A BIM-based multi-specialty collaboration method and system

Abstract

The application relates to a BIM-based multi-specialty collaboration method and system, which comprises the following steps: generating a target building model; performing a conflict detection process and a compliance checking process in parallel, and generating conflict guidance information and a checking report; generating a briefing task based on the conflict guidance information and the checking report, and delivering the briefing task to a participating user end; sending the target building model and its associated relevant data set to a construction management end, and obtaining construction data; comparing and analyzing the construction data and the target building model in real time, and updating the target building model when the deviation exceeds a deviation threshold or a design change instruction is received; and in summary, the application has the effects of reducing the rework rate, improving the design efficiency, and improving the whole life cycle management efficiency by performing parameterized modeling, performing conflict detection and compliance checking, generating a briefing task based on the detection and checking results, and comparing the construction data in real time and triggering model updating.

Description

Technical Field

[0001] This application relates to the technical field of building information modeling, and in particular to a multi-disciplinary collaborative method and system based on BIM. Background Technology

[0002] In the fields of architecture, engineering, and construction, multidisciplinary collaborative design based on Building Information Modeling (BIM) has become an important way to improve project quality and efficiency. Currently, existing collaborative workflows typically rely on a unified model platform where designers from different disciplines perform sequential or parallel designs. However, this model reveals its inherent shortcomings when dealing with large and complex projects, making it difficult to adapt to the actual needs of the projects.

[0003] First, existing technologies mostly focus on the later integration and collision detection of model results. Their collaboration often begins after various disciplines have formed relatively mature preliminary design schemes. However, this post-verification mechanism may fail to detect potential conflicts and compliance issues in the design process in the early stages, resulting in a huge amount of rework, which seriously slows down the project progress and increases costs. Second, although some existing models have achieved parametric modeling, key links such as model generation, conflict detection, and specification verification are usually still isolated from each other and lack a unified data-driven architecture. This may make it difficult for design rules to be effectively embedded and transmitted in the early stages of model building, thus making it difficult to ensure semantic consistency between models and specifications, and between models, and making it difficult to achieve correct and efficient interoperability of design data.

[0004] Furthermore, the data transfer process from design to construction in existing technologies is fragmented. Specifically, after the confirmed design model is transferred to the construction phase, the actual data generated on-site is usually difficult to be fed back to the design end in real time, making it difficult to drive timely updates of the model. When the on-site situation changes or design deviations occur, the entire collaborative process often needs to be restarted manually, which restricts the further improvement of the efficiency of the entire chain management. Summary of the Invention

[0005] To address the aforementioned shortcomings, this application provides a BIM-based multidisciplinary collaboration method and system.

[0006] The above-mentioned objective of this application is achieved through the following technical solution: A BIM-based multidisciplinary collaboration method includes the following steps: In response to the received project plan information, the project task data and planning condition data are extracted, and the corresponding parametric modeling is performed through a pre-built collaborative architecture to generate the target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. During parametric modeling, conflict detection and compliance verification processes are executed in parallel, and conflict guidance information and verification reports are generated based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. Based on the conflict guidance information and verification report, several handover tasks are generated on the cloud collaboration platform associated with the collaborative architecture and distributed to the corresponding participating user terminals; In response to the handover confirmation information from the participating user, a relevant data set is associated with the target building model. The relevant data set includes conflict guidance information, verification reports, and handover confirmation information. Send the target building model and its associated data set to the construction management terminal, and obtain construction data in real time through the construction management terminal; The system compares and analyzes construction data with the target building model in real time. When a deviation is detected that exceeds the preset deviation threshold or a design change instruction is received, the system triggers a preset update strategy to update the target building model through a collaborative architecture.

[0007] The second objective of this invention is achieved through the following technical solution: A BIM-based multidisciplinary collaborative system includes: The model generation module is used to respond to the received project plan information, extract project task data and planning condition data, and perform corresponding parametric modeling through a pre-built collaborative architecture to generate a target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. The verification and inspection module is used to execute the conflict detection process and the compliance inspection process in parallel during the parametric modeling process, and generate conflict guidance information and inspection reports respectively based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. The task generation module is used to generate several handover tasks on the cloud collaboration platform associated with the collaboration architecture based on conflict guidance information and verification reports, and then distribute them to the corresponding participating user terminals. The data association module is used to associate relevant data sets for the target building model in response to the handover confirmation information from the participating user terminal. The relevant data sets include conflict guidance information, verification reports and handover confirmation information. The construction monitoring module is used to send the target building model and its associated data set to the construction management terminal, and to obtain construction data in real time through the construction management terminal; The model update module is used to compare and analyze the construction data with the target building model in real time. When the deviation is detected to exceed the preset deviation threshold or a design change instruction is received, the preset update strategy is triggered through the collaborative architecture to update the target building model.

[0008] In summary, the BIM-based multi-disciplinary collaborative method and system provided in this application solves the problems of large-scale rework, data isolation, and data fragmentation caused by post-verification in existing technologies by performing parametric modeling and concurrently executing conflict detection and compliance verification during the modeling process. It generates handover tasks based on the detection and verification results and associates them with data sets, compares construction data in real time, and triggers model updates. It has the effects of reducing rework rate, improving design efficiency and data consistency, and enhancing the efficiency of full life cycle management. Attached Figure Description

[0009] Figure 1 This is a flowchart of an embodiment of a multi-disciplinary collaboration method based on BIM according to this application; Figure 2 This is a schematic diagram of an implementation of a target building model in an embodiment of a BIM-based multi-disciplinary collaborative method of this application; Figure 3 This is a schematic diagram illustrating the implementation of rule mapping in an embodiment of a BIM-based multi-disciplinary collaboration method of this application; Figure 4 This is a schematic diagram illustrating a conflict detection implementation in an embodiment of a BIM-based multi-disciplinary collaborative method of this application. Detailed Implementation

[0010] The following is in conjunction with the appendix Figures 1-4 This application will be described in further detail.

[0011] In one embodiment, this application discloses a BIM-based multi-disciplinary collaboration method, such as... Figures 1-4 As shown, the specific steps include the following: S10: In response to the received project plan information, extract project task data and planning condition data, and perform corresponding parametric modeling through a pre-built collaborative architecture to generate a target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. In this embodiment, BIM refers to Building Information Modeling, specifically a digital representation that integrates information throughout the entire lifecycle of a building project. By creating and managing multi-dimensional data models that include geometry, spatial relationships, geographic information, and component attributes, it provides a unified data foundation and visualization platform for building design, construction, operation, and maintenance. Multi-disciplinary collaboration refers to the process of collaboration among multiple professional teams, such as architecture, structure, and mechanical and electrical engineering, through information sharing, process coordination, and conflict resolution mechanisms during the design, construction, and management of a building project.

[0012] In this embodiment, project scheme information refers to the input data generated in the initial stage of the project, typically including architectural design schemes, functional requirements, technical indicators, planning conditions, etc.; project task data refers to data extracted from the project scheme information to clarify the division of labor and modeling tasks of each discipline, such as the modeling scope, accuracy requirements, and delivery standards of architecture, structure, MEP, etc.; planning condition data refers to data in the project scheme information related to external planning requirements, such as land use restrictions, plot ratio, building height restrictions, fire separation requirements, etc., used to ensure that the model conforms to external regulatory constraints in parametric modeling and compliance verification; collaborative architecture refers to the framework used to support multi-disciplinary collaboration in BIM. This collaborative architecture can typically be deployed on a server or cloud environment and interacts with various professional design software, databases, and user terminals. The collaborative architecture includes a parameter configuration library, a component relationship library, and process control scripts. The parameter configuration library is a component of the collaborative architecture used to store and manage various design parameters, specification clauses, standard component attributes, and verification rules, etc. The stored parameters and rules form the basis for parametric modeling, conflict detection, and compliance verification. The component relationship library, another component of the collaborative architecture, defines and stores the logical relationships, connection methods, construction dependencies, and functional constraints between building components. This library enables automatic adjustment of related components during design changes to maintain the integrity and correctness of the design model. The process control script, another component of the collaborative architecture, orchestrates and automates the execution of various collaborative tasks and processes. Based on preset logic and conditions, the process control script drives the parametric modeling engine, calls verification algorithms, and generates task instructions, thereby achieving automated and intelligent management of collaborative processes. Parametric modeling refers to the technical process of creating and modifying models by defining parameters and rules. In parametric modeling, the model's geometry and attributes are no longer fixed but driven by adjustable parameters and their relationships. When adjustable parameters change, the constructed model automatically updates to reflect these changes, thereby improving design flexibility and efficiency.

[0013] Specifically, in response to received project plan information, project task data and planning condition data are extracted. In one implementation, the project plan information can be received in unstructured document form, such as PDF or Word documents. Project task data, such as project name, schedule requirements, and budget range, as well as planning condition data, such as building height restrictions, floor area ratio requirements, and greening rate indicators, are identified and extracted from the document through text keyword matching. Corresponding parametric modeling is then executed through a pre-built collaborative architecture to generate the target building model. This collaborative architecture includes a parameter configuration library, a component relationship library, and process control scripts. In one implementation, the collaborative architecture can provide a basic parametric modeling environment where parameters converted from planning condition data are manually input. Components are then selected and assembled to construct the target building model based on predefined component types and connection rules in a component association library. Furthermore, the process control script is primarily used to record modeling operation logs and task sequence scheduling. For example, when generating a residential building model, designers can manually input parameters such as the number of floors, floor height, and unit area, and select components such as walls, floors, doors, and windows from the component association library. They can then adjust the component positions and connection relationships to form a preliminary building model.

