled with the functional dependency matching parameter to obtain a functional satisfaction evaluation value.

[0049] Specifically, the functional requirement evaluation layer retrieves standardized input data that has been mapped and matched, and simultaneously retrieves standardized functional requirement datasets, functional attribute parameters corresponding to each candidate building unit, and node attribute data of the layout topology network. Layered parsing is performed on the functional requirement data to extract area index requirements, upper and lower limits of functional carrying capacity, and unit functional adaptation requirements for each functional zone. These extracted index requirements are converted into functional capacity requirement parameters corresponding to each candidate building unit. The functional capacity requirement parameters include the minimum area threshold, maximum area limit, and standard functional carrying capacity range for a single unit. Simultaneously, the linkage operation requirements, adjacency priority requirements, and circulation organization association requirements between functional units in the functional requirement data are parsed, and functional adjacency requirement parameters corresponding to each candidate building unit are generated. These functional adjacency requirement parameters include strong dependency adjacency requirements, weak dependency proximity requirements, prohibited adjacency restrictions, and priority weight coefficients corresponding to various requirements.

[0050] Next, from the layout topology network, node association data, functional dependency characteristics between units, and spatial adjacency status data corresponding to each candidate building unit are extracted to clarify the actual adjacency type between any two candidate building units. The actual adjacency type includes three categories: direct adjacency, indirect adjacency, and no adjacency. Using the unique topology node identifier of each candidate building unit as an index, the functional adjacency requirement parameters corresponding to each unit are matched one-to-one with the actual adjacency status and functional association data between each pair of units to clarify the requirement type, requirement priority, actual status, and compliance judgment criteria for each group of units. The matching data of all unit combinations are integrated to construct a functional dependency mapping table covering all candidate building units.

[0051] Then, the generated functional dependency mapping table is retrieved. Based on the preset functional dependency satisfaction judgment rules, compliance judgment and matching degree calculation are performed on each group of candidate building unit combinations in the mapping table. For strong dependency adjacency requirements, it is determined whether the corresponding unit meets the direct adjacency state. If the requirement is met, the matching degree is assigned a value of 1; otherwise, it is assigned a value of 0. For weak dependency proximity requirements, it is determined whether the corresponding unit meets the preset proximity distance threshold requirement. If the requirement is met, the matching degree is assigned a value of 1; otherwise, it is assigned a value in a linear decreasing rule within the range of 0 to 1 according to the deviation ratio between the actual distance and the threshold. For prohibited adjacency restrictions, it is determined whether the corresponding unit has an illegal adjacency state. If an illegal adjacency exists, the matching degree of the group of unit combinations is directly assigned a value of 0, and functional constraint conflict items are simultaneously included. For each candidate building unit, the matching degree calculation results of all its associated unit combinations are statistically analyzed. Combined with the priority weight coefficients corresponding to each requirement item, a weighted fusion calculation is performed to generate functional dependency matching parameters for each candidate building unit. The functional dependency matching parameters range from 0 to 1, with higher values ​​indicating a better degree of satisfaction of functional dependency requirements between units.

[0052] Next, the functional capacity requirement parameters corresponding to each candidate building unit are retrieved, and the actual land area and actual functional carrying capacity parameters of each candidate building unit are extracted simultaneously. The compliance matching ratio between the actual carrying capacity parameters and functional capacity requirement parameters of each candidate building unit is calculated, generating the functional capacity satisfaction parameter for each candidate building unit. The functional capacity satisfaction parameter ranges from 0 to 1, with a higher value indicating a better degree of satisfaction of the unit's functional capacity requirements. According to the preset weight ratio, the functional capacity satisfaction parameter and functional dependency matching parameter corresponding to a single unit are weighted and coupled for calculation. The default baseline weight ratio is 0.55 for the functional capacity satisfaction parameter and 0.45 for the functional dependency matching parameter. The weight values ​​can be adjusted within the range of 0 to 1 according to the project's functional control requirements. The sum of the weights of the two parameters is 1, and the single-dimensional functional satisfaction evaluation value of each candidate building unit is calculated.

