A data collaboration method based on BIM 3D4D5D technology

Abstract

The application discloses a data cooperation method based on BIM3D4D5D technology and relates to the technical field of building digital management, which comprises the following steps: collecting multi-source heterogeneous data, pre-processing the data, and constructing a BIM three-dimensional component information model; performing one-to-one corresponding mapping between the BIM three-dimensional component information model and construction schedule data, and constructing a BIM four-dimensional component schedule model; utilizing component-level time sequence evolution logic and a cost dynamic adjustment mechanism to perform multi-scheme construction simulation on the BIM five-dimensional component information model, and outputting a dynamic BIM five-dimensional component state sequence; inputting the dynamic BIM five-dimensional component state sequence into a data cooperation processing flow, utilizing a multi-dimensional information consistency checking mechanism to perform space-time consistency judgment and semantic conflict detection on component modification information of different types, and outputting a component modification opinion set. The application improves the foresight of a construction scheme, the scientificity of management decision, and the data integrity and precision of a BIM five-dimensional model.

Description

Technical Field

[0001] This invention relates to the field of building digital management technology, and in particular to a data collaboration method based on BIM 3D 4D 5D technology. Background Technology

[0002] In the development of Building Information Modeling (BIM) technology, 3D modeling (3D BIM) has become a fundamental tool for architectural design, construction, and operation and maintenance. With increasing demands for construction management, 4D and 5D BIM technologies are gradually being applied, respectively linking the time and cost dimensions with 3D component models to achieve visualization and traceability of construction progress simulation and budget management. In existing research and practice, BIM 3D, 4D, and 5D technologies mainly focus on the 3D visualization of component information, the temporal simulation of construction schedules, and the static integration of cost budgets. These technologies can assist construction units in resource scheduling and cost analysis during the planning phase and reduce the risk of construction conflicts through visualization. Simultaneously, existing BIM technologies have initially demonstrated certain automation and intelligence characteristics in areas such as multi-source data fusion, construction scheme optimization, and component status tracking, providing important technical support for the digital management of building information.

[0003] Existing BIM 3D, 4D, and 5D technologies still have certain limitations in terms of collaborative updating of component information and multi-scheme construction simulation. Although construction progress and cost data can be mapped to 3D models, existing methods lack a unified and efficient data collaboration process in areas such as multi-source heterogeneous data processing, dynamic evolution of component states, and spatiotemporal consistency verification of modification information. This may lead to delayed component information updates or failure to identify semantic conflicts in a timely manner during multi-scheme construction simulation and dynamic adjustment, thus limiting the intelligent application of BIM 5D models in construction decision support. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a data collaboration method based on BIM 3D 4D 5D technology to solve the problems of dynamic collaborative updating and semantic conflict of component information in a multi-source heterogeneous data environment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a data collaboration method based on BIM 3D 4D 5D technology, which includes: collecting multi-source heterogeneous data, performing preprocessing, and constructing a BIM three-dimensional component information model;

[0008] By mapping the BIM 3D component information model to the construction schedule data in a one-to-one correspondence, a BIM 4D component schedule model is constructed.

[0009] By integrating and mapping component cost budget data with the BIM four-dimensional component schedule model, a BIM five-dimensional component information model is constructed.

[0010] By utilizing component-level temporal evolution logic and cost dynamic adjustment mechanism, multi-scheme construction simulation is performed on the BIM five-dimensional component information model, and dynamic BIM five-dimensional component state sequence is output.

[0011] The dynamic BIM five-dimensional component status sequence is input into the data collaborative processing flow. Using the multi-dimensional information consistency verification mechanism, spatiotemporal consistency judgment and semantic conflict detection are performed on the modification information of different types of components, and a component modification opinion set is output.

[0012] The BIM five-dimensional component information model is iteratively updated based on the component modification feedback set, and the updated BIM five-dimensional component information model is output.

[0013] As a preferred embodiment of the data collaboration method based on BIM3D4D5D technology described in this invention, the multi-source heterogeneous data includes building geometry and attribute data, construction schedule data, and component cost budget data.

[0014] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the preprocessing step includes the following specific steps:

[0015] Clean and correct anomalies in the building geometry and attribute data, and output a preliminary normalized dataset;

[0016] Based on the initially normalized dataset, perform unified conversion and standardization of format and fields to output a structured standard data table;

[0017] The structured standard data table is standardized in terms of units and timestamps, and combined with semantic mapping and entity disambiguation, a standardized component data table is output.