[0014] S20: During the parametric modeling process, the conflict detection process and the compliance verification process are executed in parallel, and conflict guidance information and verification reports are generated respectively based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. In this embodiment, the conflict detection process refers to the identification, analysis, and verification of geometric conflicts, logical conflicts, or attribute mismatches among components in the target building model during parametric modeling. This aims to identify and resolve potential design problems early on, avoiding rework later. The conflict detection process includes verification of the connection attributes of model components, spatial layout relationships, and engineering logical sequence. The compliance verification process refers to the automatic review and verification of whether the target building model conforms to relevant building codes, standards, planning conditions, and project requirements, aiming to ensure the legality, safety, and feasibility of the design scheme.

[0015] Furthermore, the connection attribute verification of model components refers to verifying whether components from different disciplines have the correct technical conditions for connection at the interface. The verification content specifically includes technical parameter matching, logical relationship correctness, and system connectivity. Spatial layout relationship verification, often referred to as collision detection, refers to verifying whether components from different disciplines have physical overlap or insufficient spacing in three-dimensional space. The verification content specifically includes hard collisions and soft collisions. Hard collisions refer to two or more components directly intersecting in space, such as ducts passing through structural beams or pipes and cable trays crossing each other in a suspended ceiling. Soft collisions refer to components not being directly connected. The pipes are connected, but the reserved space for installation, operation, or maintenance is insufficient. For example, the distance between the pipes and the wall is too close, making it impossible to install the insulation layer, or there is not enough maintenance passage around the equipment. The engineering logic sequence verification refers to verifying whether the time sequence of component installation and the construction logic are reasonable. The verification content specifically includes the installation sequence, workspace conflicts, and constructability verification. Among them, workspace conflict refers to analyzing whether there are conflicts in the workspace required by different trades within a specific construction period. For example, civil engineering wall construction and electromechanical pipe laying may need to be carried out in the same area and at the same time, which will cause workspace conflicts and require adjustment of the construction plan.

[0016] Specifically, during parametric modeling, conflict detection and compliance verification processes are executed in parallel. In one implementation, after completing one stage of the model's work, the conflict detection and compliance verification tools are triggered. These two tools run in independent computation threads and analyze the current model separately. For example, after completing the structural frame modeling, structural collision detection and structural code verification are initiated simultaneously to check for overlapping components or non-compliance with structural load codes. Based on the execution results, conflict guidance information and verification reports are generated respectively. In another implementation, the results of the conflict detection process can be achieved by generating a list detailing all detected geometric collision points, including the IDs and approximate locations of the colliding components. The results of the compliance verification process can be achieved by generating a text report indicating the clause numbers in the model that do not comply with specific building codes. A brief description is provided; specifically, the conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. For connection attribute verification, a preset rule table can be used to compare whether the interface types of adjacent components match, for example, checking whether the pipe interface type is consistent with the valve interface type. For spatial layout relationship verification, a bounding box collision detection algorithm can be used to identify whether there is hard geometric overlap between components in the model. For engineering logic order verification, a preset linear construction process can be used to check whether the installation sequence of model components conforms to these linear dependencies. For example, when checking an electromechanical pipeline model, connection attribute verification can find errors in the connection of pipes of different diameters, spatial layout relationship verification can find hard collisions between pipes and structural beams, and engineering logic order verification can find logical errors such as certain equipment being placed before the brackets are installed.

[0017] S30: Based on the conflict guidance information and verification report, generate several handover tasks on the cloud collaboration platform associated with the collaborative architecture and distribute them to the corresponding participating user terminals; In this embodiment, the cloud-based collaboration platform refers to an online collaborative environment platform built on cloud computing technology. This cloud-based collaboration platform provides project participants with a unified interface for data storage, model access, task management, and communication. It supports real-time collaborative work among multiple users and locations, and can interact with and integrate functions with the collaborative architecture. The briefing task refers to a task item created on the cloud-based collaboration platform to address conflict or compliance issues. It is used to clarify the location of the problem, the responsible professional, and the solution requirements. The briefing task is distributed to the participating user terminals through the cloud-based collaboration platform to ensure that the problem is traceable and can be managed in a closed loop. The participating user terminals refer to the terminals of different professional teams participating in the construction project, such as the architectural design terminal, structural engineering terminal, mechanical and electrical engineering terminal, construction management terminal, etc.

[0018] For example, the cloud-based collaborative platform in this embodiment can adopt a B / S architecture, support multi-terminal access (PC, mobile, etc.), and its communication protocol adopts HTTP / HTTPS+WebSocket, which can realize: online model browsing and annotation, real-time task push and status synchronization, hierarchical permission management (e.g., design, construction, management, and review), encrypted data storage, and operation log recording.

[0019] It should be noted that the architecture and implementation of the cloud-based collaborative platform can be determined and reproduced by technical personnel in the corresponding technical fields based on existing common knowledge data, historical experience, and actual scenarios, and need not be elaborated here.

[0020] Specifically, based on conflict guidance information and verification reports, several handover tasks are generated on the cloud-based collaborative platform associated with the collaborative architecture and distributed to the corresponding participating user terminals. In one implementation, handover tasks are created on the cloud-based collaborative platform according to conflict guidance information and verification reports. Each task needs to be filled with task content, specify the responsible participating terminal and the deadline, and upload the relevant conflict guidance information or verification report as an attachment. For example, if the verification report points out that the window size in a certain area does not conform to a certain specification, a task can be created and assigned to the architectural designer participating terminal, requiring them to modify the window size.

[0021] S40: In response to the handover confirmation information from the participating user terminal, associate relevant data sets for the target building model, the relevant data sets including conflict guidance information, verification reports and handover confirmation information; In this embodiment, the disclosure confirmation information refers to the feedback confirmation information generated by each participating user terminal after the disclosure task is sent to the participating user terminal through the cloud collaboration platform; the relevant data set refers to the collection of all process data and result data associated with the target building model, wherein the relevant data set includes conflict guidance information, verification report and disclosure confirmation information.

[0022] Specifically, in response to the handover confirmation information from the participating user, a relevant data set is associated with the target building model. This relevant data set includes conflict guidance information, verification reports, and handover confirmation information. In one implementation, after the participating user completes the handover task and submits the confirmation information, the confirmation information is stored as a text record, along with links to the conflict guidance information and verification reports, in a database associated with the target building model. This association allows the user to query its historical verification and confirmation records through the model.

[0023] S50: Send the target building model and its associated data set to the construction management terminal, and obtain construction data in real time through the construction management terminal; In this embodiment, the construction management end refers to the client software or system platform of the construction party during the project construction phase. The construction party, such as the general contractor, project manager, and site engineer, is the core participant in data interaction and collaboration between the construction and design phases. Construction data refers to various relevant data generated during the project construction process that reflects the actual site conditions, such as geometric data, progress data, and quality data.

[0024] Specifically, the target building model and its associated data set are sent to the construction management terminal, and construction data is obtained in real time through the construction management terminal. In one implementation, the target building model and its associated data set can be pushed or pulled to the construction management terminal periodically via file transfer protocol or shared network drive. The construction management terminal then records data such as construction progress, material consumption, and equipment installation location in real time through manual input by on-site personnel or through intelligent system, and uploads it to the construction management system. For example, the construction management terminal can receive the updated structural model, while on-site workers can input the area and progress of concrete pouring completed that day through tablet computer.

[0025] S60: Real-time comparison and analysis of construction data and target building model. When the deviation exceeds the preset deviation threshold or a design change instruction is received, the target building model is updated by triggering a preset update strategy through the collaborative architecture.

[0026] In this embodiment, the preset deviation threshold refers to the maximum allowable difference between the construction data and the target building model, which is used to determine whether action needs to be taken, i.e., the preset update strategy; the design change instruction refers to the instruction that requires modification of the target building model, which is another important triggering condition for initiating model updates in this embodiment; the preset update strategy refers to the pre-configured rules and logic that indicate how to update the target building model, which will be automatically executed when the deviation exceeds the limit or a design change instruction is received.

[0027] For example, in this embodiment, the preset update strategy may include local parameter update, associated linkage update, and global iterative update; wherein, local parameter update refers to modifying only the size, position, attributes, etc. of the deviation component; associated linkage update refers to automatically updating upstream and downstream components according to the component association relationship library; global iterative update refers to regenerating modeling instructions and performing a full model re-examination when there are major changes.

[0028] Specifically, the construction data is compared and analyzed with the target building model in real time. When a deviation is detected that exceeds a preset deviation threshold or a design change instruction is received, the target building model is updated through a preset update strategy triggered by the collaborative architecture. In one implementation, the construction management end compares the manually entered construction data with the target building model in terms of geometric dimensions and position. If the comparison results show that the actual size of a component differs from the model size by more than a fixed percentage threshold, or its spatial position offset exceeds a fixed distance threshold, an alarm is issued. At the same time, if a formal design change instruction is received from the design department, the collaborative architecture will modify the affected components or parameters in the target building model according to the preset update process.

[0029] For example, Figure 2 This is a schematic diagram of an implementation of the target building model in this application embodiment. The left side is a target project BIM model containing multiple building structures and bridge components, which can intuitively present the spatial component relationships of the project. The right side shows the implementation steps, including: first, based on the project plan and planning conditions, generating the target BIM model through a collaborative architecture-driven modeling engine; simultaneously conducting parallel conflict detection during the modeling process to verify component connection attributes, spatial layout, and engineering logic; conducting compliance verification according to the standard provisions and generating a verification report; intelligently generating and issuing handover tasks based on the verification results to achieve online collaboration among multiple roles; collecting construction site data in real time and comparing it with the target BIM model, triggering model iterative optimization when deviations exceed limits or change instructions are received, forming a closed-loop management process from design to construction.

[0030] By adopting the above technical solution, this embodiment addresses the problems of late collaborative start and emphasis on late-stage integration conflict detection and compliance verification in existing technologies. By executing the conflict detection process and compliance verification process in parallel during the parametric modeling process, it achieves early detection and discovery of problems, thereby avoiding a large amount of rework and modification work in the later stages. This has the effect of shortening the project cycle and reducing costs.

