Digital design method for construction project based on BIM and data collaboration

Abstract

This invention discloses a digital design method for construction projects based on BIM and data collaboration, belonging to the field of digital design technology for construction projects. The method includes acquiring an initial design model set of the target construction project and a cross-disciplinary design parameter set containing constraints from multiple disciplines such as structure, MEP, and architecture. The initial design model set is then meshed in three dimensions to generate a structured design space containing geometric voxels and attribute metadata. Within this space, a parameter semantic association network is constructed, forming a parameter association network model representing parameter dependencies and conflicts. Based on this model, multi-disciplinary constraint consistency evolution calculations are performed to generate a collaborative design optimization space that satisfies all constraints. A computer-aided design optimization engine is used for global optimization to obtain the final collaborative design scheme, which is then fed back to the BIM design platform. This invention integrates geometric and attribute data, clarifies cross-disciplinary parameter relationships, and realizes multi-disciplinary digital collaborative design optimization of construction projects.

Description

Technical Field

[0001] This invention belongs to the field of digital design technology for construction projects, specifically a digital design method for construction projects based on BIM and data collaboration. Background Technology

[0002] Traditional digital design in construction projects often relies on BIM platforms for multidisciplinary design work. Design parameters for structural, MEP, and architectural disciplines are transmitted independently, and initial design models are stored in an unstructured form. Design parameters and model geometry are separated, and cross-disciplinary design constraints are manually checked and adjusted. The relationships between design parameters across disciplines can only be determined manually, and design optimization primarily involves localized manual adjustments, lacking a unified spatial framework to integrate the design model and parameters.

[0003] In traditional BIM design models, the initial design model has not undergone 3D spatial meshing, making it impossible to form a structured spatial form integrating geometric voxels and attribute metadata. Multi-disciplinary design parameters lack standardized methods for constructing related models, failing to create a model representing parameter dependencies and conflicts. Cross-disciplinary design constraints cannot be synchronously adapted through evolutionary calculations, making it difficult to generate a collaborative design optimization space that satisfies all disciplinary constraints. The efficiency of design parameter conflict identification and constraint verification is limited, and the effectiveness of multi-disciplinary design collaboration is difficult to guarantee. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a digital design method for construction projects based on BIM and data collaboration, including: Obtain the initial design model set of the target construction project and the associated cross-disciplinary design parameter set, wherein the cross-disciplinary design parameter set includes design constraints of multiple disciplines such as structure, mechanical and electrical engineering, and architecture; Perform three-dimensional spatial meshing on the initial design model set to generate a structured design space containing geometric voxels and attribute metadata; In the structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set to form a parameter association network model that describes the dependency and conflict relationships between design parameters; Based on the parameter association network model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that satisfies the constraints of all cross-disciplinary design parameter sets. The computer-aided design optimization engine is invoked to perform global optimization within the collaborative design optimization space, generate the final collaborative design scheme, and feed the final collaborative design scheme back to the BIM design platform.

[0005] Furthermore, a three-dimensional spatial meshing process is performed on the initial design model set to generate a structured design space containing geometric voxels and attribute metadata, including: Boundary representation analysis is performed on each 3D model in the initial design model set to extract the geometric topology of the model surface; Based on a preset mesh resolution threshold, the geometric surface of each 3D model is adaptively divided into triangular facet meshes to generate a uniform model surface mesh. Based on the surface mesh of the model, a tetrahedral mesh generation algorithm is used to fill the interior of the model with a volume mesh to form a complete voxel representation of the model. From the associated information of the initial design model set, extract the professional type, designer, timestamp and physical attributes corresponding to each geometric voxel, and bind them to the corresponding voxel as attribute metadata; All voxels that have completed attribute binding are organized according to three-dimensional spatial coordinates to construct the structured design space containing hierarchical spatial indexes.

[0006] Furthermore, within the structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set to form a parameter association network model describing the dependencies and conflicts between design parameters, including: Each design constraint in the cross-disciplinary design parameter set is analyzed and transformed into a standardized parameter item containing parameter name, value range, professional source, and constraint type. In the structured design space, locate the set of geometric voxels associated with each standardized parameter item and establish the mapping relationship between parameter items and spatial voxels; Analyze whether there is an intersection between the sets of associated voxels of different standardized parameter items in the structured design space. If there is an intersection, establish potential association edges between the corresponding parameter items. The semantic relevance between parameter items with potential associated edges is calculated. The semantic relevance is derived based on the similarity of the terminology of the parameter names, the logical compatibility of the constraint types, and the spatial overlap of the associated voxels. Using standardized parameter items as nodes and the semantic relevance as edge weights, a parameter association network model is constructed to describe the dependency, transmission, and conflict relationships among all design parameters.

[0007] Furthermore, based on the parameter association network model, multi-disciplinary constraint consistency evolution calculations are performed on the structured design space to generate a collaborative design optimization space that satisfies the constraints of all cross-disciplinary design parameter sets, including: Traverse the parameter association network model to identify all parameter clusters that have direct or indirect associations; For each parameter cluster, check whether there are logical conflicts in the design constraints of the parameter items within the cluster. The logical conflicts include contradictory numerical ranges, overlapping space occupancy, and incompatible physical properties. For a cluster of parameter items with logical conflicts, a constraint resolution calculation is initiated. The constraint resolution calculation includes: sorting the conflicting constraints according to a preset professional priority rule; and using a constraint relaxation algorithm to adjust the constraint range of non-high priority parameter items until all constraints in the cluster reach a satisfyable state. After the constraint resolution calculation is completed, the conflict-free parameter cluster is remapped back to the structured design space with adjusted constraints, and the attribute metadata of the corresponding geometric voxels is updated. The updated structured design space is marked as the collaborative design optimization space that meets the consistency requirements of all cross-disciplinary design parameter sets.

