A BIM-based intelligent management method and system for prefabricated buildings

Abstract

This invention discloses a BIM model-based intelligent management method and system for prefabricated buildings, belonging to the field of prefabricated building management technology. Specifically, it includes: acquiring BIM data containing component geometric attributes, material properties, and installation logic relationships, as well as supply chain data on component production status, transportation location, and warehousing information; constructing an assembly dependency network with components as nodes and installation dependencies as edges; monitoring supply chain data in real time and identifying abnormal components; locating abnormal nodes in the assembly dependency network and traversing to determine the affected assembly paths; retrieving matching alternative components based on warehousing information and replacing nodes; and finally recalculating the installation sequence of affected components based on the updated assembly dependency network to generate a new assembly sequence. This method enables rapid response and dynamic adjustment of the assembly process to supply chain anomalies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of prefabricated building management, and particularly relates to a prefabricated building intelligent management method and system based on a BIM model. BACKGROUND

[0002] As an important development direction of building industrialization, prefabricated buildings have shown significant advantages in improving construction efficiency and ensuring engineering quality in recent years. With the accelerated advancement of the modernization process of the construction industry, BIM technology has become the core support for the whole life cycle management of prefabricated buildings, providing a digital foundation for realizing standardized design, factory production and on-site assembly of components. Under this background, how to realize fine management of the assembly process through BIM technology has become an important issue in the development of industry technology.

[0003] The existing technical solutions mainly focus on the application of static data of BIM models. Related technologies analyze the component information in the BIM model, establish the installation logical relationship between components, and develop assembly sequences based on this. In the aspect of supply chain management, the existing solutions can monitor the production progress and transportation status of components, and start the corresponding processing flow when abnormalities are found. Some improved solutions also establish a component inventory query mechanism to provide data support for plan adjustment. These technical solutions have achieved certain results in the management of standardized assembly processes.

[0004] However, the existing technical solutions still have room for improvement in dealing with the impact of supply chain abnormalities on the assembly process. Due to the lack of deep integration of BIM model data and supply chain data, it is difficult to assess the chain effect of individual component abnormalities on the overall assembly path in a timely manner. And when formulating adjustment strategies, often focus on the feasibility of replacing a single component, but do not fully consider the matching degree of the replacement scheme with the on-site assembly conditions and construction progress requirements, which may lead to new conflicts in the actual execution of subsequent assembly processes, affecting the continuity and coordination of the assembly process. SUMMARY

[0005] The purpose of the present application is to provide a prefabricated building intelligent management method and system based on a BIM model, which solves the following technical problems:

[0006] The existing prefabricated building management system cannot quickly assess the chain effect of component supply abnormalities on the overall assembly path, and the existing adjustment strategies lack systematic consideration of on-site assembly conditions and construction progress requirements, resulting in new coordination problems in the actual execution of the adjustment scheme.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A prefabricated building intelligent management method based on a BIM model, comprising the following steps:

[0009] S1, acquire the BIM model of the fabricated building and the component supply chain data, the BIM model contains the geometric properties, material properties and installation logical relationship of the fabricated components, the component supply chain data contains the production state, transportation location and storage information of the components;

[0010] S2, construct an assembly dependency network based on the installation logical relationship, the nodes in the assembly dependency network represent single components, and the edges represent the installation and dependency relationship between components;

[0011] S3, real-time monitoring of the component supply chain data, identifying abnormal components in the production delay state or transportation lag state;

[0012] S4, locate the node corresponding to the abnormal component in the assembly dependency network, traverse along the dependency edge direction in the assembly dependency network, determine the subsequent component set dependent on the abnormal component, and form the affected assembly path;

[0013] S5, based on the storage information, retrieve the alternative components with the same geometric properties and material properties as the abnormal components, and replace the node corresponding to the abnormal components;

[0014] S6, based on the replaced assembly dependency network, recalculate the installation order of all components in the affected assembly path, and generate a new component installation sequence.

[0015] As a further scheme of the application: in the S2, the specific process of constructing the assembly dependency network is:

[0016] Parse the component installation logical relationship in the building information model, extract the installation sequence constraints between components, take components as nodes and installation sequence as directed edges, establish an initial network structure, analyze the minimum installation gap data and maximum installation deviation data in the component space cooperation requirements, dynamically adjust the weight value of the edge according to the strictness of the space interference relationship, and perform loop detection on the network structure, when a circular dependency path is found, remove the circular dependency based on the priority setting in the component installation logical relationship, store the processed network structure as the assembly dependency network, and record the degree centrality data of the nodes and the weight data of the edges.