[0018] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the specific steps for constructing the BIM three-dimensional component information model are as follows:

[0019] Import the geometric information from the standardized component data table into the modeling engine and perform coordinate analysis, shape reconstruction and dimension calibration to generate the corresponding component geometric entity;

[0020] The attribute fields are retrieved from the standardized component data table to perform parameter binding, category matching, and semantic injection on the component geometric entities, forming a collection of component objects with semantic annotations;

[0021] The system performs spatial location calculations, adjacency relationship identification, and hierarchical structure organization on a collection of component objects, and outputs a BIM three-dimensional component information model.

[0022] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the specific steps for mapping the BIM three-dimensional component information model to construction schedule data to construct a BIM four-dimensional component schedule model are as follows:

[0023] The work items in the construction schedule data are decomposed and structured to extract task identifiers, work categories, start and end times and constraints, and then summarized to form a construction task list.

[0024] Match the process categories and time attributes in the construction task list with the component categories, identifiers, and spatial locations in the BIM 3D component information model to create a component index table;

[0025] Define a one-to-one correspondence between the process category field in the construction task list table and the component category field in the component index table, establish a category mapping rule table, and filter by combining spatial location and construction zoning information to form a mapping relationship between components and tasks;

[0026] By using mapping relationships, component objects are bound to construction tasks in the time dimension, and a BIM four-dimensional component progress model is output.

[0027] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the specific steps for integrating and mapping component cost budget data with the BIM four-dimensional component progress model to construct a BIM five-dimensional component information model are as follows:

[0028] The component cost budget data is cleaned, anomalies are corrected, and fields are standardized. Each cost data entry is assigned a unique identifier consistent with the component object in the BIM four-dimensional component schedule model, and a structured multidimensional cost data table is output.

[0029] By combining the structured multidimensional cost data table with the component index table, each cost data entry is matched with the corresponding component object, and combined with the construction task list, a dynamic cost mapping matrix is ​​formed.

[0030] Inject cost constraints into the component object, analyze the construction path, cost accumulation and budget deviation, and output the component cost analysis results;

[0031] In the time dimension, the dynamic cost mapping matrix is ​​bound to the component cost analysis results and the BIM four-dimensional component schedule model in terms of geometry, attributes, time and cost, and outputs the BIM five-dimensional component information model.

[0032] As a preferred embodiment of the data collaboration method based on BIM3D4D5D technology described in this invention, the step of utilizing component-level temporal evolution logic and dynamic cost adjustment mechanism to perform multi-scheme construction simulation on the BIM five-dimensional component information model and output a dynamic BIM five-dimensional component state sequence, the specific steps are as follows:

[0033] Model the operational behavior of each component in the BIM five-dimensional component information model and output a component behavior database.

[0034] Based on the component behavior database and multi-dimensional uncertainty factor sampling, multiple construction simulation scenarios are generated.

[0035] Each component is treated as an independent agent to execute the operation logic according to the parameters of the multi-scheme construction simulation scenario, and the state is exchanged through the event bus to output the simulation trajectory set;

[0036] Dynamic cost correction, deviation analysis, and comparison of construction task completion status are performed on the simulated trajectory set and actual on-site observation data to output component cost and schedule analysis results.

[0037] The component cost and schedule analysis results are bound to the BIM five-dimensional component information model in the time dimension with multiple attributes, and a component state sequence is generated according to the construction sequence, outputting a dynamic BIM five-dimensional component state sequence.

[0038] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the multidimensional uncertainty factor sampling refers to distributed sampling of the main uncertain factors affecting the progress during the construction process; the main uncertain factors include schedule fluctuations, resource prices, and equipment failures.

[0039] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the following steps are taken: The dynamic BIM five-dimensional component state sequence is input into the data collaboration processing flow; a multi-dimensional information consistency verification mechanism is used to perform spatiotemporal consistency judgment and semantic conflict detection on different types of component modification information; and a component modification opinion set is output.

[0040] The dynamic BIM five-dimensional component state sequence is input into the data collaborative processing flow, and the component geometric attributes, time dimension and cost parameters are deconstructed in a layered manner in the distributed memory cache layer to generate a multi-dimensional component state cache set.

[0041] Based on the multidimensional state cache set of components and combined with feedback from the construction site, different types of component modification information are extracted, and the spatiotemporal consistency of the component modification information is judged to output a preliminary consistency result set.

[0042] The initial consistency result set is input into the semantic parsing engine for semantic hierarchical mapping, and a cross-dimensional semantic comparison mechanism is used to identify logical conflicts, constraint contradictions and resource overlaps, and output semantic conflict detection results.

[0043] The semantic conflict detection results are subjected to multi-role interactive deduction, and conflicting entries are automatically merged and replaced to generate a set of component modification opinions.