[0008] Furthermore, for clusters of parameter terms with logical conflicts, initiating constraint resolution calculation includes: Extract all design constraints that constitute the conflict from the cluster of logically conflicting parameter items; Query the preset professional knowledge base to obtain the professional importance level, design code enforcement level, and modification cost assessment corresponding to each design constraint; Based on the aforementioned professional importance level, design specification mandatory level, and modification cost assessment, calculate the priority weight of each design constraint in conflict resolution; Conflicting design constraints are sorted in descending order according to their priority weights, and the constraint with the highest weight is retained as the baseline constraint. For constraints with weights lower than the baseline constraint, the constraint relaxation algorithm is invoked to adjust the boundaries within their original value range. The goal of this adjustment is to eliminate conflicts with higher-weight constraints while minimizing the adjustment magnitude. The constraint relaxation and conflict detection are performed iteratively until all constraints in the parameter cluster can be satisfied simultaneously, or the preset maximum number of iterations is reached.

[0009] Furthermore, the step of invoking the computer-aided design optimization engine to perform global optimization within the collaborative design optimization space and generate the final collaborative design scheme includes: Within the collaborative design optimization space, a multi-objective optimization function is defined. The design variables of the multi-objective optimization function are the adjustable properties of geometric voxels, and the optimization objectives include construction cost estimation, structural performance indicators, space utilization efficiency, and energy consumption simulation indicators. Using the intelligent optimization algorithm in the computer-aided design optimization engine, an initial design scheme population is generated within the design variable range allowed by the collaborative design optimization space; For each design scheme in the population, a rapid performance simulation is performed on the BIM design platform to calculate its corresponding multi-objective optimization function value; Based on the multi-objective optimization function value, the population of the design scheme is iteratively evolved through selection, crossover, and mutation operations to guide the population to converge toward the Pareto optimal frontier. When the convergence condition or the maximum number of generations is reached, one or more non-dominated solutions are selected from the final population as candidate cooperative design schemes.

[0010] Furthermore, for each design scheme in the population, a rapid performance simulation is performed on the BIM design platform to calculate its corresponding multi-objective optimization function value, including: The design scheme is converted into an intermediate data format that can be recognized by the BIM design platform, including adjusted geometric and attribute information; The cost estimation module integrated into the BIM design platform is invoked to automatically calculate the estimated construction cost based on the geometric and material property information of the design scheme. The structural analysis module integrated into the BIM design platform is invoked to perform simplified mechanical analysis on the design scheme and extract key structural performance indicators. Use the space analysis tools integrated into the BIM design platform to calculate the space utilization efficiency-related indicators of the design scheme. The energy consumption simulation engine integrated into the BIM design platform is invoked to perform simplified energy consumption simulation based on the three-dimensional model of the design scheme and the properties of the building envelope, thereby obtaining energy consumption simulation indicators. The calculated estimated construction cost, structural performance indicators, space utilization efficiency indicators, and energy consumption simulation indicators are combined into the comprehensive objective function value of the design scheme according to the weights or aggregation methods defined in the multi-objective optimization function.

[0011] Furthermore, based on the multi-objective optimization function value, the population of design schemes is iteratively evolved through selection, crossover, and mutation operations, including: Based on the comprehensive objective function value of each design scheme, a roulette wheel selection method is used to select the parent design scheme from the current population with a probability proportional to the quality of the objective function value. The selected parent design schemes are paired up, and under the constraints of the collaborative design optimization space, the adjustable attribute values ​​of some geometric elements in the paired schemes are exchanged to generate the child design schemes. For the generated offspring design scheme, the adjustable attribute values ​​of some of its geometric voxels are randomly changed with a preset low probability. The change must ensure that the new attribute values ​​are still within the allowable range defined in the collaborative design optimization space. The offspring design schemes generated through selection, crossover, and mutation operations are merged with some elite parent design schemes to form a new generation of design scheme populations. Calculate the combined objective function value of all design schemes in the new generation population and use it for the selection operation in the next iteration.

[0012] Furthermore, the step of selecting one or more non-dominated solutions as candidate cooperative design schemes from the final population when the convergence condition or the maximum number of generations is reached includes: In the final design scheme population, perform non-dominated sorting to find all non-dominated solutions that are not dominated by any other design scheme, and form the Pareto optimal solution set. For each nondominated solution in the Pareto optimal solution set, calculate its performance value on each optimization objective, and generate the corresponding design snapshot and key performance report; Receive selection instructions from the user from the Pareto optimal solution set, or automatically select an optimal solution according to preset decision rules; The optimal solution, selected by the user or automatically, is reconstructed in the BIM design platform as a complete and editable 3D BIM model, which serves as the final collaborative design solution.

[0013] Furthermore, after feeding the final collaborative design scheme back to the BIM design platform, the process also includes: In the BIM design platform, the final collaborative design scheme is automatically generated and annotated with design drawings and documents from all disciplines. The final collaborative design scheme, its corresponding optimization process data, constraint resolution records, and performance simulation results are packaged into a standardized data package and stored in the project collaborative database. Based on standardized data packages in the project collaboration database, the version management and impact propagation analysis process for design changes is initiated in the BIM design platform to ensure that subsequent design changes are consistent with the established collaboration plan.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The initial design model set is subjected to three-dimensional spatial meshing to generate a structured design space containing geometric voxels and attribute metadata. This transforms the traditional scattered and independent BIM design model into a spatial carrier in which geometric information and attribute data are bound together. This achieves the integrated integration of the geometric form and associated attributes of the design model, allowing the design parameters of multiple disciplines such as structure, MEP, and architecture to be presented in a unified spatial structure. This eliminates the separation between the design model and attribute metadata, and provides a unified carrier and presentation carrier for the design constraints of each discipline.