[0053] Finally, the single-dimensional functional satisfaction evaluation values ​​of all candidate building units are integrated, and a weighted average calculation is performed with the functional importance coefficient corresponding to each unit as the weight. The functional importance coefficient is generated by converting the functional priority level of each building unit extracted from the functional requirement data. The functional priority level is divided into three levels: core function, important function, and general function, with corresponding baseline coefficients of 1.0, 0.7, and 0.4, respectively. The coefficient value ranges from 0 to 1. The overall functional satisfaction evaluation value of the site is generated, and the functional satisfaction evaluation value of individual units and the overall functional satisfaction evaluation value are output simultaneously for subsequent constraint conflict analysis and layout scheme iteration optimization.

[0054] Furthermore, the method provided in this application embodiment includes: The environmental constraint data is analyzed to obtain the site's sunlight conditions, prevailing wind direction distribution, and location of environmentally sensitive areas. The degree of sunlight acquisition and the degree of ventilation channel connectivity are calculated based on the spatial location of the candidate building units in the initial layout structural model. Environmental impact parameters are generated by combining the distance relationship between the candidate building units and the environmentally sensitive areas. Based on the degree of sunlight acquisition, the degree of ventilation channel connectivity, and the environmental impact parameters, the environmental adaptability coefficient is calculated, and the environmental adaptability coefficient is used as the environmental adaptability evaluation value.

[0055] Specifically, the environmental adaptation evaluation layer retrieves standardized input data that has been mapped and matched, and simultaneously retrieves standardized environmental constraint datasets, spatial location parameters corresponding to each candidate building unit, and overall site topography and planning control data. Layered analysis is performed on the environmental constraint data to extract the site's corresponding sunshine standard requirements, annual and key solar term sunshine condition distribution data, distribution characteristics of the site's dominant wind direction and seasonal wind direction, wind speed and ventilation corridor control requirements. Simultaneously, the spatial boundaries, control types, avoidance distance thresholds, and compliance control requirements of environmentally sensitive areas within and around the site are extracted. Environmentally sensitive areas include ecological protection control zones, noise-sensitive zones, pollution source impact zones, and landscape control zones. This completes the parameterization transformation of core environmental constraint elements, providing standardized control benchmarks and input data for subsequent specialized calculations.

[0056] Next, the spatial coordinates, closed boundary of the site, and preset building height parameters of each candidate building unit in the initial layout structural model are retrieved. Simultaneously, the shading impact data of surrounding buildings and topographic elevation data are retrieved. Based on the current building daylighting design code standards, the hourly daylighting simulation calculation method commonly used in this field is adopted. The calculation period is based on the winter solstice or the day of the coldest day specified in the code. The effective sunshine period specified in the code is taken and calculated hourly step by hour. The solar altitude angle and azimuth angle at the corresponding time are calculated. Combined with the building outline, height and topographic elevation, shadow projection analysis is performed to determine whether the target building unit is shaded by surrounding buildings and topography and the shading period is eliminated. The sunshine duration without shading within the site of the target building unit is counted. At the same time, invalid sunshine segments with a single continuous duration of less than 15 minutes as specified in the code are eliminated. The effective sunshine duration of the corresponding site area and ancillary spaces of each candidate building unit is obtained. The actual effective sunshine duration is compared with the minimum sunshine duration threshold required by the standard. The sunshine acquisition degree parameter is calculated according to the formula: Sunshine Acquisition Degree Parameter = min(Actual Effective Sunshine Duration / Minimum Sunshine Duration Threshold of Standard, 1). The sunshine acquisition degree parameter corresponding to each candidate building unit is generated. The value of the sunshine acquisition degree parameter is in the range of 0-1. When the actual effective sunshine duration reaches or exceeds the threshold, the parameter value is 1. When it does not reach the threshold, the value is taken according to the actual ratio. The higher the value, the better the sunshine conditions of the building unit are met.