[0017] As a further scheme of the application: in the S3, the specific process of real-time monitoring of the component supply chain data is:

[0018] establishing a data connection with the supply chain management system, obtaining component production status data at fixed time intervals, establishing a data connection with the logistics tracking system, continuously receiving component transportation location data, setting production status monitoring conditions, marking the component as being in a production delay state when the component production stage marker has not been updated within a predetermined time period, setting transportation status monitoring conditions, marking the component as being in a transportation lag state when the component transportation trajectory deviates from the predetermined route, marking the component as being in a transportation lag state when the component transportation speed is continuously lower than the standard speed, and generating an abnormal component list through multi-source data fusion processing.

[0019] As a further scheme of the present application: in the S4, the specific process of determining the affected assembly path is:

[0020] extracting the type identifier and installation stage information of the abnormal component from the abnormal component list, locating the abnormal component node and its directly connected support component node in the assembly dependency network, retrieving the processing case library of the same type of abnormal component in the historical project, obtaining the alternative solution data and adjustment time data in the case library, analyzing the connection type and connection strength between the abnormal component and the support component, determining the adjustment time window under the current construction progress according to the installation stage information, calculating the path influence coefficient combining the connection strength data and the adjustment time window, sorting the affected paths based on the path influence coefficient, and generating an affected assembly path report.

[0021] As a further scheme of the present application: the process of calculating the path influence coefficient is:

[0022] obtaining the processing records of the same type of abnormal component in the historical case library, extracting the adjustment time and alternative solution in the processing records, analyzing the mechanical transmission characteristics of different connection types, establishing the correspondence between the component type and the adjustment scheme, calculating the basic influence value according to the connection strength and the mechanical transmission characteristics, calculating the time influence factor combining the adjustment time window under the current construction progress, and obtaining the path influence coefficient by synthesizing the basic influence value and the time influence factor.

[0023] As a further scheme of the present application: in the S5, the specific process of searching and replacing is,

[0024] Obtain the geometric property data and material property data of the abnormal component, generate a replacement component matching condition based on the geometric tolerance range and material compatibility requirements, send a query request containing the geometric tolerance and material requirements to the warehouse management system, receive the list of available replacement components returned by each warehouse node, obtain the transportation distance data, inventory quantity data and quality inspection report of each replacement component, analyze the interface size matching degree and installation process compatibility of each replacement component with adjacent nodes, calculate the applicability score in combination with the transportation distance data, inventory quantity data, quality inspection report and interface matching degree, select the optimal replacement component from the list of available replacement components according to the applicability score, and perform a node replacement operation in the assembly dependency network to replace the abnormal component node with the optimal replacement component node.

[0025] As a further scheme of the present application: in the S6, the specific process of recalculating the installation sequence is:

[0026] Obtain the full topology data of the assembly dependency network after replacing the node, extract the set of component nodes that need to be reordered from the affected assembly path report, collect the working radius data of the on-site hoisting equipment and the skill level data of the construction personnel, construct a multi-objective optimization model based on the node connection relationship, equipment working radius data and personnel skill level data, set the minimum construction period target, maximum resource utilization target and structure stability guarantee target, add hoisting equipment working range constraints, construction personnel skill matching constraints and component installation process sequence constraints, use a sequence generation algorithm to solve a candidate installation sequence that meets all constraint conditions, verify the consistency of the candidate installation sequence with the spatial coordinate data of the building information model, and output the component installation sequence that passes the verification.

[0027] As a further scheme of the present application: the process of constructing a multi-objective optimization model is:

[0028] Set the construction period minimization objective function, set the resource utilization maximization objective function, set the structure stability guarantee objective function, add hoisting equipment working range constraints, add construction personnel skill matching constraints, and add component installation process sequence constraints, collect on-site equipment state data to update equipment working range constraints, collect personnel attendance data to update skill matching constraints, and use a sequence generation algorithm to solve an installation sequence that meets all constraint conditions.