[0015] In a structured design space, a parameter semantic association network is constructed for a set of cross-disciplinary design parameters, forming a parameter association network model that can describe the dependencies and conflicts between parameters. Based on this model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that adapts to all cross-disciplinary design parameter constraints. This automatically represents the association and conflict states between multi-disciplinary parameters, completes the synchronous adaptation of multi-disciplinary design constraints, and forms a standardized design space that can be used by a computer-aided design optimization engine for global optimization, allowing design optimization work to be carried out within a space that conforms to all professional constraints. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the digital design method for construction projects based on BIM and data collaboration as described in this invention. Figure 2 A flowchart for generating a structured design space; Figure 3 A flowchart for constructing a parameter association network model; Figure 4 The Pareto optimal frontier (construction cost vs. annual energy load); Figure 5 A spatial diagram illustrating the collaborative design of electromechanical systems (ventilation ducts and cable trays). Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1 The process involves acquiring the initial design model set of the target construction project and its associated cross-disciplinary design parameter set, which includes design constraints from structural, MEP, and architectural disciplines. A 3D spatial mesh is then performed on the initial design model set to generate a structured design space containing geometric voxels and attribute metadata. Within this structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set, forming a parameter association network model describing the dependencies and conflicts between design parameters. Based on this model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that satisfies all cross-disciplinary design parameter set constraints. A computer-aided design optimization engine is invoked to perform global optimization within the collaborative design optimization space, generating a final collaborative design scheme, which is then fed back to the BIM design platform.

[0019] In one embodiment of the present invention, see [reference] Figure 2 The initial design model set is subjected to 3D spatial meshing to generate a structured design space containing geometric voxels and attribute metadata. The process involves parsing the boundary representation of each 3D model in the initial design model set, extracting the geometric topology of the model surface. Based on a preset mesh resolution threshold, the geometric surface of each 3D model is adaptively divided into triangular facet meshes, generating a uniform model surface mesh. Based on the model surface mesh, a tetrahedral mesh generation algorithm is used to fill the model's interior with a volume mesh, forming a complete voxel representation of the model. From the associated information of the initial design model set, the professional type, designer, timestamp, and physical attributes corresponding to each geometric voxel are extracted; this information is used as attribute metadata and bound to the corresponding voxel. All voxels with completed attribute binding are organized according to 3D spatial coordinates, constructing a structured design space containing hierarchical spatial indexes.

[0020] In practical implementation, the implementation method of 3D spatial meshing in the digital design method of construction projects based on BIM and data collaboration is described using an example scenario of an office building construction project. The initial design model set includes a 3D architectural model, a 3D structural model, and a 3D electromechanical model. The cross-disciplinary design parameter set is associated with the initial design model set. When performing 3D spatial meshing on the initial design model set, boundary representation is parsed and applied to each 3D model in the initial design model set. The vertex, edge, and face information of the model surface is extracted to form the geometric topology. In some embodiments, the mesh resolution threshold is preset to 0.1 meters. In another example, the mesh resolution threshold is preset to 0.5 meters. Data comparison shows that the mesh density generated by the 0.1-meter threshold is higher than that of the 0.5-meter threshold, but the processing time increases accordingly. According to the preset mesh resolution threshold, the geometric surface of each 3D model is adaptively divided into triangular facet meshes. The division process adjusts the size of the triangular facets according to the local curvature. Larger facets are used in flat areas and smaller facets are used in high-curvature areas, thus generating a uniform model surface mesh. The criterion formula for adaptive triangular facet meshing is: in: Indicates the target side length of the local triangular facet. These are constant coefficients used to control the overall mesh density. This represents the absolute value of the curvature of a geometric surface at a local point. A very small positive number is used to avoid division by zero errors. Based on the model surface mesh, a tetrahedral mesh generation algorithm fills the model's interior with a volume mesh. The algorithm generates tetrahedral elements from the surface mesh inwards, forming a complete voxel representation of the model. In specific implementations, the professional type, designer, timestamp, and physical attributes corresponding to each geometric voxel are extracted from the associated information of the initial design model set. The professional type includes architectural, structural, or MEP categories; the designer identifies the creator; the timestamp records the modification time; and the physical attributes include material density and thermal conductivity. Attribute metadata is bound to the corresponding voxel. It can be understood that all voxels with completed attribute binding are organized according to three-dimensional spatial coordinates, defined based on a global coordinate system. Voxels are inserted into an octree index structure according to their spatial position, constructing a structured design space containing a hierarchical spatial index. Optionally, the hierarchical spatial index supports fast spatial queries, such as retrieving overlapping voxels based on coordinate ranges. In some embodiments, data is compared to determine the volume error of the structured design space under different mesh resolution thresholds; at a threshold of 0.1 meters, the volume error is lower than the 0.5-meter threshold. In practice, the geometric elements and attribute metadata in the structured design space provide input for the subsequent construction of the parameter semantic association network.

[0021] In one embodiment of the invention, within a structured design space, see [reference]. Figure 3 This study constructs a parameter semantic association network for a cross-disciplinary design parameter set, forming a parameter association network model that describes the dependencies and conflicts among design parameters. The construction process involves parsing each design constraint in the cross-disciplinary design parameter set, transforming each constraint into a standardized parameter item containing the parameter name, value range, professional origin, and constraint type. In the structured design space, the set of geometric voxels associated with each standardized parameter item is located, establishing the mapping relationship between parameter items and spatial voxels. The study analyzes whether there is an intersection between the associated voxel sets of different standardized parameter items in the structured design space; if an intersection exists, potential association edges are established between the corresponding parameter items. The semantic relevance between parameter items with potential association edges is calculated, based on the similarity of parameter names in professional terminology, the logical compatibility of constraint types, and the spatial overlap of associated voxels. Using standardized parameter items as nodes and semantic relevance as edge weights, a parameter association network model describing the dependencies, transmission, and conflicts among all design parameters is constructed.