[0031] Secondly, in response to the problems of isolation and lack of unified data-driven architecture in the model generation, conflict detection, and specification verification stages in the existing technology, this embodiment achieves integration and data-driven operation of each key stage through a pre-built collaborative architecture. This ensures that design rules are effectively embedded and transmitted in the early stage of model building, guarantees semantic consistency between the model and the specification, and between models, and overcomes the limitations of data isolation and inefficient interoperability in the existing technology.

[0032] Furthermore, in response to the problem of data transmission gaps from design to construction in existing technologies, and the difficulty in providing real-time feedback of construction site data to drive timely model updates, this embodiment achieves dynamic synchronization between the design model and the construction site through real-time comparison analysis and automated update triggering. This improves the efficiency of the entire chain of management and effectively avoids construction errors and resource waste caused by information lag.

[0033] In one embodiment, step S10 includes: S11: Perform semantic parsing on project plan information to extract planning condition data, and convert the planning condition data into standardized parameters and constraint rules through the parameter configuration library; In this embodiment, step S11 refers to performing in-depth understanding and structuring processing on unstructured or semi-structured project plan information to identify and extract data related to architectural planning. Planning condition data forms the basis for building model generation, such as building height restrictions, floor area ratio, setback requirements, and functional zoning. The parameter configuration library is a knowledge base storing predefined parameters, rules, and transformation logic. It unifies diverse planning condition data into a standardized format that the system can recognize and process. This process can employ natural language processing techniques, such as rule-based pattern matching or machine learning models, to analyze text-based project plan information, identify planning condition data, and then match and transform the extracted data with standardized parameters in the parameter configuration library using a pre-defined mapping table or transformation function. Alternatively, project plan information can be received through a structured data interface, and planning condition data can be directly extracted from the structured data using a data parser. Furthermore, the parameter configuration library can be a database or a collection of configuration files, defining the standardized names, data types, value ranges, and correspondences with modeling parameters for various planning conditions.

[0034] For example, the parameter configuration library in this embodiment is a structured relational database, which stores the following: a cross-professional interface standard table: component ID, professional type, physical specification, connection method, performance parameter, tolerance range; a specification clause mapping table: specification number, clause type, constraint parameter, threshold, applicable scenario; a modeling constraint rule table: size constraint, position constraint, material constraint, fire protection index, sound insulation index, energy saving index; and a deviation threshold table: geometric dimension deviation threshold, spatial position deviation threshold, and attribute parameter deviation threshold.

[0035] Furthermore, the parameter configuration library in this embodiment supports semantic retrieval and rule matching, which can be directly called for conflict detection, compliance verification, and model updates.

[0036] It should be noted that the content stored in the parameter configuration library can be determined by technical personnel in the corresponding technical field based on existing common knowledge data, historical experience, and actual scenarios, which will not be elaborated here.

[0037] S12: Generate a sequence of executable modeling task instructions for process control scripts based on standardized parameters and constraint rules; In this embodiment, step S12 refers to transforming abstract planning conditions into a set of specific instructions for executable and ordered modeling steps. Standardized parameters and constraint rules are the basis for generating the modeling task instruction sequence, ensuring that the generated model meets the planning requirements. The modeling task instruction sequence can be dynamically generated by a template engine or rule engine based on standardized parameters and constraint rules, such as Python scripts, Grasshopper definition files, Dynamo scripts, etc. These generated script codes contain modeling instructions such as creating geometry, setting attributes, and defining relationships. Alternatively, standardized parameters and constraint rules can be mapped to specific modeling functions or API calls through a predefined modeling operation library, and then these function calls can be combined according to a preset logical order to form an executable modeling task instruction sequence.

[0038] S13: The process control script executes the sequence of modeling task instructions, driving the corresponding parametric modeling engine to generate the target building model by combining the predefined logical associations in the component association library.

[0039] In this embodiment, step S13 refers to the process control script acting as a program that executes the modeling task instruction sequence, responsible for scheduling and managing the modeling process. The parametric modeling engine is the core software that actually performs the modeling operations, dynamically creating and modifying building components based on the modeling task instruction sequence and the logic in the component relationship library. Furthermore, the component relationship library stores predefined relationships between building components, such as geometry, function, and topology, ensuring the consistency and integrity of the model. The process control script can be a main control program that parses and executes each instruction in the modeling task instruction sequence, and the parametric modeling engine can be RevitA... PI, Dynamo, and other scripts create components such as walls, floors, doors, and windows by calling the interface of the parametric modeling engine, and automatically adjust the relationships between components according to the rules defined in the component relationship library; or, the process control script can also be a state machine or workflow engine, which drives the parametric modeling engine according to the progress and results of the modeling task instruction sequence; the component relationship library can be a graph database or a relational database, which stores component types, attributes, connection points, and their dependencies and constraints. When the parametric modeling engine creates a component, it queries the component relationship library to ensure that the generated component conforms to the preset logic and specifications.

[0040] For example, the component association relationship library in this embodiment is stored in a graph database, which includes: component topological relationships: parent-child relationship, dependency relationship, adjacency relationship, collision constraint relationship; construction logic dependency: installation sequence, process dependency, work surface conflict rule; change linkage rule: when a component is modified, the list of associated components that are automatically linked and updated and the update priority.

[0041] For example, the process control script in this embodiment is an executable automation script, such as a Python script or a C# script, which can achieve: parsing and scheduling of modeling task instructions; parallel triggering and result summarization of conflict detection and compliance verification; closed loop of task generation, allocation, issuance, and confirmation of briefing tasks; comparison of construction data, judgment of deviations, and triggering of model updates; automatic re-examination of the updated model and push of results.

[0042] Furthermore, in this embodiment, the process control script communicates with the parametric modeling engine, the cloud collaboration platform, and the construction management terminal via API interfaces. At the same time, when modeling and updating the component association library, the process control script can automatically read the component association library to ensure the consistency of the model logic.

[0043] It should be noted that the specific implementation methods of the content stored in the component association library and the process control script can be determined and reproduced by technical personnel in the corresponding technical field based on existing common knowledge data, historical experience and actual scenarios, and need not be elaborated here.

[0044] Specifically, the solution in this application performs semantic parsing on project plan information, extracts planning condition data, and uses a parameter configuration library to convert it into standardized parameters and constraint rules. Based on these standardized parameters and constraint rules, an executable modeling task instruction sequence is generated for the process control script. Subsequently, the process control script executes the modeling task instruction sequence, driving the parametric modeling engine to combine with predefined logical associations in the component association library, thereby efficiently and accurately generating the target building model.

[0045] Through the above technical solution, this application can effectively transform unstructured or semi-structured project requirements into standardized inputs that can be understood and processed by computers. On this basis, it generates a sequence of executable modeling task instructions based on standardized parameters and constraint rules, and drives a parametric modeling engine in conjunction with a component relationship library to generate the target building model. This has the effect of improving the automation and accuracy of BIM model generation from project plan to BIM model. It can not only reduce manual intervention and potential errors, but also ensure that the model strictly follows various planning conditions and design specifications in the initial stage, thereby improving the efficiency of multi-disciplinary collaboration and the quality of model construction.

[0046] In one embodiment, step S20 includes: S21: Extract the interface attribute information of the target component, perform semantic matching between the interface attribute information and the pre-set cross-professional interface standards in the parameter configuration library, and generate the first verification information based on the semantic matching result. The interface attribute information includes physical specifications, connection method and performance parameters. In this embodiment, the interface attribute information of the target component is extracted to obtain the key connection features of each component in the BIM model. Interface attribute information describes the characteristics of a component when it connects or interacts with other components. Its purpose is to provide a data foundation for subsequent cross-disciplinary interface standard matching. This can be achieved by automatically extracting attribute values ​​of predefined fields by parsing the component's metadata in the BIM model file; or by manually or semi-automatically annotating the component's interface attributes during the modeling process using the user terminal. Interface attribute information includes physical specifications, connection methods, and performance parameters to ensure the comprehensiveness and accuracy of interface verification. Physical specifications refer to the geometric dimensions, shape, and material of the component interface; connection methods refer to the connection types used between components, such as bolted connections, welding, and flange connections; and performance parameters refer to the mechanical, thermal, and fluid performance indicators of the interface under specific working conditions.

[0047] In this embodiment, semantic matching is performed on the interface attribute information and the pre-set cross-disciplinary interface standards in the parameter configuration library. This aims to verify whether the connections between components from different disciplines conform to the preset specifications. Semantic matching refers to comparing the extracted component interface attribute information with the standardized interface requirements stored in the parameter configuration library for different professional interface types, such as structural and mechanical / electrical, or water supply and drainage and HVAC. Its purpose is to ensure the compatibility and compliance of components from different disciplines at their connections. This can be achieved through a rule-based matching engine that compares the keywords and numerical ranges of the component interface attributes with the standards; or by using natural language processing technology. Semantic analysis is performed on unstructured interface descriptions, and intelligent matching is performed with standards. Based on the semantic matching results, first verification information is generated, aiming to transform the output of semantic matching into structured verification results. The first verification information is a quantitative or qualitative description of the interface attribute matching status, such as indicating successful matching, incompatibility, or missing necessary attributes. Its purpose is to provide conflict basis at the interface level for the subsequent generation of conflict guidance information. It can be implemented by encoding the matching results into specific status codes or Boolean values ​​and attaching a detailed matching report; or by generating structured data containing conflict type, conflict components, conflict location, and suggested solutions.