[0029] The present application also includes a BIM model-based prefabricated building intelligent management system for implementing the above-mentioned BIM model-based prefabricated building intelligent management method, comprising:

[0030] A multi-source data integration module is configured to obtain a BIM model of a fabricated building and component supply chain data, the BIM model comprising geometric properties, material properties and installation logical relationships of fabricated components, and the component supply chain data comprising production status, transportation location and storage information of the components;

[0031] An assembly topology modeling module is configured to construct an assembly dependency network based on the installation logical relationships, wherein nodes in the assembly dependency network represent individual components, and edges represent installation precedence dependency relationships between the components;

[0032] A supply chain anomaly monitoring module is configured to monitor the component supply chain data in real time and identify abnormal components in a production delay state or a transportation lag state;

[0033] An impact path analysis module is configured to locate a node corresponding to the abnormal component in the assembly dependency network, traverse in a direction of dependency edges in the assembly dependency network, determine a set of subsequent components dependent on the abnormal component, and form an affected assembly path;

[0034] A dynamic replacement decision module is configured to search for a replacement component having the same geometric properties and material properties as the abnormal component based on the storage information, replace the node corresponding to the abnormal component, and establish a connection relationship between the replacement component node and original adjacent nodes;

[0035] A sequence optimization generation module is configured to recalculate installation sequences of all components in the affected assembly path based on the replaced assembly dependency network, and generate a new component installation sequence.

[0036] Advantages of the present application:

[0037] The present application establishes a deep association between BIM model data and supply chain data by constructing an assembly dependency network, forming a unified data system of component properties, installation logic and supply chain status, and developing a dynamic impact evaluation mechanism based thereon to quickly locate the impact range of abnormal components and accurately identify affected assembly paths and their criticality. By integrating historical case libraries and real-time construction condition data, a multi-dimensional evaluation system including geometric matching, interface compatibility, transportation timeliness, inventory status and the like is established to achieve accurate screening and optimized matching of replacement components. The assembly sequence generated based on a multi-objective optimization algorithm not only satisfies installation logic constraints between components, but also fully considers actual site conditions such as hoisting equipment working range and construction personnel skill allocation to ensure the executability of the adjusted scheme. This data-driven decision-making method significantly improves the response speed and decision-making quality of the fabricated building management system when facing supply chain fluctuations, effectively avoids interruption of the overall assembly process caused by local component abnormalities, ensures the continuity and coordination of assembly operations, and provides reliable technical support for efficient implementation of fabricated buildings. BRIEF DESCRIPTION OF DRAWINGS

[0038] The application will be further described below with reference to the drawings.

[0039] Figure 1 is a flowchart of an intelligent management method for fabricated buildings based on a BIM model of the application;

[0040] Figure 2 is a module diagram of an intelligent management system for fabricated buildings based on a BIM model of the application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0042] Please refer to Figure 1 The application is an intelligent management method for fabricated buildings based on a BIM model, which comprises the following steps:

[0043] S1, obtaining a BIM model of a fabricated building and component supply chain data, the BIM model containing geometric properties, material properties and installation logical relationships of fabricated components, and the component supply chain data containing production status, transportation location and storage information of components;

[0044] S2, constructing an assembly dependency network based on the installation logical relationships, the nodes in the assembly dependency network representing single components, and the edges representing installation and dependency relationships between components;

[0045] S3, monitoring the component supply chain data in real time to identify abnormal components in a production delay state or a transportation lag state;

[0046] S4, locating the nodes corresponding to the abnormal components in the assembly dependency network, traversing in the direction of the dependency edges in the assembly dependency network, determining a set of subsequent components dependent on the abnormal components, and forming an affected assembly path;

[0047] S5, based on the storage information, retrieving alternative components with the same geometric properties and material properties as the abnormal components, and replacing the nodes corresponding to the abnormal components;

[0048] S6, based on the assembly dependency network after replacement, recalculating the installation order of all components in the affected assembly path to generate a new component installation sequence.

[0049] In the S2 of the present application, the specific process of constructing the assembly dependency network is:

[0050] The building information model is structurally parsed by the BIM model parsing engine, and the component installation logical relationship data is extracted. This process calls the built-in assembly sequence metadata of the model, identifies and extracts the installation sequence constraints between all components, including the constraint relationship between the pre-installed base components and the subsequent installed auxiliary components, and the relative order requirements of the components installed in the same batch. Each assembly component is mapped to a network node, and the installation sequence constraints between components are converted into directed edges, with the direction of the directed edges pointing from the first installed component to the second installed component, to establish an initial network structure.

[0051] According to the spatial cooperation requirements of the components, the spatial relationship data in the model is parsed by the geometry engine, and the minimum installation gap data and the maximum installation deviation data between the components are extracted. According to these two types of data, the strictness of the spatial interference relationship is quantified, when the minimum installation gap is less than the preset threshold or the maximum installation deviation limit is strict, it is determined that the spatial interference relationship is strict, and the weight value of the related directed edge is increased; when the minimum installation gap is large and the maximum installation deviation limit is loose, it is determined that the spatial interference relationship is low, and the weight value of the related directed edge is reduced.