[0022] In practical implementation, the method of constructing a parameter semantic association network in the digital design method of construction projects based on BIM and data collaboration is described using an example scenario of an office building construction project. The structured design space already contains geometric voxels and attribute metadata of the architectural, structural, and MEP models. The cross-disciplinary design parameter set includes design constraints such as "minimum cross-section of structural column 400mm x 400mm", "clear height of ventilation duct from beam bottom not less than 300mm", and "clear height of conference room interior not less than 2.8 meters". In the structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set to parse each design constraint in the cross-disciplinary design parameter set. A constraint "clear height of ventilation duct from beam bottom not less than 300mm" is transformed into a standardized parameter item containing the parameter name "clear height of duct from beam bottom", the value range "≥300mm", the professional source "MEP", and the constraint type "lower limit of spatial distance". In practical implementation, the set of geometric voxels associated with each standardized parameter item is located in the structured design space. The parameter "clear height of the duct beam bottom" is associated with all ventilation duct voxels and structural beam voxels, and "clear height inside the conference room is not less than 2.8 meters" is associated with all floor slab voxels and ceiling voxels located in the conference room area, establishing a mapping relationship between parameter items and spatial voxels. The intersection of the associated voxel sets of different standardized parameter items in the structured design space is analyzed. The voxel set associated with the parameter "clear height of the duct beam bottom" and the voxel set associated with the parameter "clear height inside the conference room" have voxel intersection in the spatial area above the conference room, establishing potential association edges between the corresponding parameter items. It can be understood that the semantic relevance between parameter items with potential association edges is calculated. The semantic relevance is derived based on the similarity of the professional terminology of the parameter names, the logical compatibility of the constraint types, and the spatial overlap of the associated voxels. In some embodiments, cosine similarity is used to calculate the similarity of the professional terminology of the parameter names; in other embodiments, semantic distance calculation based on a professional thesaurus is used. Data comparison shows that the latter is more accurate in identifying synonyms such as "clear height" and "floor height." Semantic relevance is defined for quantification as: in: This indicates the semantic relevance strength between parameter terms a and b. This represents the similarity score between parameter names and technical terms calculated based on a word embedding model. This represents the logical compatibility score for the constraint type. The logical compatibility score is determined by the rule table based on whether the constraint is "required to be satisfied" or "recommended to be satisfied". The spatial overlap score represents the spatial overlap of related voxels. The spatial overlap score is calculated as the ratio of the number of intersecting voxels to the number of union voxels. These are preset weighting coefficients used to balance the influence of the three factors. Optional, the weighting coefficients... The initial spatial weights can be adjusted according to the project stage. The parameters can be set to a higher value. A parameter association network model is constructed, using standardized parameter items as nodes and semantic relevance as edge weights, to describe the dependencies, propagation, and conflicts among all design parameters. In practice, edge weights below a certain threshold are considered potential conflicts requiring further investigation, while edge weights above another higher threshold are considered strong dependencies. It can be understood that forming the parameter association network model defines the analysis objects and relationship foundation for subsequent multi-disciplinary constraint consistency evolution calculations.

[0023] In one embodiment of the present invention, based on a parameter association network model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that satisfies the constraints of all cross-disciplinary design parameter sets. The calculation process traverses the parameter association network model, identifying all parameter item clusters with direct or indirect associations. For each parameter item cluster, the design constraints of the parameter items within the cluster are checked for logical conflicts, including contradictions in numerical ranges, overlapping space occupancy, and incompatible physical attributes. For parameter item clusters with logical conflicts, constraint resolution calculation is initiated. The constraint resolution calculation extracts all design constraints constituting the conflict from the parameter item clusters with logical conflicts. A preset professional knowledge base is queried to obtain the professional importance level, design specification enforcement level, and modification cost assessment corresponding to each design constraint. Based on the professional importance level, design specification enforcement level, and modification cost assessment, the priority weight of each design constraint in conflict resolution is calculated. The conflicting design constraints are sorted in descending order according to their priority weights, and the constraint with the highest weight is retained as the benchmark constraint. For constraints with weights lower than the baseline constraint, a constraint relaxation algorithm is invoked to adjust the boundaries within their original value range. The goal of this adjustment is to eliminate conflicts with constraints of higher weights while minimizing the adjustment magnitude. Constraint relaxation and conflict detection are iteratively performed until all constraints within the parameter cluster can be simultaneously satisfied, or the preset maximum number of iterations is reached. The conflict-free parameter cluster, after constraint resolution calculations, has its adjusted constraints remapped back to the structured design space, and the attribute metadata of the corresponding geometric voxels is updated. The updated structured design space is then marked as a collaborative design optimization space that satisfies the consistency requirements of all cross-disciplinary design parameter sets.

[0024] In practical implementation, the method of multi-disciplinary constraint consistency evolution calculation in the digital design method of construction projects based on BIM and data collaboration is described using an example scenario of the core tube area of ​​an office building. The parameter association network model has been constructed, including the relationships between parameters such as "shear wall thickness not less than 200mm" for structural engineering, "main pipe space reserved width not less than 800mm" for mechanical and electrical engineering, and "corridor clear width not less than 1800mm" for architectural engineering. Based on the parameter association network model, multi-disciplinary constraint consistency evolution calculation is performed on the structured design space. The parameter association network model is traversed to identify all parameter clusters with direct or indirect relationships. In the example, the three parameters "shear wall thickness", "pipe space reserved width", and "corridor clear width" are identified as the same parameter cluster because they are associated with voxels in the same spatial location. For each parameter cluster, the design constraints of the parameters within the cluster are checked for logical conflicts. Logical conflicts include contradictory numerical ranges, overlapping space occupancy, and incompatible physical properties. In the example scenario, the sum of the shear wall thickness, the reserved width for pipe space, and the clear width of the corridor under the original design exceeds the distance along the building axis, constituting a logical conflict of overlapping space occupancy. Understandably, constraint resolution calculations are initiated for parameter clusters with logical conflicts.