[0048] S22: Dynamic interference detection and safety distance verification are performed on the relative positions between target components using a predefined spatial topology algorithm to identify whether there are hard collisions and soft collisions between target components, and the minimum net distance threshold is adjusted according to the functional type of the target components to generate second verification information. In this embodiment, a predefined spatial topology algorithm is used to perform dynamic interference detection and safety distance verification on the relative positions between target components, aiming to identify physical conflicts and insufficient safety distances between components in the model. The spatial topology algorithm analyzes the geometric relationships of components in three-dimensional space. Dynamic interference detection refers to the detection performed by simulating the space occupancy of components during movement or installation to discover potential collisions. Safety distance verification verifies whether necessary spacing is maintained between components to avoid conflicts caused by maintenance, construction, or functional requirements. This can be achieved by using geometric algorithms such as bounding box detection and octree spatial partitioning for rapid coarse detection, combined with precise Boolean operations for interference analysis; or by using voxelization or meshing-based methods to discretize the space occupied by components for efficient collision detection.

[0049] For example, in this embodiment, a spatial topology algorithm is used to dynamically detect the relative positions between target components. This can be implemented as a two-layer spatial topology algorithm that combines coarse detection using AABB bounding boxes with precise detection using triangular facets. The coarse detection includes establishing axis-aligned bounding boxes for the target components to quickly eliminate components with no collision potential. The precise detection includes performing triangular facet intersection calculations on overlapping bounding box components to identify hard collisions. The safety distance verification includes calculating the minimum Euclidean distance between the target component surfaces and comparing it with a dynamic clearance threshold to identify soft collisions. The dynamic clearance threshold can be adaptively adjusted according to the functional type, for example, a minimum safety distance can be preset for pipes, cable trays, and structural beams. The output includes collision ID, location, type, involved components, and suggested modification direction.

[0050] It should be noted that the specific implementation of the spatial topology algorithm can be determined by technical personnel in the corresponding technical field based on existing common knowledge data, historical experience, and actual scenarios, and will not be elaborated here.

[0051] In this embodiment, the system identifies whether hard and soft collisions exist between target components. Hard and soft collisions are two main types of spatial conflict detection. Hard collisions refer to physical overlap between components, where two or more components occupy the same space, which is generally unacceptable. Soft collisions refer to situations where components do not physically overlap but fail to meet preset safety distance or clearance requirements, potentially affecting construction, maintenance, or functionality. Soft collisions serve to differentiate between different degrees of severity to allow for appropriate handling strategies. The minimum clearance threshold is adjusted based on the functional type of the target component to generate second verification information. This aims to introduce a judgment method based on component functional characteristics. Different functional types of components have different minimum clearance requirements. Dynamically adjusting the minimum clearance threshold based on the component's functional type makes safety distance verification more accurate and reasonable, avoiding false alarms or missed alarms. The second verification information is a structured description of the spatial conflict detection results. This can be implemented by pre-setting minimum clearance rules corresponding to different component functional types in a parameter configuration library and dynamically querying and applying them during detection; or by using a machine learning model to automatically learn and recommend appropriate clearance thresholds based on historical conflict data and component types.

[0052] S23: Based on the construction logic dependencies in the target component matching component association library, simulate the installation sequence of the target component based on the construction logic dependencies, detect whether there are timing conflicts during the simulation process, and generate third verification information based on the preset critical path analysis strategy; In this embodiment, the construction logic dependencies in the component association database are matched based on the target components to identify the sequence and mutual constraints of components during construction. The component association database stores construction logic dependency rules between different component types, such as "install the main structure first, then the curtain wall" or "lay the pipes first, then the ceiling." Matching these construction logic dependency rules is the foundation for simulating the construction sequence. This can be achieved by querying information such as component type, location, and professional attributes to find the corresponding construction dependencies in the component association database; or by storing the dependencies between components in a graph database and performing path queries. Simulating the installation sequence of the target components based on construction logic dependencies aims to discover time conflicts by simulating the construction process. Specifically, based on the matched construction logic dependencies, the installation sequence and timeline of components can be simulated. This reveals potential timing issues in actual construction, such as the installation of a component depending on an incomplete component, or conflicts in the installation of multiple components. This can be achieved by using a discrete event simulation model to simulate component installation activities and resource allocation; or by using a state machine-based model to describe the state transitions and event triggering of component installation.

[0053] In this embodiment, detecting timing conflicts during the simulation process is the key focus of construction timing simulation. A timing conflict refers to the discovery during the simulated installation process that the installation conditions of some components are not met, or that the installation activities of multiple components overlap in time and cannot be performed in parallel. Its purpose is to identify potential bottlenecks and unreasonable aspects in the construction plan. Third verification information is generated based on a preset critical path analysis strategy. The critical path analysis strategy is a project management strategy used to identify the sequence of activities that has the greatest impact on the overall project duration. Applying the critical path analysis strategy to construction timing simulation can help identify key components or activities that cause timing conflicts. The third verification information is a structured description of the timing conflict detection results. It can be implemented by analyzing the simulation results and identifying timing conflict points on the critical path using the critical path method or program review algorithm; or by generating a report containing conflicting activities, causes of conflict, scope of impact, and suggested adjustment schemes.

[0054] For example, the construction sequence conflict detection and critical path analysis in this embodiment can be implemented by the following steps: reading construction logic dependencies from the component association database and generating a directed acyclic graph; simulating the installation sequence according to the construction procedures to determine whether there are contradictions between installations that are installed first and those that are installed later; calculating the total construction period and critical procedures using the CPM critical path method; identifying sequence conflicts, specifically including resource conflicts, spatial conflicts, and procedure reversal conflicts; wherein, the output of the construction sequence conflict detection and critical path analysis may include conflicting procedures, critical paths, and optimizable sequence schemes.

[0055] S24: Generate conflict guidance information based on the first verification information, the second verification information, and the third verification information.

[0056] In this embodiment, generating conflict guidance information based on the first verification information, the second verification information, and the third verification information is the final output of the conflict detection process. Specifically, the results of interface attribute verification, spatial conflict detection, and construction sequence conflict detection are integrated to generate comprehensive and detailed conflict guidance information. Its purpose is to provide users with multi-dimensional conflict reports, thereby guiding subsequent conflict resolution and design optimization. This can be achieved by summarizing, classifying, and prioritizing the three types of verification information to generate a unified conflict report; or by displaying different types of conflicts in the BIM model through a visual interface, and providing detailed conflict attributes and suggestions.

[0057] Specifically, in the parametric modeling process, to ensure the quality and feasibility of the target building model, this embodiment uses a conflict detection process to verify the connection attributes, spatial layout relationships, and engineering logic sequence of model components. First, the interface attribute information of each target component in the target building model is extracted, covering details such as physical specifications, connection methods, and performance parameters. The extracted interface attribute information is semantically matched with pre-defined cross-disciplinary interface standards in the parameter configuration library to identify compatibility issues in connections between components from different disciplines, and based on this, first verification information is generated. Simultaneously, a predefined spatial topology algorithm is used to determine the relative positions between target components. Dynamic interference detection and safety distance verification are performed. This process not only identifies whether there are hard collision conflicts due to physical overlap of components, but also dynamically adjusts the minimum clearance threshold according to the functional type of the components to discover soft collision conflicts that may affect construction or maintenance, thereby generating second verification information. In addition, by matching the construction logic dependencies in the component association database, the installation sequence of the target components can be simulated. During the simulation, it detects whether there are timing conflicts caused by unmet dependencies or resource conflicts, and generates third verification information based on a preset critical path analysis strategy. Finally, the first verification information, the second verification information, and the third verification information are integrated to generate conflict guidance information.

[0058] Through the above technical solutions, this application can significantly improve the comprehensiveness and accuracy of conflict detection in multi-disciplinary collaboration. Specifically, by semantically matching the interface attributes of model components, potential incompatibility issues at the connection points of cross-disciplinary components can be effectively identified, avoiding rework caused by inconsistent interface standards. At the same time, through dynamic interference detection and minimum clearance adjustment based on function type, the identification of spatial conflicts becomes more accurate, not only detecting physical collisions but also providing early warnings of insufficient safety distances affecting construction, maintenance, and functionality. Furthermore, by simulating construction logic dependencies and installation sequence, combined with critical path analysis, timing conflicts in the construction plan can be identified in advance, thereby optimizing the construction process and avoiding on-site idle work and schedule delays. Through the combined effect of the above multi-dimensional verification mechanisms, the generated conflict guidance information becomes more detailed and instructive, improving design quality and construction efficiency while reducing project risks and costs.

[0059] In some implementations, conflict detection and compliance verification processes are executed in parallel during parametric modeling, generating conflict guidance information and verification reports based on the results. However, in actual project implementation, compliance verification for building projects may involve complex planning elements, diverse regulatory provisions, and different levels of architectural design. Failure to meticulously identify and layer-by-layer verify these elements may result in insufficient accuracy and comprehensiveness in the generated verification reports, thereby affecting overall collaborative efficiency.

[0060] In this regard, this application further proposes that, in one embodiment, step S20 includes: S201: Extract planning elements from project task data, and perform semantic mapping and logical association between planning elements and normative clauses in the parameter configuration library to identify mandatory clauses and recommended guidelines that require compliance verification. In this embodiment, planning elements are extracted from project task data to identify and obtain key information related to the planning of the building project. These planning elements include land use, plot ratio, building density, green space ratio, building height restrictions, setback requirements, fire access requirements, and sunlight analysis requirements. Extracting these planning elements is fundamental to compliance verification, ensuring that subsequent verification work is based on accurate and comprehensive planning data. The extraction process can employ natural language processing technology to parse unstructured text data, or directly obtain data from structured data using predefined templates and rules. The planning elements are then compared with those in the parameter configuration library. The purpose of semantic mapping and logical association of the standard provisions is to establish the correspondence between project planning elements and various standard provisions preset in the parameter configuration library. Semantic mapping refers to aligning planning elements with standard provisions in different forms of expression on a semantic concept basis, such as matching building height with height restriction requirements. Logical association, on this basis, further clarifies how planning elements are constrained by standard provisions, such as the calculation relationship between plot ratio and total building area. This process can utilize ontology technology to construct knowledge graphs, or use machine learning models for pattern recognition and association rule mining to ensure that planning elements can accurately reference and apply relevant standard provisions.