[0057] Based on the analysis of the prevailing wind direction distribution data and the site ventilation corridor control range, three sub-item adaptation coefficients are first calculated: First, the angle between the main layout direction of the building unit and the prevailing wind direction of the site is calculated, and the wind direction angle adaptation coefficient in the 0-1 interval is generated according to linear rules. When the angle is 0 degrees (parallel to the prevailing wind direction), the coefficient is 1, and when the angle is 90 degrees (perpendicular to the prevailing wind direction), the coefficient is 0. Second, the overlap area between the building unit's footprint boundary and the preset ventilation corridor control range is calculated. The overlap area ratio is used as the shading ratio to generate the corridor shading compliance coefficient in the 0-1 interval. When there is no shading, the coefficient is 1, and when there is full shading, the coefficient is 0. Third, based on the ratio of the minimum spacing between adjacent building units to the building height, the wind environment circulation efficiency coefficient in the 0-1 interval is generated according to the conventional wind environment evaluation rules in this field. The larger the spacing-to-height ratio, the higher the circulation efficiency, and the closer the corresponding coefficient is to 1. According to the preset baseline weight ratio, the wind direction angle adaptation coefficient is 0.3, the corridor obstruction compliance coefficient is 0.4, and the wind environment circulation efficiency coefficient is 0.3, with a weight sum of 1. This can be adjusted according to the project's ventilation control requirements. The three sub-coefficients are weighted and integrated to generate the ventilation channel connectivity parameter for each candidate building unit. The ventilation channel connectivity parameter ranges from 0 to 1. The higher the value, the better the building unit's adaptability to the site's ventilation channels and its wind environment friendliness.

[0058] Next, the spatial boundaries and control requirements data of various environmentally sensitive areas obtained from the analysis are retrieved. Combined with the spatial location and land boundaries of each candidate building unit, the minimum straight-line distance between each candidate building unit and various environmentally sensitive areas is calculated based on a planar spatial coordinate system. The calculated actual distance is compared with the control threshold of the corresponding environmentally sensitive area. For environmentally sensitive areas that need to be forcibly avoided, the distance compliance is calculated. When the actual distance meets the minimum avoidance threshold, the compliance is assigned a value of 1. When it does not meet the threshold, the value is linearly decreased in the range of 0-1 according to the deviation ratio. For landscape resource areas that need to be adapted for use, the distance adaptability is calculated. The optimal adaptability distance range is conventionally set to 30m-200m based on the landscape resource type and site scale, which can be adjusted according to project planning requirements. Within the optimal adaptability distance range, the adaptability is assigned a value of 1. When it exceeds the range, the value is decreased according to the degree of distance deviation. By combining the preset weight coefficients of the control priorities of various environmentally sensitive areas, the compliance and adaptability values ​​of each individual unit are weighted and fused to generate environmental impact parameters for each candidate building unit. The environmental impact parameters range from 0 to 1, and the higher the value, the better the adaptability of the building unit to the control requirements of the environmentally sensitive area.

[0059] Finally, the parameters for sunlight acquisition, ventilation connectivity, and environmental impact corresponding to each candidate building unit are retrieved, and weighted coupling calculations are performed according to the preset weight ratios. The default baseline weight ratios are 0.35 for sunlight acquisition, 0.25 for ventilation connectivity, and 0.4 for environmental impact. The weight values ​​can be adjusted within the range of 0-1 according to the core focus of the project's environmental control. The sum of the weights of the three parameters is 1, and the environmental adaptability coefficient of each candidate building unit is calculated. The environmental adaptability coefficient ranges from 0 to 1, and the higher the value, the better the overall adaptability of the building unit to the site environmental constraints. The environmental adaptability coefficients of all candidate building units are integrated and weighted averaged using the floor area of ​​each candidate building unit as the weight. The results are then adjusted based on the compliance status of the site's overall mandatory environmental constraints. If there are any non-compliant mandatory environmental constraints, the coefficients of the overall evaluation value are adjusted within the range of 0.8-1.0 according to the impact scope and control priority of the non-compliant items, generating the overall site environmental adaptability evaluation value. Similarly, the environmental adaptability coefficients of individual units and the overall environmental adaptability evaluation value are output simultaneously for subsequent constraint conflict analysis and layout scheme iteration optimization.

[0060] Furthermore, the method provided in this application embodiment includes: The convergence result is determined by comparing the degree of change of the layout co-evolution state matrix during continuous iteration. When the degree of change is less than the preset convergence threshold, the layout topology network is determined to have reached a stable state.