[0025] In some embodiments, constraint resolution calculations extract all design constraints constituting the conflict from clusters of logically conflicting parameter items. The extracted design constraints include "shear wall thickness ∈ [200mm, 300mm]", "reserved width for pipe space ∈ [800mm, 1000mm]", and "clear width of corridor ∈ [1800mm, 2200mm]". A preset professional knowledge base is queried to obtain the professional importance level, design code enforcement level, and modification cost assessment corresponding to each design constraint. In the example, "shear wall thickness" is marked as "high" in both professional importance and design code enforcement level because it involves structural safety, while the modification cost assessment of "reserved width for pipe space" is assessed as "medium" because it involves multiple system routes. Based on the professional importance level, design code enforcement level, and modification cost assessment, the priority weight of each design constraint in conflict resolution is calculated. The priority weights are: in: This represents the priority weight of the i-th design constraint. A quantitative value representing the level of importance of a profession. The quantitative value representing the mandatory level of the design specification. This indicates a modification to the quantitative value of the cost assessment. These are preset weighting coefficients used to adjust the contribution ratio of the three factors in the calculation. Optional, weighting coefficients. It can be configured according to the project type, in typical office building projects. and The values ​​are relatively high. Conflicting design constraints are sorted in descending order of priority weight, with the constraint with the highest weight retained as the baseline constraint. In the example calculation, the "shear wall thickness" constraint has the highest priority weight, and its value range [200mm, 300mm] is determined as the baseline constraint and is not adjusted. For constraints with weights lower than the baseline constraint, the constraint relaxation algorithm is invoked to adjust the boundaries within their original value range. The goal of the adjustment is to eliminate conflicts with higher-weight constraints while minimizing the adjustment magnitude. In specific implementation, the constraint relaxation algorithm attempts to adjust the lower limits of the "pipe space reserved width" and the "corridor net width." In another example data comparison, two strategies, linear programming relaxation and heuristic search relaxation, are used; linear programming is more computationally efficient. Constraint relaxation and conflict detection are iteratively performed until all constraints within the parameter cluster can be simultaneously satisfied, or the preset maximum number of iterations is reached.

[0026] Understandably, in the office building example, after three iterations, the constraints were adjusted to "shear wall thickness = 250mm", "reserved width for pipe space = 850mm", and "clear width of corridor = 1900mm". These three conditions satisfy the space constraints without violating any original intent. The conflict-free parameter clusters, after constraint resolution calculations, are then remapped back to the structured design space with adjusted constraints. The corresponding attribute metadata of the geometric voxels is updated; for example, the "design clear width" attribute value of the voxels related to the corridor area is updated to 1900mm. The updated structured design space is then marked as a collaborative design optimization space that satisfies the consistency requirements of all cross-disciplinary design parameter sets. In this collaborative design optimization space, the parameter constraints bound to all voxels are logically consistent.

[0027] In one embodiment of the present invention, the implementation method of calling a computer-aided design optimization engine for global optimization in the digital design method of construction projects based on BIM and data collaboration is described in the example scenario of collaborative optimization of the facade and spatial layout of an office building. The collaborative design optimization space already includes adjustable voxel attributes of the exterior wall window-to-wall ratio, the depth of the sunshade components, and the position of the internal partition walls, which have been consistent. A multi-objective optimization function is defined in the collaborative design optimization space. The design variables of the multi-objective optimization function are the adjustable attributes of geometric voxels, such as the window-to-wall ratio of the south facade windows ranging from [0.3, 0.6], and the horizontal sunshade projection depth ranging from [0.0m, 0.9m]. The optimization objectives include construction cost estimation, structural performance indicators, space utilization efficiency, and energy consumption simulation indicators. The multi-objective optimization function uses a weighted aggregation method to synthesize multiple objectives into a single comprehensive objective function value, the formula of which is expressed as: in: This represents the comprehensive objective function value of the design scheme. This represents the target for the normalized construction cost estimate; a lower value is better. This represents the normalized structural lateral displacement index; a lower value indicates better structural performance. This represents the normalized space utilization efficiency index; a higher value is better. This represents the normalized annual cooling and heating load index; the lower the value, the better the energy efficiency. This represents the preset weight coefficient assigned to the j-th optimization objective. An intelligent optimization algorithm from the computer-aided design optimization engine is used to generate an initial design scheme population within the allowable range of design variables in the collaborative design optimization space. The intelligent optimization algorithm employs a genetic algorithm, with an initial population size of 50 individuals. Each individual represents a specific combination of design variable values ​​for window-to-wall ratio, shading depth, and partition wall location.

[0028] For each design scheme in the population, rapid performance simulation is performed on the BIM design platform to calculate its corresponding multi-objective optimization function value. The rapid performance simulation converts the design scheme into an intermediate data format recognizable by the BIM design platform. This intermediate data format is an IFC file fragment containing adjusted geometric and attribute information. The cost estimation module integrated into the BIM design platform is invoked to automatically calculate the estimated construction cost based on the geometric and material attribute information of the design scheme. The cost estimate is obtained by summing the products of wall area, window area, and shading component volume with the current market unit price. The structural analysis module integrated into the BIM design platform is invoked to perform simplified mechanical analysis on the design scheme and extract key structural performance indicators. In this example, the structural performance indicator is the maximum inter-story drift angle at the top floor of the building. The spatial analysis tool integrated into the BIM design platform is invoked to calculate the space utilization efficiency indicators of the design scheme. These indicators are measured by the effective number of standard office workstations. The energy consumption simulation engine integrated into the BIM design platform is invoked to perform simplified energy consumption simulation based on the 3D model of the design scheme and the building envelope attributes, obtaining energy consumption simulation indicators. These indicators represent the building's cumulative annual heating and cooling load under typical weather conditions. It is understandable that the calculated estimated construction cost, structural performance indicators, space utilization efficiency indicators, and energy consumption simulation indicators are combined according to the weights defined in the multi-objective optimization function to form the comprehensive objective function value of the design scheme. The weight coefficients v1 to v4 are set to 0.4, 0.2, 0.2, and 0.2 respectively in the example. Performance simulation results for some design scheme populations are shown in Table 1.