[0061] In this embodiment, mandatory clauses and recommended guidelines requiring compliance verification are identified. This aims to distinguish the nature of regulatory provisions, classifying them into mandatory clauses that must be strictly followed and recommended guidelines that provide optimization directions. Mandatory clauses typically involve binding provisions in regulations, standards, and industry standards, such as fire safety distances and structural load requirements. Recommended guidelines may include local regulations, design guidelines, green building standards, etc., to improve building quality or achieve specific goals. The identification and distinction between mandatory clauses and recommended guidelines determines the rigor of the verification and the focus of the report. Furthermore, the identification process can be conducted through a pre-set labeling system, keyword matching, or expert system rules.

[0062] S202: Based on mandatory provisions and recommended guidelines, conduct the first compliance verification of the spatial structure and development intensity distribution of building groups; In this embodiment, step S202 aims to conduct a compliance check on the building planning at the macro level. Specifically, the spatial structure verification of the building complex includes verifying whether the overall layout, spacing, orientation, setbacks, and fire access of the buildings comply with planning requirements and mandatory clauses. The development intensity distribution verification includes verifying whether indicators such as plot ratio, building density, and green space ratio are within the prescribed range. Based on this, the first compliance verification mainly assesses the compliance and rationality of the project from the overall planning level, ensuring that the project meets basic requirements in terms of urban planning and land use. Furthermore, the first compliance verification can be implemented using spatial analysis algorithms, geometric calculations, and direct comparison with planning indicators.

[0063] S203: Based on mandatory provisions and recommended guidelines, conduct a second compliance verification of the size, circulation, and facility configuration of individual buildings or functional spaces; In this embodiment, step S203 aims to conduct compliance checks on the micro-level architectural design details. Specifically, the verification of building unit dimensions includes checking whether the floor height, span, and depth comply with regulations; the verification of functional space flow includes checking whether the organization of pedestrian, material, and vehicular traffic is reasonable and safe, such as the width of evacuation routes and the slope of accessible ramps; and the verification of facility configuration includes checking whether fire-fighting facilities, electromechanical equipment, and pipeline layout meet relevant standards. Based on this, the second compliance verification aims to ensure that the building's internal functions and detailed design meet specific usage requirements and safety standards. Furthermore, the second compliance verification can be achieved using methods such as component attribute extraction, path analysis, and collision detection from the BIM model.

[0064] S204: Generate a verification report based on the results of the first compliance verification and the second compliance verification.

[0065] In this embodiment, step S204 aims to summarize and integrate the compliance verification results at both the macro and micro levels to form a comprehensive verification report. The verification report will not only list all matters that do not comply with mandatory provisions, but also point out aspects that fail to meet recommended guidelines, and may provide improvement suggestions. Furthermore, the generation of the verification report can be achieved by presenting the identified problems, relevant regulatory provisions, degree of impact, and suggested modification directions in a structured manner to provide clear feedback.

[0066] Specifically, to ensure that the target building model fully and accurately meets various planning and design specifications during parametric modeling, this embodiment further refines the compliance verification process. Specifically, all relevant planning elements are extracted from the project task data, and semantic mapping and logical association are performed between the extracted planning elements and preset specification clauses in the parameter configuration library to ensure that the planning elements can be accurately referenced and applied to the corresponding specification requirements. Based on this, mandatory clauses and recommended guidelines are distinguished, setting different priorities and focuses for subsequent verification work. Accordingly, compliance verification is divided into first compliance verification and second compliance verification, where the first compliance verification specifically involves verifying the identified mandatory clauses and recommended guidelines. The first set of guidelines assesses the spatial structure and development intensity distribution of the entire building complex, such as checking whether the plot ratio, building density, green space ratio, and overall layout comply with urban planning requirements, ensuring the project's compliance and rationality at the overall planning level. The second compliance verification, based on mandatory clauses and guidelines, checks details such as the size, circulation, and facility configuration of individual buildings or functional spaces, such as checking whether room sizes, evacuation routes, and equipment installation space meet specific functional and safety standards. Through this layered verification mechanism, all different stages from macro planning to micro design can be comprehensively covered. Finally, the results of the first and second compliance verifications are integrated to generate a verification report.

[0067] For example, Figure 3 This is a schematic diagram of rule mapping implementation in this embodiment. The diagram shows the project site planning information interface and BIM visualization model. The process includes: first, inputting all the project plan information, and extracting planning elements through semantic parsing; matching the standard clauses and building a parameter configuration library to achieve semantic mapping; identifying land use constraints and generating rule control parameters; and finally outputting standardized constraint logic and modeling instructions to provide data support for compliance verification and realize the transformation from unstructured planning conditions to standardized rules.

[0068] For example, Figure 4 This is a schematic diagram of conflict detection implementation in this embodiment. The diagram shows multi-disciplinary pipeline and component models and marks three types of conflict points. The process includes: performing parallel verification from three dimensions: interface attribute semantic matching, spatial layout hard collision, and construction sequence logic; outputting the first, second, and third verification information respectively, and integrating them to generate conflict guidance information; wherein, the closed-loop update is triggered by construction data deviation or design change, and finally triggers the scheme update strategy through the collaborative architecture to achieve triple accurate verification of connection attributes, spatial layout, and engineering logic, and efficiently resolve multi-disciplinary conflicts.

[0069] Through the above technical solutions, this application can achieve layered and comprehensive verification of the compliance of the target building model. Specifically, by extracting planning elements from project task data and semantically mapping and logically associating them with the normative clauses in the parameter configuration library, the accuracy of the verification basis can be ensured. Furthermore, by distinguishing between mandatory clauses and recommended guidelines, the verification work can be more targeted and prioritized. By conducting the first compliance verification on the spatial structure and development intensity distribution of the building group, and the second compliance verification on the size, circulation, and facility configuration of individual buildings or functional spaces, a comprehensive coverage from macro planning to micro design is achieved, avoiding omissions and blind spots that may occur in simple verification. Finally, a targeted verification report is generated, which can provide clear modification directions and basis for the design end, improve the quality and efficiency of parametric modeling, effectively reduce the risk of design rework due to compliance issues in the later stages of the project, and thus optimize the smoothness and reliability of the entire multi-disciplinary collaborative process.

[0070] In some implementation schemes, a first compliance verification is proposed based on mandatory provisions and recommended guidelines for the spatial structure and development intensity distribution of building complexes, and a second compliance verification is proposed for the size, circulation, and facility configuration of individual buildings or functional spaces. However, in actual project plans, there may be conflicts between mandatory provisions and recommended guidelines, or contradictions may exist between different mandatory provisions, which may lead to the inability to effectively conduct subsequent compliance checks.

[0071] In this regard, this application further proposes that, in one embodiment, step S203 includes: S2031: When a conflict of requirements is identified between mandatory provisions and recommended guidelines, or a contradiction is identified between different mandatory provisions, at least one target rule is matched through a predefined priority rule base; In this embodiment, when a conflict is identified between mandatory clauses and recommended guidelines, or when different mandatory clauses contradict each other, it indicates that at least two rules or guidelines are inconsistent or mutually exclusive in the application of the project design or planning. This conflict identification can be achieved through semantic analysis, logical reasoning, or matching based on preset conflict patterns between the mandatory clauses and recommended guidelines. For example, keywords, numerical ranges, and logical relationships in the mandatory clauses can be analyzed to determine whether there are direct or indirect contradictions. Once a conflict is identified, at least one target rule is matched using a predefined priority rule base. This priority rule base is a structured knowledge base containing preset strategies and priority rankings for handling different types of conflicts; for example, it may stipulate that safety specifications take precedence over economic benefit recommendations. Based on the identified conflict type and the content of the involved clauses, one or more of the most applicable target rules are retrieved from the priority rule base as guiding rules for resolving the conflict.

[0072] S2032: Generate at least one optimized adaptation scheme based on the target rules, and evaluate the optimized adaptation scheme through a preset multi-objective optimization algorithm. Select the optimized adaptation scheme with the highest degree of fit with the recommended guidelines under the premise of satisfying all mandatory clauses as the conflict resolution scheme. In this embodiment, at least one optimized adaptation scheme is generated based on the matched target rules. The optimized adaptation scheme refers to a specific solution or design adjustment suggestion proposed for the conflict situation, aiming to comply with the target rules while taking into account all relevant requirements as much as possible. Furthermore, the generation of the optimized adaptation scheme can employ heuristic algorithms, constraint satisfaction techniques, or case-based reasoning methods. The optimized adaptation scheme is simulated and evaluated using a preset multi-objective optimization algorithm. This algorithm can simultaneously consider multiple competing objectives, such as maximizing the fit with recommended guidelines, minimizing costs, or maximizing performance while satisfying mandatory clauses. Simulation evaluation refers to the process of predicting the impact of the scheme on various indicators by simulating its performance in actual application. For example, it can simulate the impact of different design schemes on building performance, space utilization, or construction complexity. During this evaluation process, the optimized adaptation scheme with the highest fit with recommended guidelines while satisfying all mandatory clauses is selected as the conflict resolution scheme. This means that mandatory clauses are hard constraints that must be met, while recommended guidelines are soft objectives that are optimized as much as possible based on these constraints. Priority is given to ensuring the compliance of the scheme, and then the scheme that best matches the recommended guidelines is selected from the compliant schemes.

[0073] S2033: Based on the conflict resolution scheme as the benchmark rule, the verification logic of the first compliance verification and the second compliance verification is modified.