[0052] A depth-first search algorithm is used to detect loops in the initial network structure, traverse all possible path combinations, and identify paths with circular dependencies. When a circular dependency path is found, the pre-set component installation logic priority parameter in the BIM model is called, which is based on the structural stability requirements and construction specifications, and the components with high priority are determined as pre-installed items to remove the circular dependency relationship and ensure that the network structure is a directed acyclic graph. The processed network structure is stored as an assembly dependency network in a graph database format, and the degree centrality data of each node, i.e. the number of connected edges, and the weight data of each edge are recorded, providing basic data support for subsequent influence path analysis.

[0053] In the S3 of the present application, the specific process of real-time monitoring of component supply chain data is:

[0054] A bidirectional data connection with the supply chain management system is established through a standardized API interface, and a time-triggered polling mechanism is used to obtain component production status data from the supply chain management system at fixed time intervals, including production progress percentage, current processing procedure, quality detection results, etc. At the same time, a data connection with the logistics tracking system is established through a logistics information interaction protocol, and based on Beidou or GPS positioning data, continuous receipt of component transportation location data is achieved, including real-time latitude and longitude, transportation tool identification, current transportation route, etc.

[0055] The production state monitoring condition is set, the standard update period of each production stage is determined based on the process time node in the component production plan, when the component production stage mark does not change within the standard update period, the system automatically marks that the component is in a production delay state. The transportation state monitoring condition is set, the electronic fence is constructed based on the preset transportation route planning data, when the real-time transportation position of the component continuously exceeds the range of the electronic fence, it is judged that the transportation trajectory deviates from the predetermined route, and the component is marked in a transportation lag state; at the same time, based on the speed limit standard and the planned driving speed of the transportation section, the transportation speed threshold is calculated, when the transportation speed of the component is lower than the transportation speed threshold for a plurality of continuous collection periods, the component is also marked in a transportation lag state.

[0056] The labeling results of the production delay state and the transportation lag state are subjected to multi-source data fusion processing, repeated labeling and false information are removed through data consistency verification, multi-dimensional abnormal states of the same component are combined, and finally an abnormal component list containing the unique identification of the component, the abnormal type and the abnormal occurrence time is generated, thereby providing accurate abnormal object data for subsequent impact analysis.

[0057] In the S4 of the present application, the specific process of determining the affected assembly path is:

[0058] First, abnormal component information extraction and node positioning are performed. The data analysis module is called from the abnormal component list to extract the type identification and installation stage information of the abnormal component. The type identification includes the component specification model code, material classification code and function attribute label, and the installation stage information covers the component construction subarea number, planned installation time node and belonging assembly flow section. Based on the component type identification and unique equipment identification code, the node index library of the assembly dependency network is matched, and the network node corresponding to the abnormal component is located through the node query interface of the graph database. At the same time, the associated node retrieval function is triggered to identify the support component nodes that directly provide support for the abnormal component in the installation logic, such nodes usually include the abnormal component's load base component, connection transition component and positioning reference component, and the retrieval result is temporarily stored in the form of node ID list.

[0059] Subsequently, the historical case library retrieval process is started. The historical case library adopts a distributed storage architecture and is indexed by component type identification. Each case record contains abnormal type code, alternative scheme technical parameters, adjustment process time consumption statistics, associated component influence range record, and processing effect evaluation data. Through the case retrieval module, the type identification of the abnormal component and the abnormal state code are used as the retrieval keywords to call the fuzzy matching algorithm to filter out the historical processing records with similar degrees meeting the threshold requirements. The alternative scheme data and adjustment time data are extracted from the records, including the alternative component model, the adaptation installation process, and the required auxiliary tool list. The adjustment time data includes component replacement time consumption, process connection waiting time, and quality recheck time consumption. The two types of data are stored in the temporary data buffer area.

[0060] Next, the connection characteristic analysis and adjustment time window calculation are carried out. The connection attribute data of the abnormal component and support component nodes in the assembly dependency network are read through the structural mechanics analysis module. The connection types are identified, including bolt connection, welding connection, mortise and tenon connection, and grouting connection. The preset connection structural mechanics parameters in the BIM model are extracted, including shear strength grade, tensile strength grade, and node stiffness coefficient. Based on these parameters, the connection strength is classified. Combined with the planned installation time nodes in the installation stage information and the current construction progress data, the time connection relationship between the current process and the subsequent process is calculated through the progress simulation module to determine the adjustment time window available for abnormal processing. The starting time of the window is the completion time of the current process, and the termination time is the earliest start time of the subsequent key process. The window length is directly related to the time constraint conditions of the adjustment operation.