[0029] Table 1: Simulation Results of Population Performance under the Design Scheme Based on the multi-objective optimization function value, a population of design schemes is iteratively evolved through selection, crossover, and mutation operations. A roulette wheel selection method is used to choose a parent design scheme from the current population based on the comprehensive objective function value of each scheme, with a probability proportional to the quality of the objective function value. In the example, a smaller comprehensive objective function value Z indicates a better scheme, and the probability of a scheme being selected is proportional to (1 / Z). The selected parent design schemes are paired, and under the constraints of the collaborative design optimization space, the adjustable attribute values ​​of some geometric elements in the paired schemes are exchanged. For example, the window-to-wall ratio of scheme A on the south facade is exchanged with the sunshade depth value of scheme B to generate a child design scheme. The adjustable attribute values ​​of some geometric elements of the generated child design schemes are randomly changed with a preset small probability. The change operation must ensure that the new attribute values ​​are still within the allowable range defined by the collaborative design optimization space. For example, the window-to-wall ratio of a certain child scheme is randomly reset within the interval [0.3, 0.6] with a 5% probability. It is understandable that the offspring design schemes generated through selection, crossover, and mutation operations are merged with some elite parent design schemes to form a new generation of design schemes. The elite retention strategy retains the 10 schemes with the best comprehensive objective function value from the previous generation directly into the next generation. The comprehensive objective function value of all design schemes in the new generation is calculated and used for the selection operation in the next iteration. The iteration process is repeated.

[0030] When the convergence condition or the maximum number of generations is reached, one or more non-dominated solutions are selected from the final population as candidate collaborative design schemes. The convergence condition is defined as the optimal solution whose comprehensive objective function value no longer improves after the last 20 generations. Non-dominated solutions are sorted in the final design scheme population to find all non-dominated solutions not dominated by any other design scheme, forming a Pareto optimal solution set. One scheme dominating another means that it is not inferior in all optimization objectives and is better in at least one objective. For each non-dominated solution in the Pareto optimal solution set, its performance value on each optimization objective is calculated, and a corresponding design snapshot and key performance report are generated. The design snapshot is a 3D rendering, and the key performance report lists the specific values ​​of all optimization objectives. In some embodiments, the user's selection instruction from the Pareto optimal solution set is received. The user can manually select based on strategies such as cost priority or energy efficiency priority. In other embodiments, an optimal solution is automatically selected according to a preset decision rule, which may be the solution closest to the ideal point. Optionally, the Euclidean distance to the ideal point can be used for automatic decision-making. in: The Euclidean distance between a design scheme's representation point and its theoretical ideal point in a normalized target space is represented by a dimensionless scalar. The smaller the value, the closer the solution is to the ideal state. To optimize the target's sequence index, For the first The normalized values ​​of the optimization objectives For the theoretical optimal value of the j-th objective, Indicates the first For each optimization objective, the minimum value among all candidate solutions. Indicates the first For each optimization objective, the maximum value among all candidate solutions is used to reconstruct the optimal solution, either user-selected or automatically selected, into a complete and editable 3D BIM model in the BIM design platform. This model serves as the final collaborative design solution, and the reconstruction process drives the update of the original parametric BIM model based on the optimized variable values.

[0031] See Figure 4 In the multi-objective optimization phase of the digital design method for construction projects based on BIM and data collaboration, the Pareto optimal frontier (cost vs. energy consumption) intuitively presents the trade-off between construction cost and annual energy load. The red pentagrams in the figure represent Pareto optimal solutions obtained through global optimization. Within the current collaborative design optimization space, no other solution can outperform both objectives (cost or energy consumption) without sacrificing at least one. The gray dashed lines connect all Pareto optimal solutions, forming the Pareto optimal frontier, clearly outlining the performance boundary between cost and energy consumption. The evolution trend of the frontier shows that as construction cost gradually increases from 10.5 million yuan to 14.5 million yuan, the annual load (energy consumption) continuously decreases from 1000 GJ to 735 GJ, demonstrating the typical trade-off principle that "increasing construction cost investment can be exchanged for lower energy consumption performance." This frontier provides a quantitative basis for design decisions: when cost control is the core objective of the project, the low-cost solution on the left side of the frontier can be selected; if energy efficiency is the priority, the low-energy solution on the right side of the frontier can be selected; if a balance between cost and energy consumption is required, the compromise solution in the middle of the frontier can be selected, providing an intuitive and professional reference framework for the final collaborative design decision.

[0032] In one embodiment of the present invention, after the final collaborative design scheme is fed back to the BIM design platform, the platform automatically generates and annotates design drawings and documents for all disciplines. The final collaborative design scheme, its corresponding optimization process data, constraint resolution records, and performance simulation results are packaged into a standardized data package, which is stored in the project collaboration database. Based on the standardized data package in the project collaboration database, the version management and impact propagation analysis process for design changes is initiated in the BIM design platform.

[0033] In practical implementation, the method for feeding back the final collaborative design scheme to the BIM design platform in the digital design approach for construction projects based on BIM and data collaboration is described using an office building construction project as an example. The final collaborative design scheme is a 3D BIM model of a ten-story office building, which integrates optimized design information from architecture, structure, and MEP (Mechanical, Electrical, and Plumbing) disciplines. The BIM design platform automatically generates and annotates all professional design drawings and documents based on the final collaborative design scheme. The automatic generation process, based on the geometric information, attribute information, and professional classification of components in the model, drives the drawing generation engine to batch produce architectural floor plans, structural beam and slab reinforcement drawings, and MEP system diagrams. The annotation process automatically adds dimensions, component markers, annotations, and elevation symbols to the components in the drawings. In some embodiments, the annotation style template referenced is the "Chinese National Standard Construction Drawing Specification," while in other embodiments, the annotation style template is the "Enterprise Customized Drawing Standard." Data comparison shows differences between the two templates in layer naming and annotation leader styles. It is understandable that the annotation content is directly extracted from the component attributes of the 3D BIM model corresponding to the final collaborative design scheme. For example, the wall annotation automatically displays its material, thickness and fire resistance information.

[0034] In implementation, the final collaborative design scheme, its corresponding optimization process data, constraint resolution records, and performance simulation results are packaged into a standardized data package. This standardized data package is organized using a unified directory structure and metadata description file. The final collaborative design scheme is stored in IFC format; the optimization process data is recorded in JSON format, showing the design variables and objective function values ​​of each generation of the population during optimization iterations; the constraint resolution records are recorded in log file format, detailing the entire process of parameter cluster conflict identification and relaxation adjustment; and the performance simulation results are stored in structured CSV format, containing a detailed list of indicators such as cost, displacement, and energy consumption. The standardized data package is stored in the project collaboration database, a version-controlled distributed database that stores the hash value, timestamp, and uploader information of the data package. Optionally, the standardized data package can be losslessly compressed before storage to reduce storage space usage. In the example comparison, ZIP compression and lossless database encoding are used; the latter is superior in random access speed.