[0074] In this embodiment, the verification logic of the first compliance verification and the second compliance verification is modified based on the selected conflict resolution scheme as the benchmark rule. This means that the original rules that may cause conflicts are no longer directly used for verification. Instead, the compliance check is guided by the new rules or the modified rules established by the conflict resolution scheme. For example, if the conflict resolution scheme determines a specific building height or setback distance, the subsequent compliance verification will be based on the determined value.

[0075] Specifically, the introduction of the conflict resolution mechanism in this embodiment can effectively solve the contradictions between mandatory clauses and recommended guidelines, or between different mandatory clauses, in compliance verification. Specifically, when a potential conflict is identified, at least one target rule for resolving the current conflict is first matched using a predefined priority rule base. Based on the matched target rule, multiple optimized adaptation schemes are generated. These optimized adaptation schemes can satisfy all mandatory requirements while taking into account the recommended guidelines as much as possible. A preset multi-objective optimization algorithm is used to simulate and evaluate the optimized adaptation schemes, ensuring that the selected scheme not only complies with all mandatory clauses but also has the highest degree of conformity with the recommended guidelines. Finally, the selected conflict resolution scheme will serve as a new benchmark rule to modify the verification logic of the first and second compliance verifications.

[0076] Through the above technical solutions, this application can effectively identify and resolve potential conflicts between mandatory clauses and recommended guidelines or between different mandatory clauses, avoiding verification difficulties, inaccurate results, or iterative design caused by rule contradictions during compliance verification. By pre-matching target rules, generating and evaluating optimized adaptation schemes, and finally correcting the verification logic, it ensures that subsequent first and second compliance verifications are conducted on a logically clear and conflict-free basis, thereby improving the accuracy, efficiency, and reliability of compliance verification.

[0077] In some implementations, conflict detection and compliance verification processes are executed in parallel during parametric modeling, generating conflict guidance information and verification reports respectively based on the results. However, the generated conflict guidance information and verification reports are usually structured data, which may lead to information overload when presented directly to project participants. This makes it difficult to quickly locate problems, clarify responsibilities, and efficiently promote implementation, potentially delaying the problem-solving process and affecting the efficiency of multi-disciplinary collaboration.

[0078] In this regard, this application further proposes that, in one embodiment, step S30 includes: S31: Perform semantic analysis on conflict guidance information and verification reports to identify several pending items and generate corresponding briefing tasks through a preset task template library; In this embodiment, semantic analysis is performed on the conflict guidance information and verification report to extract key issues and specific actions that need to be taken from a large amount of technical data. This can be achieved through natural language processing techniques, such as keyword extraction, entity recognition, and sentiment analysis, or by identifying specific phrases or data structures in the report through predefined rule sets and pattern matching, thereby transforming unstructured or semi-structured report content into structured tasks. The identified tasks are specific problems that need to be solved or specific tasks that need to be completed. The task template library is a collection of various types of briefing task templates that are pre-stored. Each template defines the structure, fields, and possible related professional fields of the task. After the tasks are identified, the most suitable template is selected from the task template library according to the type or content of the tasks, and the specific information of the tasks is filled in to generate standardized briefing tasks. For example, for tasks involving structural conflicts, the corresponding "structural conflict resolution" task template can be called, and the specific location of the conflict and the components involved can be automatically filled in.

[0079] S32: Identify the roles, permissions, and professional fields of participating users, and determine the responsible user and collaborating user for each handover task by matching them with the technical field attributes of the handover task. In this embodiment, the role permissions and professional field scope of participating user terminals are identified. Specifically, this can be achieved by querying user information stored in the cloud collaboration platform. Each participating user terminal is pre-configured with its assigned role in the platform, such as structural engineer, HVAC engineer, architect, project manager, etc., as well as its professional field of expertise or responsibility, such as structural design, mechanical and electrical design, architectural design, construction management, etc. This configuration information constitutes the profile information of each user terminal. The technical field attribute of the briefing task refers to the professional field classification involved in each generated briefing task, such as "structure", "mechanical and electrical", "architecture", "fire protection", etc. By linking the technical field attribute of the briefing task with the participating user terminal's role permissions and professional field scope, the user terminal can identify the role permissions and professional field scope of participating user terminals. Matching users based on their professional fields can initially screen out users who may be responsible or need to collaborate. For example, for a handover task involving structure, users with expertise in structural engineering or "structural design" will be matched first. Based on this, combined with user role permissions (e.g., only engineers with design modification permissions can be the responsible party) and corresponding matching algorithms (e.g., considering the user's current workload and historical task completion status), the responsible party user and the collaborating party user are finally determined for the handover task. For example, the responsible party user is the user primarily responsible for resolving the handover task, and the collaborating party user is the user who needs to provide assistance or be aware of the handover task's progress.

[0080] S33: Based on the user terminals of the responsible party and the collaborating party respectively, a task disclosure list is generated and distributed in the cloud collaboration platform. The task disclosure list includes task content, processing time limit, and associated conflict guidance information and verification report.

[0081] In this embodiment, the task disclosure list is generated and distributed in the cloud collaboration platform based on the responsible party's user terminal and the collaborating party's user terminal, respectively. This means that, according to the aforementioned responsible party and collaborating party, a unique task list is generated for each participating user terminal in the cloud collaboration platform. The generated task disclosure list is typically not just a simple list of task names; it includes detailed task content, processing deadlines, associated conflict guidance information, and verification reports (providing the task's background and detailed technical basis). The task content specifically describes the problems and requirements to be solved; the processing deadline specifies the task's completion date; and the associated conflict guidance information and verification reports provide the task's background and detailed technical basis. After the task disclosure list is generated, it is distributed to the corresponding user terminals through the cloud collaboration platform's message notification mechanism, ensuring that relevant personnel can receive and process their assigned or collaborative tasks in a timely manner.

[0082] Specifically, this application identifies specific tasks by performing semantic analysis on conflict guidance information and verification reports, and quickly generates standardized handover tasks using a task template library, avoiding the tedious process of manually filtering information from reports and manually creating tasks. At the same time, by identifying the role permissions and professional fields of participating users and matching them with the technical field attributes of the handover tasks, it achieves precise task allocation, thereby ensuring that each task is assigned to the most suitable and capable responsible user and that necessary collaborating users are notified.

[0083] Through the above technical solution, this application can transform complex conflict guidance information and verification reports into clear and executable briefing tasks, and realize the automated generation and allocation of tasks. This has the effect of improving the efficiency from problem discovery to problem resolution, reducing human intervention and errors in information transmission, ensuring that project participants can obtain the task information they are responsible for in a timely and accurate manner, thereby promoting collaboration among multiple disciplines and effectively reducing project risks caused by information lag or unclear responsibilities.

[0084] In some implementations, a method is proposed to generate handover tasks on a cloud-based collaborative platform based on conflict guidance information and verification reports, and to identify the roles, permissions, and professional fields of participating users. The responsible user and collaborating user are then determined by matching the technical field attributes of the handover task. However, relying solely on role permissions and professional field matching may lead to uneven task allocation or inefficiency, especially when multiple users meet the professional requirements. How to accurately and evenly allocate tasks may become a challenge that needs to be addressed.

[0085] In this regard, this application further proposes that, in one embodiment, step S32 includes: S321: Identify the pre-configured role permission levels and professional domain tags of each participating user terminal in the cloud collaboration platform; In this embodiment, when determining the responsible user terminal and collaborating user terminal for each handover task, it is first necessary to identify the pre-configured role permission level and professional field label of each participating user terminal in the cloud collaboration platform. Among them, the role permission level refers to the definition of the user terminal's operation permissions and scope of responsibility in the platform, such as project manager, professional leader, ordinary member, etc., which are usually pre-set by the administrator or project leader. The professional field label refers to the identification of the technical field that the user is good at or responsible for, such as structural design, HVAC, electrical engineering, water supply and drainage, etc., which can be configured by the user terminal itself or assigned by the administrator.

[0086] S322: Extract the technical field attribute keywords of the briefing task, and perform semantic matching with the professional field tags on the user side. Based on the semantic matching results, select a candidate set of responsible parties. In this embodiment, the briefing task typically includes a description of a specific technical problem, such as "conflict between structural beams and pipes" or "fire compartment adjustment." Natural language processing technology can be used to automatically extract keywords such as "structure," "pipes," and "fire prevention" from the task description as technical field attribute keywords. Subsequently, the extracted technical field attribute keywords are semantically compared with the identified user professional field tags. Semantic matching can be performed in various ways, such as ontology-based matching and keyword similarity calculation, to assess the degree of relevance between the task and the user's professional field. The semantic matching results will be used to screen out users who are highly relevant to the technical field of the task, forming a potential set of responsible parties.

[0087] S323: Based on the current task load and semantic matching results of the candidate set of responsible parties, determine the user terminals of the responsible party and the collaborating party through a predefined load balancing algorithm.

[0088] In this embodiment, after selecting the candidate set of responsible parties, to avoid uneven task allocation, it is necessary to further consider the current task load of each candidate user, i.e., the number of tasks or the complexity of the tasks they are currently processing. The load balancing algorithm will comprehensively consider the accuracy of semantic matching and the user's current task load. For example, the load balancing algorithm can prioritize the user with the highest semantic matching degree and the lowest current task load as the responsible party user. A similar logic can be used for the collaborating party user, selecting users who are related to the task but not primarily responsible, and who have spare capacity for collaboration. Predefined load balancing algorithms can include round-robin algorithms, least-connection algorithms, weighted least-connection algorithms, etc., to ensure that tasks can be efficiently and evenly allocated.

[0089] For example, the load balancing algorithm in this embodiment can adopt the weighted minimum load algorithm, which specifically includes: matching candidate responsible persons by professional tags; obtaining the current number of pending tasks, task complexity, and historical completion rate; calculating the load weight and automatically allocating it to the optimal responsible person; and automatically matching collaborators by professional association.