[0061] In one embodiment, convergence result determination is performed synchronously during the aforementioned iterative update process. After each round of iterative update of the layout topology network, the layout co-evolution state matrix generated in the current round and the previous consecutive rounds is retrieved. The spatial location coordinates, land occupation boundary parameters, and functional association parameters corresponding to all topology nodes in the matrix are extracted, and the overall change degree of matrix parameters between consecutive iteration rounds is calculated. The overall change degree adopts the parameter deviation rate calculation method commonly used in the art: first, the relative deviation value of each parameter of a single node is calculated, and then a weighted average calculation is performed with the land occupation area of ​​each node as the weight to obtain the overall change degree value of the layout co-evolution state matrix. The preset convergence threshold is set to 0.5% by default, and can be adjusted within the range of 0.1%-1% according to the optimization accuracy requirements of the scheme.

[0062] When the overall change value obtained from two consecutive iterations is less than the preset convergence threshold, the layout topology network is determined to have reached a stable state, and the iterative update process is terminated. If the convergence condition is not met, the updated layout co-evolution state matrix is ​​input into the multi-objective integrated optimization channel to perform the next round of constraint coupling propagation and three-dimensional objective evaluation, and the iterative optimization process is continuously advanced until the above condition is met.

[0063] Furthermore, the method provided in this application embodiment includes: After determining that the layout topology network has reached a stable state, architectural engineering layout structure data is generated based on the spatial positional relationships of candidate building units in the layout topology network, and the corresponding architectural engineering layout planning scheme is output based on the architectural engineering layout structure data.

[0064] Optionally, after determining that the layout topology relationship network has reached a stable state, the final version of the layout topology relationship network corresponding to the termination iteration is retrieved first, and the unique identifier, functional attribute parameters, spatial location coordinates, closed land boundary data, and inter-unit adjacency relationship data of each topology node in the network are extracted. The core parameters of all nodes are integrated to generate standardized building engineering layout structure data.

[0065] Next, based on the building layout and structural data, and combined with the current building design and drawing standards, basic planning data such as site boundaries, control red lines, and supporting facilities are added to generate a layout planning scheme for building design that includes building unit floor plan, functional zoning, related circulation lines, and compliance control information. This scheme data is compatible with the data format requirements of general building design platforms and can be directly used in the subsequent detailed design stage.

[0066] Furthermore, the method provided in this application embodiment includes: The layout planning scheme is sent to the target user, the target user's identification feedback is established, the identification feedback is used to establish a feedback data sequence, and the feedback data sequence is used to perform self-optimization management of the architectural engineering design layout.

[0067] Optionally, after generating the final layout plan, the layout plan, along with the corresponding compliance evaluation report and multi-dimensional optimization instructions, are sent to the target user through the user terminal of the architectural design collaboration platform. Simultaneously, a unique scheme identification identifier is assigned to the layout plan, establishing a binding relationship between the scheme identification identifier and the target user's identity identifier. The platform receives feedback from the target user regarding adjustments, optimization suggestions, and compliance objections to the layout plan, and binds this feedback to the corresponding scheme identification identifier, thus completing the identification feedback establishment for the target user.

[0068] Next, all user feedback content with tags undergoes parametric transformation and classification. It is then sorted and archived according to the order of receipt, scheme identification, and feedback content type, establishing a structured feedback data sequence. This sequence includes standardized data items such as scheme identification, feedback time, feedback type, parametric adjustment requirements, and constraint correction requirements. Valid data from the feedback data sequence is retrieved, and self-optimization management of the architectural engineering design layout is executed. For individual schemes requiring personalized adjustments, parametric constraint correction requirements and optimization target adjustment data are updated to the corresponding scheme's initial functional and environmental constraint data, re-triggering the layout optimization iteration process and generating an updated layout planning scheme adapted to user feedback needs.