[0061] In the path influence coefficient calculation link, first, the historical processing records of the same type of abnormal components are retrieved from the buffer area. Through the data cleaning module, invalid data and abnormal values are removed, and the adjustment time and alternative scheme details in each record are extracted, including the installation adaptability parameters of the replacement component and the influence degree on the associated components during the adjustment process. Through the mechanics characteristic analysis module, the mechanical transmission characteristics of different connection types are quantitatively analyzed, such as the elasticity buffer of bolt connection due to its detachable and replaceable nature, and the more direct and wider influence range of welding connection. Based on these characteristics, a mapping relationship library of component types and adjustment schemes is established to clearly define the preferred adjustment strategies corresponding to different component types.

[0062] The basic influence value is calculated based on the connection strength and mechanical transmission characteristics. The reference range of the basic influence value is determined according to the connection strength level. The higher the connection strength level of the node, the greater the possibility of abnormal transmission, and the reference range of the basic influence value is correspondingly improved. The reference range is modified in combination with the mechanical transmission characteristics. The connection type with high mechanical transmission efficiency will expand the abnormal influence range, and the modification coefficient of the basic influence value is correspondingly improved. Finally, the modified parameters are converted into the basic influence value through the grade mapping rule.

[0063] Meanwhile, the time influence factor is calculated in combination with the adjustment time window. The length of the adjustment time window is compared with the statistical value of the historical adjustment time through the time constraint analysis module. If the window length is less than the average value of the historical adjustment time, it means that the adjustment time is tight, and the time influence factor is high. If the window length is greater than the maximum value of the historical adjustment time, the time influence factor is low. If the window length is between the two, the time influence factor of the middle level is determined according to the difference ratio.

[0064] Finally, the basic influence value and the time influence factor are integrated through the weight fusion module. The weight distribution is set according to the current management priority of the project. If the current stage focuses on structural safety, the weight proportion of the basic influence value is increased. If the current stage focuses on progress guarantee, the weight proportion of the time influence factor is increased. The path influence coefficient is obtained through weighted fusion. After the path influence coefficient is calculated, the path sorting module is called to sort all the affected paths from high to low according to the coefficient value. The path with higher coefficient has more significant influence on the overall assembly progress and structural safety. Based on the sorting result, an affected assembly path report is generated. The report includes the component node sequence of each path, the influence degree rating of each node, the specific value of the path influence coefficient and the recommended processing priority.

[0065] In the S5 of the application, the specific process of searching and replacing is,

[0066] The geometric property data and material property data of the abnormal component are extracted from the model database. The geometric property data covers three-dimensional size parameters, shape feature parameters and interface topology structure data, and the material property data includes material category code, mechanical performance parameters and environmental adaptability indicators. Based on these data, the matching condition generation engine is started, the size tolerance and shape tolerance range is determined in combination with the geometric tolerance standard in the industry specification, the material category matching threshold and performance parameter deviation allowed range is set according to the material compatibility criterion, and these constraints are converted into structured alternative component matching conditions.

[0067] A query request is initiated to the warehouse management system through a standardized data interface, the interface uses an industrial Ethernet protocol to realize real-time data transmission, and the structured parameters of the geometric tolerance range and material requirements are encapsulated in the request data packet. The inventory management unit of each warehouse node receives the request, calls the local inventory retrieval algorithm, filters out the components that meet the matching conditions, generates a list of available alternative components and feeds back to the management system, the list includes the unique identification of the component, the warehouse node number and the basic attribute summary.

[0068] Based on the feedback list, the transportation distance of each alternative component from the warehouse node to the project site is calculated through the geographic information module, the real-time inventory quantity data of each alternative component is retrieved from the warehouse inventory account, and the corresponding quality inspection report is obtained from the quality traceability system, the report includes the factory detection data and the warehouse re-inspection result. Start the interface matching analysis module, use the three-dimensional geometric comparison algorithm to calculate the interface size matching degree of the alternative component and the adjacent component, evaluate the compatibility of the alternative component and the existing installation process through the process adaptability analysis module, and judge whether to add new tooling or adjust the process.

[0069] The transportation distance data, inventory quantity data, quality inspection report results and interface matching degree evaluation results are input into the applicability scoring module, which uses a weighted evaluation mechanism to allocate weights to each indicator according to the current management priority of the project, and calculates the applicability score of each alternative component. According to the scoring order, the optimal alternative component is selected, the assembly dependency network editing interface is called to perform node replacement operation, the geometric and material attribute data of the node are updated, the connection relationship between the node and the adjacent node is reconstructed, and the consistency of the network topology and attribute information after replacement is ensured.