[0035] Based on standardized data packages in the project collaboration database, a version management and impact propagation analysis process for design changes is initiated within the BIM design platform. The version management process is automatically triggered whenever a user initiates any modification to the final collaborative design scheme. The system creates a new data version branch in the project collaboration database and records the change content, the voxel set associated with the change, and the change initiator. The impact propagation analysis process is initiated after version submission. The analysis process reads the parameter association network model and constraint resolution records stored in the standardized data package. When the voxel attributes associated with the design change intersect with the voxels involved in a historical constraint resolution record recorded in the standardized data package, the change is marked as a "potential propagation change" that may affect previously resolved conflicts. The impact propagation analysis process assesses the scope of impact of the "potential propagation change," determining the set of potentially affected parameters based on the topological connections of the changed parameters in the parameter association network model. In some embodiments, the results of the impact propagation analysis are presented as a visual report listing the affected disciplines, components, and performance indicators that may require re-verification. In another example, the results are pushed to relevant design personnel via message notification. The version management and impact propagation analysis process for design changes continues until subsequent design changes and the established collaboration scheme are consistent with all recorded constraints. Consistency checks are completed by comparing the current model state with the constraints within the standardized data packets recorded in the project collaboration database.

[0036] See Figure 5 In the collaborative design space of electromechanical systems (ventilation ducts and cable trays), the spatial layout and constraint relationships of various professional components can be analyzed through a structured design space and parameter association network model. Specifically, the supply air duct (blue) and return air duct (orange), as the core carriers of the ventilation system, have their spatial orientation and connections with diffusers and return air vents constituting the topological constraints of ventilation parameters. The connection relationships between cable trays (green dashed lines), distribution boxes (yellow), and sockets define the power supply path and load constraints of the electrical system. The spatial conflict risk between the two systems can be quantified through the parameter association network model: the spatial occupancy range of the supply air ducts, return air ducts, and terminal equipment of the ventilation system is filtered, and the boundary voxel set of each component in three-dimensional space is recorded; the same operation is performed on the cable trays, distribution boxes, and sockets of the electrical system. The degree of spatial overlap between the two systems is measured by the voxel intersection ratio, and this ratio is added as a spatial conflict constraint term to the multi-professional constraint consistency evolution calculation. During parameter configuration, the spatial occupancy filtering resolution is set to 100mm×100mm×100mm, and the component safety distance threshold is 300mm.

[0037] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A construction engineering digital design method based on BIM and data collaboration, characterized in that, The method includes: Obtain the initial design model set of the target construction project and the associated cross-disciplinary design parameter set, wherein the cross-disciplinary design parameter set includes design constraints of multiple disciplines such as structure, mechanical and electrical engineering, and architecture; Perform three-dimensional spatial meshing on the initial design model set to generate a structured design space containing geometric voxels and attribute metadata; In the structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set to form a parameter association network model that describes the dependency and conflict relationships between design parameters; Based on the parameter association network model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that satisfies the constraints of all cross-disciplinary design parameter sets. The computer-aided design optimization engine is invoked to perform global optimization within the collaborative design optimization space, generate the final collaborative design scheme, and feed the final collaborative design scheme back to the BIM design platform.

2. The construction engineering digital design method based on BIM and data collaboration according to claim 1, characterized in that, The initial design model set is subjected to three-dimensional spatial meshing to generate a structured design space containing geometric voxels and attribute metadata, including: Boundary representation analysis is performed on each 3D model in the initial design model set to extract the geometric topology of the model surface; Based on a preset mesh resolution threshold, the geometric surface of each 3D model is adaptively divided into triangular facet meshes to generate a uniform model surface mesh. Based on the surface mesh of the model, a tetrahedral mesh generation algorithm is used to fill the interior of the model with a volume mesh to form a complete voxel representation of the model. From the associated information of the initial design model set, extract the professional type, designer, timestamp and physical attributes corresponding to each geometric voxel, and bind them to the corresponding voxel as attribute metadata; All voxels that have completed attribute binding are organized according to three-dimensional spatial coordinates to construct the structured design space containing hierarchical spatial indexes.

3. The construction engineering digital design method based on BIM and data collaboration according to claim 2, characterized in that, Within the structured design space, a parameter semantic association network is constructed for the cross-disciplinary design parameter set, forming a parameter association network model describing the dependencies and conflicts among design parameters, including: Each design constraint in the cross-disciplinary design parameter set is analyzed and transformed into a standardized parameter item containing parameter name, value range, professional source, and constraint type. In the structured design space, locate the set of geometric voxels associated with each standardized parameter item and establish the mapping relationship between parameter items and spatial voxels; Analyze whether there is an intersection between the sets of associated voxels of different standardized parameter items in the structured design space. If there is an intersection, establish potential association edges between the corresponding parameter items. The semantic relevance between parameter items with potential associated edges is calculated. The semantic relevance is derived based on the similarity of the terminology of the parameter names, the logical compatibility of the constraint types, and the spatial overlap of the associated voxels. Using standardized parameter items as nodes and the semantic relevance as edge weights, a parameter association network model is constructed to describe the dependency, transmission, and conflict relationships among all design parameters.

4. The construction engineering digital design method based on BIM and data collaboration according to claim 3, characterized in that, Based on the parameter association network model, a multi-disciplinary constraint consistency evolution calculation is performed on the structured design space to generate a collaborative design optimization space that satisfies the constraints of all cross-disciplinary design parameter sets, including: Traverse the parameter association network model to identify all parameter clusters that have direct or indirect associations; For each parameter cluster, check whether there are logical conflicts in the design constraints of the parameter items within the cluster. The logical conflicts include contradictory numerical ranges, overlapping space occupancy, and incompatible physical properties. For a cluster of parameter items with logical conflicts, a constraint resolution calculation is initiated. The constraint resolution calculation includes: sorting the conflicting constraints according to a preset professional priority rule; and using a constraint relaxation algorithm to adjust the constraint range of non-high priority parameter items until all constraints in the cluster reach a satisfyable state. After the constraint resolution calculation is completed, the conflict-free parameter cluster is remapped back to the structured design space with adjusted constraints, and the attribute metadata of the corresponding geometric voxels is updated. The updated structured design space is marked as the collaborative design optimization space that meets the consistency requirements of all cross-disciplinary design parameter sets.