[0069] For common feedback content that frequently appears in multiple solutions, when the same type of feedback content appears repeatedly in 3 or more independent solutions of the same type of project, or is repeatedly raised in 2 or more rounds of user feedback for the same project, it is determined to be high-frequency common feedback content. The benchmark ratio of multi-objective optimization weights, the default setting of constraint control thresholds, and the benchmark parameters of evaluation rules are updated to form a self-optimizing parameter benchmark library. This provides more adaptable basic parameters for the layout planning optimization of subsequent projects of the same type, and completes the closed-loop management from user feedback to layout optimization.

[0070] Furthermore, the method provided in this application embodiment includes: Based on the functional requirement data, anomalies in the layout planning scheme are identified and marked with visual markers. These visual markers are then added to the layout planning scheme and displayed on the screen simultaneously.

[0071] Optionally, while generating the layout planning scheme, a full-process anomaly self-check is performed on the layout planning scheme based on the initial input functional requirement data and mandatory constraint control requirements. The self-check content includes the matching degree between the functional capacity of building units and functional requirements, the satisfaction of functional adjacency requirements, the compliance of mandatory environmental constraints, and the compliance of building spacing with site control red lines.

[0072] For anomalies identified by self-inspection, corresponding visual markers are established according to the anomaly type and severity level. Anomalies that do not comply with mandatory constraints are marked with a highly recognizable first type of marker, which is a closed red solid line annotation box with a text description of the anomaly content. Anomalies that are optimization-related due to insufficient functional matching are marked with a second type of marker, which is a closed yellow dashed line annotation box with an optimization prompt text description. At the same time, the visual markers are bound to the spatial location of the building unit corresponding to the anomaly and the description of the anomaly content.

[0073] Finally, all generated visual markers are added to the corresponding layers of the layout planning scheme to form a layout planning scheme with anomaly self-checking indicators. The marked scheme data is then transmitted to the display terminal, and the full content of the scheme and the visual markers are displayed synchronously on the screen, making it easy for designers and users to intuitively identify abnormal content and optimization directions.

[0074] In summary, the architectural engineering design layout planning method based on multi-objective optimization provided in this application has the following technical effects: This application constructs a building layout topology network and performs constraint coupling propagation. Through three-dimensional hierarchical evaluation of space, function, and environment, constraint conflict analysis, and iterative convergence calculation, the layout is optimized and adjusted by combining multi-dimensional evaluation values ​​and mandatory constraint requirements. This achieves multi-objective automated optimization of building engineering design layout, making the compliance, adaptability, and optimization accuracy of the layout planning scheme more reliable and efficient. It achieves the technical effect of multi-dimensional collaborative optimization of building layout, improving planning efficiency and accuracy, and outputting stable and feasible layout schemes.

[0075] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0076] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for planning a layout of a construction project based on multi-objective optimization, characterized in that, The method includes: Obtain site boundary data, functional requirement data, building unit type data, and environmental constraint data of the building project to be planned, and construct an initial layout structure model containing multiple candidate building units and spatial relationships based on the site boundary data and building unit type data. Spatial topology analysis is performed on each candidate building unit in the initial layout structure model to extract multi-dimensional layout relationship features that characterize the spatial adjacency, functional dependency, and traffic connectivity between building units, and a layout topology network is constructed based on the multi-dimensional layout relationship features. After constructing the initial functional constraint state and environmental constraint state using functional requirement data and environmental constraint data respectively, the multi-dimensional layout relationship features, initial functional constraint state and environmental constraint state are input into the multi-objective integrated optimization channel. Constraint coupling propagation is performed along the layout topology relationship network in the multi-objective integrated optimization channel to form a layout cooperative evolution state matrix that characterizes the overall layout cooperative state. Based on the layout co-evolution state matrix, the evaluation analysis of the three-dimensional target evaluation sub-channel is performed. The evaluation analysis results are used to perform iterative updates of the layout topology relationship network. Based on the convergence results, the layout planning scheme is output.

2. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 1, characterized in that, The three-dimensional target evaluation sub-channel is a processing sub-channel of the multi-target integrated optimization channel, and the evaluation items of the three-dimensional target evaluation sub-channel include space utilization items, functional satisfaction items, and environmental adaptation items.

3. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 2, characterized in that, The evaluation analysis of the three-dimensional target evaluation sub-channel is performed based on the aforementioned layout co-evolution state matrix, including: The layout co-evolution state matrix is ​​mapped to the space utilization evaluation layer, functional satisfaction evaluation layer and environmental adaptation evaluation layer in the three-dimensional target evaluation sub-channel. The space utilization evaluation layer is used to calculate the space utilization evaluation value based on the spatial adjacency relationship and land distribution between candidate building units; The functional satisfaction evaluation layer calculates the functional satisfaction evaluation value based on the functional requirement data and the functional dependencies between candidate building units. The environmental adaptation evaluation layer calculates the environmental adaptation evaluation value based on the environmental constraint data and the spatial location of the candidate building units. The evaluation values ​​of space utilization, functional satisfaction, and environmental adaptability are synchronized to the multi-objective conflict analysis layer to establish the evaluation analysis results.

4. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 3, characterized in that, The space utilization evaluation layer calculates space utilization evaluation values ​​based on the spatial adjacency relationships and land distribution among candidate building units, including: Extract the footprint boundary of each candidate building unit and the spatial distance data between each candidate building unit and its adjacent candidate building units. Calculate the effective usable space range of the corresponding candidate building unit based on the extraction results. Spatial density parameters are generated based on the proportion of the land area occupied by candidate building units within the site boundary and the spatial distance between adjacent candidate building units. The space utilization efficiency coefficient of each candidate building unit is calculated by combining the effective available space range and space density parameters, and the space utilization efficiency coefficient is output as the space utilization evaluation value.

5. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 3, characterized in that, The functional satisfaction evaluation layer calculates a functional satisfaction evaluation value based on the functional requirement data and the functional dependencies between candidate building units, including: The functional requirement data is parsed to generate functional capacity requirement parameters and functional adjacency requirement parameters for each candidate building unit. The functional adjacency status between candidate building units is extracted based on the layout topology network, and a functional dependency mapping table between candidate building units is constructed in combination with the functional adjacency requirement parameters. Based on the functional dependency mapping table, the degree of functional dependency satisfaction between each candidate building unit is determined, and functional dependency matching parameters characterizing the functional synergy relationship between the candidate building units are generated. The functional capacity satisfaction parameter is calculated by combining the functional capacity requirement parameter with the actual bearing capacity of the candidate building unit, and the functional capacity satisfaction parameter is coupled with the functional dependency matching parameter to obtain the functional satisfaction evaluation value.

6. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 3, characterized in that, The environmental adaptation evaluation layer calculates environmental adaptation evaluation values ​​based on the environmental constraint data and the spatial location of candidate building units, including: The environmental constraint data is analyzed to obtain the site's sunshine conditions, prevailing wind direction distribution, and location of environmentally sensitive areas; Calculate the degree of sunlight acquisition and the degree of ventilation channel connectivity based on the spatial location of the candidate building units in the initial layout structural model; Environmental impact parameters are generated by combining the distance relationship between candidate building units and environmentally sensitive areas. Based on the sunlight acquisition degree, ventilation channel connectivity degree and environmental impact parameters, an environmental adaptability coefficient is calculated, and the environmental adaptability coefficient is used as the environmental adaptability evaluation value.

7. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 1, characterized in that, The convergence result is determined by comparing the degree of change of the layout co-evolution state matrix during continuous iteration. When the degree of change is less than the preset convergence threshold, the layout topology network is determined to have reached a stable state.

8. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 7, characterized in that, After determining that the layout topology network has reached a stable state, architectural engineering layout structure data is generated based on the spatial positional relationships of candidate building units in the layout topology network, and the corresponding architectural engineering layout planning scheme is output based on the architectural engineering layout structure data.

9. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 1, characterized in that, The layout planning scheme is sent to the target user, the target user's identification feedback is established, the identification feedback is used to establish a feedback data sequence, and the feedback data sequence is used to perform self-optimization management of the architectural engineering design layout.

10. The architectural engineering design layout planning method based on multi-objective optimization as described in claim 1, characterized in that, Based on the functional requirement data, anomalies in the layout planning scheme are identified and marked with visual markers. These visual markers are then added to the layout planning scheme and displayed on the screen simultaneously.

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