[0070] In the S6 of the application, the specific process of recalculating the installation sequence is:

[0071] First, data collection and preprocessing are performed. Call the graph database interface of the assembly dependency network, extract the full topology data after replacing the node, which includes the geometric coordinates, installation time parameters, material characteristic labels of all nodes, and the dependency relationship type, weight value and priority code of the edge, the data is imported into the topology analysis module for structured storage in JSON format. At the same time, the path ordering results are retrieved from the affected assembly path report, the top N affected paths are filtered out according to the path influence coefficient, the component node set contained in these paths is extracted, and the repeated items are removed through the node deduplication algorithm to form the core node list that needs to be reordered, the list is associated with the installation prerequisites and subsequent dependent node information of the node to avoid efficiency loss caused by full node rearrangement.

[0072] The on-site data collection module is started, communication is established with the controller of the hoisting equipment through LoRa or 5G Internet of Things terminal, and the working radius data, rated lifting capacity, and current working condition parameters (such as boom angle, turning radius) of the equipment are obtained in real time. The data sampling frequency and the equipment state update frequency are kept synchronous to ensure the timeliness of the parameters. The skill level data of the on-site personnel is called from the construction personnel management system. The skill level is coded according to the national professional skill standard, covering hoisting command, component splicing, welding operation, and other special skill levels. At the same time, the current construction area and available working hours information of the personnel are associated to form a personnel skill-resource mapping table.

[0073] In the multi-objective optimization model construction link, first, the objective function is set. The construction period minimization objective function is based on the total installation working hours of all components in the core node list, combined with the process connection gap time and resource waiting time, and the goal is to shorten the total construction period to within the planned threshold. The resource utilization maximization objective function takes the effective operation time length ratio of the hoisting equipment, the matching degree of personnel skills and processes as the core index, and the goal is to reduce the equipment idle rate and personnel skill waste. The structure stability guarantee objective function is based on the structural mechanics analysis results of the BIM model, and the influence of component installation sequence on the overall structure stress distribution is used as the basis, and the goal is to preferentially install load-bearing components and key support components to avoid the risk of temporary structure instability.

[0074] Then add the constraint conditions. The hoisting equipment working range constraint condition is based on the equipment working radius data and the component installation coordinates, and through spatial geometric calculation, it is determined whether the equipment can cover the component installation position, and the installation sequence beyond the equipment working range is prohibited. The construction personnel skill matching constraint condition is based on the personnel skill-resource mapping table, and it is limited that a specific process must be executed by personnel with corresponding skill level and above, and personnel with unmatched skills are prohibited from being assigned to the scheme. The component installation process sequence constraint condition directly associates the dependency relationship of the edges of the assembly dependency network, and clearly states that the post-installed component must be executed after the pre-installed component is installed, and the sequence generation that violates the logical order is prohibited.

[0075] During the model construction process, the constraint parameters are updated in real time. Through the device state monitoring module, real-time working condition data of on-site equipment is collected, and if the equipment fails or the parameters are adjusted, the hoisting equipment working range constraint is immediately updated. Through the personnel attendance clock-in system, real-time attendance data of personnel is collected, and if personnel are absent or positions are adjusted, the construction personnel skill matching constraint is updated synchronously to ensure that the model is consistent with the actual conditions on site.

[0076] The model is solved by using a sequence generation algorithm, the algorithm is improved genetic algorithm or tabu search algorithm, the component installation sequence is taken as chromosome coding, and the objective function is taken as fitness function, and the sequence is continuously optimized in the iteration process. The algorithm firstly generates an initial installation sequence population, then selects, crosses and mutates to screen sequences meeting the constraint conditions, simultaneously introduces a local search strategy to optimize the key process sequence, and finally outputs multiple candidate installation sequences meeting the constraints.

[0077] A sequence verification link is started, a spatial collision detection engine of the BIM model is called, each component in the candidate installation sequence is simulated to be installed to the corresponding coordinate of the BIM model in sequence, the geometric interference of the component with the installed component, equipment and construction space is checked, and whether the installation sequence meets the structural stability requirement is verified. The candidate sequence with collision or stability risk is removed, the sequence passing the verification is retained, the optimal scheme meeting the three objective functions is selected from the sequence, and the optimal scheme is output as the final component installation sequence, and the output result comprises a component installation sequence, a corresponding equipment and personnel allocation scheme and a predicted completion time, thereby providing an execution basis for site construction.