[0090] It should be noted that the specific implementation of the load balancing algorithm can be determined and reproduced by technical personnel in the corresponding technical field based on existing common knowledge data, historical experience, and actual scenarios, and there is no need to elaborate on it here.

[0091] Specifically, the solution in this application identifies the user's role and permission level and professional domain tags, and performs semantic matching by combining the technical domain attribute keywords of the briefing task, thereby initially screening out a candidate set of responsible parties with professional capabilities. On this basis, the solution further considers the current task load of the candidate set of responsible parties and conducts a comprehensive evaluation through a predefined load balancing algorithm to finally determine the most suitable user terminal of the responsible party and the collaborating party.

[0092] Through the above technical solutions, this application can achieve accurate and balanced allocation of handover tasks. Specifically, the solution of this embodiment not only ensures that handover tasks can be allocated to users with corresponding professional knowledge and permissions, but also effectively avoids the problem of uneven task allocation by introducing consideration of the user's current task load and combining it with a load balancing algorithm, thereby improving the efficiency and quality of project collaboration.

[0093] In one embodiment, step S60 includes: S61: Compare and analyze the construction data with the target building model to identify deviations in geometric dimensions, spatial location, and attribute parameters. In this embodiment, step S61 aims to accurately quantify the differences between the actual situation at the construction site and the design model. Geometric dimension deviation refers to the discrepancy between the physical dimensions of the component (length, width, height, etc.) and the target building model; spatial position deviation refers to the discrepancy between the component's coordinates and orientation in three-dimensional space and the target building model; attribute parameter deviation refers to the discrepancy between the component's non-geometric information, such as material type, fire resistance rating, and equipment model, and the target building model. Further, the comparison analysis and deviation identification process can be achieved by acquiring three-dimensional point cloud data of the construction site using technologies such as laser scanning and photogrammetry. Then, the point cloud data is registered and the difference analysis is performed with the target building model, and algorithms are used to automatically identify deviations in geometric dimensions and spatial position. Simultaneously, attribute parameters of the component are acquired through IoT sensors or manual input and compared with the attribute information in the model to identify attribute parameter deviations. Alternatively, RFID tags or QR codes can be deployed at the construction site, combined with mobile terminal devices for real-time component positioning and information collection. Finally, the collected data is compared with the corresponding component information in the target building model, and differences in geometric dimensions, spatial position, and attribute parameters are identified through computational geometry algorithms and database queries.

[0094] S62: If any one of the deviations, such as geometric dimension deviation, spatial position deviation, and attribute parameter deviation, exceeds the preset deviation threshold, identify the deviation type and match the corresponding update strategy according to the deviation type. In this embodiment, step S62 aims to determine how to update the target building model based on the nature and severity of the deviation. A preset deviation threshold is used to determine whether a deviation requires an update. This threshold can be dynamically determined based on the actual scenario and historical experience. For example, a dimensional deviation exceeding 5mm may require an update, while a positional deviation exceeding 10mm may require an update. Identifying the deviation type helps in selecting the most suitable update method. This can be achieved by pre-establishing a mapping rule base between deviation types and update strategies. When a deviation exceeds the threshold, the corresponding update strategy is searched in the mapping rule base according to the deviation type. For example, for "beam length deviation," the strategy of "adjusting beam component parameters" might be matched; for "pipeline position deviation," the strategy of "recalculating the pipeline path" might be matched. Alternatively, a machine learning-based method can be used, training a classification model using historical data. This model is trained to automatically identify the most suitable update strategy based on the input deviation data, which includes the deviation amount, deviation type, and affected components. For example, for minor geometric deviations, a strategy of local fine-tuning might be matched, while for severe structural deviations, a strategy requiring the regeneration of related components might be matched.

[0095] S63: Based on the matched update strategy, the parameter configuration library and component association library are called through the process control script to drive the parametric modeling engine to perform local or global iterative updates on the target building model and obtain the updated building model. In this embodiment, step S63 is the execution process of updating the target building model. The process control script, based on the matched update strategy, coordinates information from the parameter configuration library and the component association library to guide the parametric modeling engine in modifying the target building model. Local updates may only involve adjustments to a single component or a local area, while global iterative updates may involve chain-reaction adjustments to multiple interconnected components to ensure the overall coordination of the model. The process control script can be a predefined programming instruction or macro command, which, according to the update strategy, retrieves new parameter values ​​from the parameter configuration library, such as new component dimensions and position coordinates, and combines them with the component... The logic defined in the relational database, such as the association between walls and doors / windows, and the connection relationships between structural components, drives the parametric modeling engine to recalculate and generate the affected components. Alternatively, it can be implemented through a rule-based expert system. Specifically, when a specific update strategy is matched, the expert system activates the corresponding rule set. These activated rule sets query the parameter configuration library to obtain the parameters required for the update and use the component relational database to identify all affected components and their dependencies. Finally, the parametric modeling engine executes the matched update strategy to iteratively adjust the target building model until all constraints are met, resulting in an updated building model.

[0096] S64: Perform conflict detection and compliance verification processes in parallel on the updated building model, and send the updated building model and its associated data set after the conflict detection and compliance verification processes are passed to the construction management end.

[0097] In this embodiment, step S64 aims to ensure the quality and reliability of the updated building model. After the model is updated, conflict detection and compliance verification must be performed again to verify whether the update operation has introduced new conflicts or violated specifications. Only updated building models that pass conflict detection and compliance verification can be considered valid and sent to the construction management end, thereby avoiding the transmission of defective models to the construction site. After the model update is completed, the same conflict detection and compliance verification processes as in the initial modeling phase are triggered. These two processes can run in parallel to improve efficiency. If all verifications pass, the updated model and related conflict guidance information, verification reports, and other data are packaged and sent to the construction management end. Alternatively, an automated testing module can be integrated into the collaborative architecture. This automated testing module automatically calls a preset set of test cases after the model update is completed. These test cases cover various potential conflicts and compliance issues.

[0098] Specifically, the solution in this application compares construction data obtained from the construction site with the current target building model to identify specific differences in geometric dimensions, spatial location, and attribute parameters. If any of these differences exceeds a preset tolerance range, the type of deviation is further identified, and the most suitable update strategy is matched from a preset strategy library. Based on the matched update strategy, the process control script in the collaborative architecture calls standardized parameters stored in the parameter configuration library and logical dependencies defined in the component relationship library, driving the parametric modeling engine to perform partial adjustments or comprehensive iterative updates to the target building model, thereby generating an updated building model that conforms to the actual situation or the latest design intent. Furthermore, to ensure that the updated building model still meets design requirements and specifications, a conflict detection process and a compliance verification process are executed in parallel. Only when the updated building model passes all necessary checks and confirms that no new conflicts or non-compliance have been introduced is the updated building model and its associated latest data set sent to the construction management end to provide accurate guidance for subsequent construction activities.

[0099] For example, suppose that at the construction site of a building project, the actual position and size data of an installed pipe section are obtained using a 3D laser scanner. This construction data is compared with the corresponding pipe component in the target building model. If the comparison shows that the actual installation position of the pipe section has a 15mm vertical deviation relative to the model, and its length is 8mm shorter than the design model, and the preset deviation threshold is 10mm, then the vertical position deviation exceeds the threshold. Further, the deviation types are identified as "pipe spatial position deviation" and "pipe geometric dimension deviation." According to preset matching rules, the "pipe spatial position deviation" is matched with an update strategy of "adjusting the pipe path," and the "pipe geometric dimension deviation" is matched with an update strategy of "adjusting the pipe component length parameters." This triggers a process control script. This process involves retrieving parameters such as the allowable bending radius and connection method for the pipe type from the parameter configuration library, and combining these with the connection constraints between the pipe and other equipment defined in the component association library. The parametric modeling engine then remodels the pipe section and its upstream and downstream connecting components to adapt to the new location and length, ensuring that the connection relationships with other components remain valid. Immediately after generating the updated building model, a conflict detection process is executed to check for collisions with other professional components, and a compliance verification process is performed to verify that the pipe's slope, clearance, etc., comply with relevant specifications. If all checks and verifications pass, the updated building model, along with its latest deviation information, verification reports, and other data sets, is sent to the construction management end to guide construction personnel in subsequent installation or corrections.

[0100] Through the above technical solutions, this application can respond to and handle deviations or design changes that occur during construction. Specifically, by identifying the types of geometric dimension deviations, spatial position deviations, and attribute parameter deviations, and matching corresponding update strategies, blind or inappropriate model modifications are avoided, thereby ensuring the relevance and effectiveness of the updates. After updating the model, a conflict detection process and a compliance verification process are immediately implemented, which can effectively prevent the introduction of new design defects or non-compliance issues due to model updates, thereby ensuring the accuracy and reliability of the updated model data. Through this dynamic model update and verification mechanism, the practical value of the BIM model throughout the entire construction lifecycle can be enhanced, thereby reducing construction risks and rework costs, and improving the efficiency and quality of multi-disciplinary collaborative work.

[0101] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0102] In one embodiment, a BIM-based multi-disciplinary collaborative system is provided, which corresponds one-to-one with the BIM-based multi-disciplinary collaborative method described in the above embodiments. The BIM-based multi-disciplinary collaborative system includes: The model generation module is used to respond to the received project plan information, extract project task data and planning condition data, and perform corresponding parametric modeling through a pre-built collaborative architecture to generate a target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. The verification and inspection module is used to execute the conflict detection process and the compliance inspection process in parallel during the parametric modeling process, and generate conflict guidance information and inspection reports respectively based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. The task generation module is used to generate several handover tasks on the cloud collaboration platform associated with the collaboration architecture based on conflict guidance information and verification reports, and then distribute them to the corresponding participating user terminals. The data association module is used to associate relevant data sets for the target building model in response to the handover confirmation information from the participating user terminal. The relevant data sets include conflict guidance information, verification reports and handover confirmation information. The construction monitoring module is used to send the target building model and its associated data set to the construction management terminal, and to obtain construction data in real time through the construction management terminal; The model update module is used to compare and analyze the construction data with the target building model in real time. When the deviation is detected to exceed the preset deviation threshold or a design change instruction is received, the preset update strategy is triggered through the collaborative architecture to update the target building model.