5. The construction engineering digital design method based on BIM and data collaboration according to claim 4, characterized in that, For clusters of parameter terms with logical conflicts, the constraint resolution calculation is initiated, including: Extract all design constraints that constitute the conflict from the cluster of logically conflicting parameter items; Query the preset professional knowledge base to obtain the professional importance level, design code enforcement level, and modification cost assessment corresponding to each design constraint; Based on the aforementioned professional importance level, design specification enforcement level, and modification cost assessment, calculate the priority weight of each design constraint in conflict resolution; Conflicting design constraints are sorted in descending order according to their priority weights, and the constraint with the highest weight is retained as the baseline constraint. For constraints with weights lower than the baseline constraint, the constraint relaxation algorithm is invoked to adjust the boundaries within their original value range. The goal of this adjustment is to eliminate conflicts with higher-weight constraints while minimizing the adjustment magnitude. The constraint relaxation and conflict detection are performed iteratively until all constraints in the parameter cluster can be satisfied simultaneously, or the preset maximum number of iterations is reached.

6. The construction engineering digital design method based on BIM and data collaboration according to claim 5, characterized in that, The step of calling the computer-aided design optimization engine to perform global optimization within the collaborative design optimization space and generate the final collaborative design scheme includes: Within the collaborative design optimization space, a multi-objective optimization function is defined. The design variables of the multi-objective optimization function are the adjustable properties of geometric voxels, and the optimization objectives include construction cost estimation, structural performance indicators, space utilization efficiency, and energy consumption simulation indicators. Using the intelligent optimization algorithm in the computer-aided design optimization engine, an initial design scheme population is generated within the range of design variables allowed by the collaborative design optimization space; For each design scheme in the population, a rapid performance simulation is performed on the BIM design platform to calculate its corresponding multi-objective optimization function value; Based on the multi-objective optimization function value, the population of the design scheme is iteratively evolved through selection, crossover, and mutation operations to guide the population to converge toward the Pareto optimal frontier. When the convergence condition or the maximum number of generations is reached, one or more non-dominated solutions are selected from the final population as candidate cooperative design schemes.

7. The construction engineering digital design method based on BIM and data collaboration according to claim 6, characterized in that, For each design scheme in the population, a rapid performance simulation is performed on the BIM design platform to calculate its corresponding multi-objective optimization function value, including: The design scheme is converted into an intermediate data format that can be recognized by the BIM design platform, including adjusted geometric and attribute information; The cost estimation module integrated into the BIM design platform is invoked to automatically calculate the estimated construction cost based on the geometric and material property information of the design scheme. The structural analysis module integrated into the BIM design platform is invoked to perform simplified mechanical analysis on the design scheme and extract key structural performance indicators. Use the space analysis tools integrated into the BIM design platform to calculate the space utilization efficiency-related indicators of the design scheme. The energy consumption simulation engine integrated into the BIM design platform is invoked to perform simplified energy consumption simulation based on the three-dimensional model of the design scheme and the properties of the building envelope, thereby obtaining energy consumption simulation indicators. The calculated estimated construction cost, structural performance indicators, space utilization efficiency indicators, and energy consumption simulation indicators are combined into the comprehensive objective function value of the design scheme according to the weights or aggregation methods defined in the multi-objective optimization function.

8. The construction engineering digital design method based on BIM and data collaboration according to claim 7, characterized in that, Based on the multi-objective optimization function value, the population of design schemes is iteratively evolved through selection, crossover, and mutation operations, including: Based on the comprehensive objective function value of each design scheme, a roulette wheel selection method is used to select the parent design scheme from the current population with a probability proportional to the quality of the objective function value. The selected parent design schemes are paired up, and under the constraints of the collaborative design optimization space, the adjustable attribute values ​​of some geometric elements in the paired schemes are exchanged to generate the child design schemes. For the generated offspring design scheme, the adjustable attribute values ​​of some of its geometric voxels are randomly changed with a preset low probability. The change must ensure that the new attribute values ​​are still within the allowable range defined in the collaborative design optimization space. The offspring design schemes generated through selection, crossover, and mutation operations are merged with some elite parent design schemes to form a new generation of design scheme populations. Calculate the combined objective function value of all design schemes in the new generation population and use it for the selection operation in the next iteration.

9. The construction engineering digital design method based on BIM and data collaboration according to claim 8, characterized in that, When the convergence condition or the maximum number of generations is reached, one or more non-dominated solutions are selected from the final population as candidate cooperative design schemes, including: In the final design scheme population, perform non-dominated sorting to find all non-dominated solutions that are not dominated by any other design scheme, and form the Pareto optimal solution set. For each nondominated solution in the Pareto optimal solution set, calculate its performance value on each optimization objective, and generate the corresponding design snapshot and key performance report; Receive selection instructions from the user from the Pareto optimal solution set, or automatically select an optimal solution according to preset decision rules; The optimal solution, selected by the user or automatically, is reconstructed in the BIM design platform as a complete and editable 3D BIM model, which serves as the final collaborative design solution.

10. The construction engineering digital design method based on BIM and data collaboration according to claim 9, characterized in that, After feeding the final collaborative design scheme back to the BIM design platform, the process also includes: In the BIM design platform, the final collaborative design scheme is automatically generated and annotated with design drawings and documents from all disciplines. The final collaborative design scheme, its corresponding optimization process data, constraint resolution records, and performance simulation results are packaged into a standardized data package and stored in the project collaborative database. Based on standardized data packages in the project collaboration database, the version management and impact propagation analysis process for design changes is initiated in the BIM design platform to ensure that subsequent design changes are consistent with the established collaboration plan.

Images