[0078] Please refer to Figure 2 The application also includes a prefabricated building intelligent management system based on a BIM model, which is used for implementing the prefabricated building intelligent management method based on the BIM model, and comprises the following modules.

[0079] A multi-source data integration module is used to acquire the BIM model and component supply chain data of the prefabricated building, the BIM model comprises geometric properties, material properties and installation logical relationships of the prefabricated components, and the component supply chain data comprises production states, transportation positions and storage information of the components.

[0080] An assembly topology modeling module is used to construct an assembly dependency network according to the installation logical relationships, wherein a node in the assembly dependency network represents a single component, and an edge represents an installation sequence dependency relationship between components.

[0081] A supply chain anomaly monitoring module is used to monitor the component supply chain data in real time, and identify abnormal components in a production delay state or a transportation lag state.

[0082] An influence path analysis module is used to locate a node corresponding to the abnormal component in the assembly dependency network, traverse in the direction of a dependency edge in the assembly dependency network, determine a subsequent component set dependent on the abnormal component, and form an affected assembly path.

[0083] A dynamic replacement decision module is used to search for a replacement component with the same geometric properties and material properties as the abnormal component based on the storage information, replace the node corresponding to the abnormal component, and establish a connection relationship between the replacement component node and the original adjacent node.

[0084] a sequence optimization generation module, configured to recalculate the installation sequence of all components in the affected assembly path based on the replaced assembly dependency network, and generate a new component installation sequence.

[0085] The above describes one embodiment of the present application in detail, but the content is only the preferred embodiment of the present application, and cannot be considered to limit the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the patent scope of the present application.

Claims

1. A BIM model-based intelligent management method for fabricated buildings, characterized in that, The method comprises the following steps: S1, obtaining a BIM model of a prefabricated building and component supply chain data, the BIM model containing geometric properties, material properties and installation logical relationships of prefabricated components, and the component supply chain data containing production status, transportation location and storage information of the components; S2, constructing an assembly dependency network based on the installation logical relationships, wherein the nodes in the assembly dependency network represent individual components, and the edges represent the installation sequence dependency relationship between the components; S3, monitoring the component supply chain data in real time to identify abnormal components in a production delay state or a transportation lag state; S4, locating the node corresponding to the abnormal component in the assembly dependency network, traversing along the dependency edges in the assembly dependency network, determining a set of subsequent components dependent on the abnormal component, and forming an affected assembly path; S5, based on the storage information, retrieving a replacement component having the same geometric properties and material properties as the abnormal component, and replacing the node corresponding to the abnormal component; S6, based on the replaced assembly dependency network, recalculating the installation sequence of all components in the affected assembly path to generate a new component installation sequence. 2.The BIM model-based fabricated building intelligent management method according to claim 1, characterized in that, In S2, the specific process of constructing the assembly dependency network is as follows: Parse the component installation logical relationship in the building information model, extract the installation sequence constraint between the components, take the components as nodes and the installation sequence as directed edges, establish an initial network structure, analyze the minimum installation gap data and the maximum installation deviation data in the component space cooperation requirement, dynamically adjust the weight value of the edge according to the strictness of the space interference relationship, perform loop detection on the network structure, when a circular dependency path is found, resolve the circular dependency based on the priority setting in the component installation logical relationship, store the processed network structure as the assembly dependency network, and record the degree centrality data of the nodes and the weight data of the edges. 3.The BIM model-based fabricated building intelligent management method according to claim 1, characterized in that, In S3, the specific process of monitoring the component supply chain data in real time is as follows: Establish data connection with the supply chain management system, obtain component production status data at fixed time intervals, establish data connection with the logistics tracking system, continuously receive component transportation location data, set production status monitoring conditions, when the component production stage marker is not updated within the preset time period, mark the component as being in a production delay state, set transportation state monitoring conditions, when the component transportation trajectory deviates from the predetermined route, mark the component as being in a transportation lag state, when the component transportation speed is continuously lower than the standard speed, mark the component as being in a transportation lag state, and generate an abnormal component list through multi-source data fusion processing. 4.The BIM model-based fabricated building intelligent management method of claim 1, wherein, In S4, the specific process of determining the affected assembly path is as follows: The type identifier and installation stage information of the abnormal component are extracted from the abnormal component list, the abnormal component node and its directly connected support component nodes in the assembly dependency network are located, the processing case library of the same type abnormal component in the historical project is retrieved, the replacement scheme data and adjustment time data in the case library are obtained, the connection type and connection strength between the abnormal component and the support component are analyzed, the adjustment time window under the current construction progress is determined according to the installation stage information, the path influence coefficient is calculated combining the connection strength data and the adjustment time window, the affected paths are sorted based on the path influence coefficient, and the affected assembly path report is generated.