[0103] Optionally, the model generation module includes: The semantic parsing submodule is used to perform semantic parsing on project plan information to extract planning condition data, and convert the planning condition data into standardized parameters and constraint rules through the parameter configuration library; The instruction generation submodule is used to generate a sequence of executable modeling task instructions for process control scripts based on standardized parameters and constraint rules. The instruction execution submodule is used to execute the modeling task instruction sequence through the flow control script, driving the corresponding parametric modeling engine to generate the target building model by combining the predefined logical associations in the component association library.

[0104] For specific limitations regarding a BIM-based multi-disciplinary collaborative system, please refer to the limitations of a BIM-based multi-disciplinary collaborative method described above, which will not be repeated here. Each module in the aforementioned BIM-based multi-disciplinary collaborative system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A BIM-based multi-disciplinary collaborative method, characterized in that, Including the following steps: In response to the received project plan information, the project task data and planning condition data are extracted, and the corresponding parametric modeling is performed through a pre-built collaborative architecture to generate the target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. During parametric modeling, conflict detection and compliance verification processes are executed in parallel, and conflict guidance information and verification reports are generated based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. Based on the conflict guidance information and verification report, several handover tasks are generated on the cloud-based collaborative platform associated with the collaborative architecture and distributed to the corresponding participating users, including: Semantic analysis is performed on conflict guidance information and verification reports to identify several pending items, and corresponding briefing tasks are generated through a pre-set task template library; Identify the roles, permissions, and professional fields of participating users, and match them with the technical field attributes of the handover tasks to determine the responsible and collaborating user terminals for each handover task, including: Identify the pre-configured role and permission levels and professional domain tags of each participating user in the cloud collaboration platform; Extract the technical field attribute keywords of the briefing task and perform semantic matching with the professional field tags on the user side. Based on the semantic matching results, select a candidate set of responsible parties. Based on the current task load and semantic matching results of the candidate set of responsible parties, the user terminals of the responsible parties and the collaborating parties are determined through a predefined load balancing algorithm. Among them, the load balancing algorithm comprehensively considers the accuracy of semantic matching and the user's current task load, and selects the user with the highest semantic matching degree and the lowest current task load as the responsible user end. For the collaborating user end, users who are related to the task but are not the main responsible party and have spare capacity are selected for collaboration. Based on the user terminals of the responsible party and the collaborating party respectively, a task list for handover is generated and distributed in the cloud collaboration platform. The task list includes task content, processing time limit, and associated conflict guidance information and verification report. In response to the handover confirmation information from the participating user, a relevant data set is associated with the target building model. The relevant data set includes conflict guidance information, verification reports, and handover confirmation information. Send the target building model and its associated data set to the construction management terminal, and obtain construction data in real time through the construction management terminal; The system compares and analyzes construction data with the target building model in real time. When a deviation is detected that exceeds the preset deviation threshold or a design change instruction is received, the system calls the parameter configuration library and component association library through the process control script to trigger the preset update strategy to update the target building model.

2. The BIM-based multi-disciplinary collaboration method according to claim 1, characterized in that: The steps of responding to received project plan information, extracting project task data and planning condition data, and executing corresponding parametric modeling through a pre-built collaborative architecture to generate a target building model, wherein the collaborative architecture includes a parameter configuration library, a component association library, and a process control script, include the following steps: Semantic parsing is performed on project plan information to extract planning condition data, and the planning condition data is converted into standardized parameters and constraint rules through a parameter configuration library; Generate a sequence of executable modeling task instructions based on standardized parameters and constraint rules to control the process. The process control script executes a sequence of modeling task instructions, driving the corresponding parametric modeling engine to generate the target building model by combining the predefined logical relationships in the component association library.

3. The BIM-based multi-disciplinary collaboration method according to claim 1, characterized in that: During the parametric modeling process, a conflict detection process and a compliance verification process are executed in parallel. Conflict guidance information and a verification report are generated based on the execution results, respectively. The conflict detection process includes steps such as verifying the connection attributes of model components, verifying spatial layout relationships, and verifying the engineering logical order. Extract the interface attribute information of the target component, perform semantic matching between the interface attribute information and the pre-set cross-professional interface standards in the parameter configuration library, and generate the first verification information based on the semantic matching result. The interface attribute information includes physical specifications, connection method and performance parameters. The relative positions between target components are dynamically interfered and the safety distance is verified by a predefined spatial topology algorithm. This identifies whether there are hard collisions or soft collisions between target components and adjusts the minimum net distance threshold according to the functional type of the target components to generate second verification information. Based on the construction logic dependencies in the target component matching component association library, the installation sequence of the target component is simulated based on the construction logic dependencies. During the simulation, the existence of timing conflicts is detected, and third verification information is generated based on the preset critical path analysis strategy. Conflict guidance information is generated based on the first verification information, the second verification information, and the third verification information.

4. The BIM-based multi-disciplinary collaboration method according to claim 1, characterized in that: During the parametric modeling process, a conflict detection process and a compliance verification process are executed in parallel. Conflict guidance information and a verification report are generated based on the execution results, respectively. The conflict detection process includes steps such as verifying the connection attributes of model components, verifying spatial layout relationships, and verifying the engineering logical order. Planning elements are extracted from project task data, and semantic mapping and logical association are performed between planning elements and normative clauses in parameter configuration library to identify mandatory clauses and recommended guidelines that require compliance verification. Based on mandatory provisions and recommended guidelines, the first compliance verification is conducted on the spatial structure and development intensity distribution of building groups; Based on mandatory provisions and recommended guidelines, a second compliance verification is conducted on the size, circulation, and facility configuration of individual buildings or functional spaces. A verification report is generated based on the results of the first compliance verification and the second compliance verification.

5. A BIM-based multi-disciplinary collaboration method according to claim 4, characterized in that: Prior to the step of conducting the first compliance verification of the spatial structure and development intensity distribution of the building complex based on mandatory provisions and recommended guidelines, the steps include: When a conflict is identified between mandatory provisions and recommended guidelines, or a contradiction is found between different mandatory provisions, at least one target rule is matched using a predefined priority rule base. At least one optimized adaptation scheme is generated based on the target rules, and the optimized adaptation scheme is simulated and evaluated by a preset multi-objective optimization algorithm. The optimized adaptation scheme with the highest degree of fit with the recommended guidelines under the premise of satisfying all mandatory clauses is selected as the conflict resolution scheme. Based on the conflict resolution scheme as the benchmark rule, the verification logic of the first compliance verification and the second compliance verification is modified.

6. A BIM-based multi-disciplinary collaborative method according to claim 1, characterized in that: The real-time comparison and analysis of construction data and the target building model, and the step of updating the target building model by calling the parameter configuration library and component association library through the process control script when the deviation exceeds the preset deviation threshold or a design change instruction is received, includes the following steps: The construction data is compared and analyzed with the target building model to identify deviations in geometric dimensions, spatial location, and attribute parameters. If any of the deviations in geometric dimensions, spatial position, and attribute parameters exceeds a preset deviation threshold, the deviation type is identified and the corresponding update strategy is matched according to the deviation type. Based on the matched update strategy, the parameter configuration library and component association library are called through the process control script to drive the parametric modeling engine to perform local or global iterative updates on the target building model and obtain the updated building model. The conflict detection process and compliance verification process are executed in parallel on the updated building model, and the updated building model and its associated data set after passing the conflict detection process and compliance verification process are sent to the construction management terminal.

7. A BIM-based multi-disciplinary collaborative system, used to execute the steps of a BIM-based multi-disciplinary collaborative method as described in any one of claims 1-6, characterized in that, include: The model generation module is used to respond to the received project plan information, extract project task data and planning condition data, and perform corresponding parametric modeling through a pre-built collaborative architecture to generate a target building model. The collaborative architecture includes a parameter configuration library, a component association library, and a process control script. The verification and inspection module is used to execute the conflict detection process and the compliance inspection process in parallel during the parametric modeling process, and generate conflict guidance information and inspection reports respectively based on the execution results. The conflict detection process includes verification of the connection attributes of model components, verification of spatial layout relationships, and verification of engineering logic order. The task generation module is used to generate several handover tasks on the cloud collaboration platform associated with the collaboration architecture based on conflict guidance information and verification reports, and then distribute them to the corresponding participating user terminals. The data association module is used to associate relevant data sets for the target building model in response to the handover confirmation information from the participating user terminal. The relevant data sets include conflict guidance information, verification reports and handover confirmation information. The construction monitoring module is used to send the target building model and its associated data set to the construction management terminal, and to obtain construction data in real time through the construction management terminal; The model update module is used to compare and analyze the construction data with the target building model in real time. When the deviation is detected to exceed the preset deviation threshold or a design change instruction is received, the preset update strategy is triggered through the collaborative architecture to update the target building model.

8. A BIM-based multi-disciplinary collaborative system according to claim 7, characterized in that: The model generation module includes: The semantic parsing submodule is used to perform semantic parsing on project plan information to extract planning condition data, and convert the planning condition data into standardized parameters and constraint rules through the parameter configuration library; The instruction generation submodule is used to generate a sequence of executable modeling task instructions for process control scripts based on standardized parameters and constraint rules. The instruction execution submodule is used to execute the modeling task instruction sequence through the flow control script, driving the corresponding parametric modeling engine to generate the target building model by combining the predefined logical associations in the component association library.

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