5. The BIM model-based fabricated building intelligent management method according to claim 4, characterized in that, The process of calculating the path influence coefficient is as follows: The processing records of the same type abnormal component in the historical case library are obtained, the adjustment time and replacement scheme in the processing records are extracted, the mechanical transmission characteristics of different connection types are analyzed, the corresponding relationship between the component type and the adjustment scheme is established, the basic influence value is calculated according to the connection strength and the mechanical transmission characteristics, the time influence factor is calculated combining the adjustment time window under the current construction progress, and the path influence coefficient is obtained by comprehensively considering the basic influence value and the time influence factor.

6. The BIM model-based fabricated building intelligent management method according to claim 1, characterized in that, In S5, the specific process of searching and replacing is as follows: The geometric property data and material property data of the abnormal component are obtained, the replacement component matching condition is generated based on the geometric tolerance range and material compatibility requirement, the query request containing the geometric tolerance and material requirement is sent to the warehouse management system, the available replacement component list returned by each warehouse node is received, the transportation distance data, inventory quantity data and quality inspection report of each replacement component are obtained, the interface size matching degree and installation process compatibility of each replacement component with the adjacent node are analyzed, the applicability score is calculated combining the transportation distance data, inventory quantity data, quality inspection report and interface matching degree, the optimal replacement component is selected from the available replacement component list according to the applicability score, and the node replacement operation is performed in the assembly dependency network to replace the abnormal component node with the optimal replacement component node.

7. The BIM model-based fabricated building intelligent management method according to claim 1, characterized in that, In S6, the specific process of recalculating the installation sequence is as follows: The full topology data of the assembly dependency network after the replacement node is obtained, the component node set that needs to be reordered is extracted from the affected assembly path report, the working radius data of the on-site hoisting equipment and the skill level data of the construction personnel are collected, a multi-objective optimization model is constructed based on the node connection relationship, equipment working radius data and personnel skill level data, the construction cycle minimization target, resource utilization maximization target and structure stability guarantee target are set, the hoisting equipment working range constraint, construction personnel skill matching constraint and component installation process sequence constraint are added, the sequence generation algorithm is used to solve the candidate installation sequence that meets all the constraint conditions, the consistency of the candidate installation sequence with the spatial coordinate data of the building information model is verified, and the component installation sequence that passes the verification is output.

8. The BIM model-based fabricated building intelligent management method according to claim 7, characterized in that, The process of constructing the multi-objective optimization model is as follows: A construction period minimization objective function is set, a resource utilization rate maximization objective function is set, a structure stability guarantee objective function is set, a hoisting equipment working range constraint condition is added, a construction personnel skill matching constraint condition is added, a component installation process sequence constraint condition is added, field equipment state data is collected to update the equipment working range constraint, personnel attendance data is collected to update the skill matching constraint, and a sequence generation algorithm is used to solve an installation sequence that satisfies all constraint conditions. 9.A BIM model-based intelligent management system for fabricated buildings, used to implement the BIM model-based intelligent management method for fabricated buildings according to any one of claims 1-8. It comprises: a multi-source data integration module for obtaining a BIM model of a fabricated building and component supply chain data, the BIM model containing geometric properties, material properties and installation logical relationships of fabricated components, and the component supply chain data containing production status, transportation location and storage information of components; an assembly topology modeling module for constructing an assembly dependency network based on the installation logical relationships, nodes in the assembly dependency network representing individual components and edges representing installation precedence dependency relationships between components; a supply chain anomaly monitoring module for monitoring the component supply chain data in real time and identifying abnormal components in a production delay state or a transportation lag state; an influence path analysis module for locating nodes corresponding to abnormal components in the assembly dependency network, traversing in the direction of dependency edges in the assembly dependency network, determining a set of subsequent components dependent on the abnormal components, and forming affected assembly paths; a dynamic replacement decision module for retrieving replacement components having the same geometric properties and material properties as the abnormal components based on the storage information, replacing nodes corresponding to the abnormal components, and establishing connection relationships between the replacement component nodes and original adjacent nodes; a sequence optimization generation module for recalculating installation sequences of all components in the affected assembly paths based on the replacement assembly dependency network, and generating a new component installation sequence.

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