[0044] As a preferred embodiment of the data collaboration method based on BIM 3D 4D 5D technology described in this invention, the updated BIM five-dimensional component information model is obtained by applying the component modification opinion set to the BIM five-dimensional component information model and iteratively adjusting the geometric attributes, construction progress, and cost execution status of each component in the BIM five-dimensional component information model.

[0045] The beneficial effects of this invention are as follows: by performing component-level temporal evolution and multi-scheme construction simulation on the BIM five-dimensional component information model, dynamic prediction and optimization of component status, progress and cost during construction are realized. At the same time, the dynamic status sequence is input into the multi-dimensional information consistency verification process to detect spatiotemporal and semantic conflicts in component modification information, thereby achieving efficient collaborative updating of component information, thereby improving the foresight of construction schemes, the scientific nature of management decisions and the data integrity and accuracy of the BIM five-dimensional model. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of a data collaboration method based on BIM 3D 4D 5D technology.

[0048] Figure 2 This is a flowchart of the data preprocessing steps.

[0049] Figure 3 Flowchart for building a BIM 4D component progress model.

[0050] Figure 4 Flowchart for building a five-dimensional component information model for BIM. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0054] Reference Figure 1 This is one embodiment of the present invention, which provides a data collaboration method based on BIM 3D 4D 5D technology. Please refer to [link / reference]. Figure 2 This includes the following steps:

[0055] S1 collects heterogeneous data from multiple sources, performs preprocessing, and constructs a BIM 3D component information model.

[0056] It should be noted that multi-source heterogeneous data includes building geometry and attribute data, construction schedule data, and component cost budget data.

[0057] Building geometry and attribute data include the spatial coordinates, geometric shape, dimensional parameters, and attribute information such as component category, material, and function.

[0058] Construction schedule data includes work items, task identifiers, work categories, start and end times, constraints, and construction zone information.

[0059] The component cost budget data includes the component unit price, material cost, labor cost, machinery cost, total cost, and a unique identifier corresponding to the component object.

[0060] S1.1 cleans and corrects anomalies in the building geometry and attribute data, and outputs a preliminary normalized dataset.

[0061] It should be noted that the building geometry and attribute data are cleaned and anomaly corrected by identifying duplicate entries, missing values, inconsistent formats, and abnormal attribute values ​​in the building geometry and attribute data. Duplicate entries are merged, missing values ​​are filled by referring to example values ​​of adjacent component attributes or construction specification texts, inconsistent formats are standardized in terms of fields and units, and abnormal attribute values ​​are corrected to a reasonable range, resulting in a preliminary standardized dataset.

[0062] S1.2, based on the initially normalized dataset, performs unified conversion and standardization of format and fields, and outputs a structured standard data table.

[0063] It should be noted that the field names in the preliminary standardized dataset are uniformly named, the architectural geometry and attribute data fields from different sources are mapped to the standard field set according to a unified standard, the numerical type fields are uniformly formatted, including numerical representation, character encoding and unit format, and the category fields are standardized and encoded, and finally the structured standard data table is output.

[0064] S1.3 unifies the units and aligns the timestamps of the structured standard data table, and combines semantic mapping and entity disambiguation to output a standardized component data table.

[0065] It should be noted that unit unification and timestamp alignment are performed on structured standard data tables. By identifying the unit type of various numerical fields in the structured standard data tables, fields such as length, area, and volume are converted into unified units, and time fields are standardized according to a unified time format, so that timestamps such as start and end times, creation times, and update times are aligned under the same benchmark.

[0066] By using semantic mapping, the component categories, attribute names, and functional descriptions in the structured standard data table are uniformly encoded and standardized. Then, through entity disambiguation, component objects that may be duplicated or conflicting are compared and merged to output a standardized component data table.

[0067] S1.4 Import the geometric information from the standardized component data table into the modeling engine and perform coordinate analysis, shape reconstruction and dimension calibration to generate the corresponding component geometric entity.

[0068] It should be noted that the geometric information in the standardized component data table is imported into the modeling engine. By reading the spatial coordinates, contour points and boundary curves in the standardized component data table, the geometry is reconstructed and calibrated according to the component category and size parameters. The dimensional attributes such as the length, width, height and thickness of the component are adjusted and confirmed, and the corresponding component geometric entity is generated to ensure that the geometric entity fully reflects the spatial and dimensional information in the standardized component data table.

[0069] It should also be noted that the modeling engine is a software tool used to generate and represent the geometric entities of three-dimensional components. By supporting the import of spatial coordinates and dimensional parameters from standardized component data tables, it reconstructs the component outline, boundary, and cross-sectional shape into operable three-dimensional geometric objects. The modeling engine is obtained through existing three-dimensional modeling software.

[0070] S1.5, retrieve attribute fields from the standardized component data table, perform parameter binding, category matching, and semantic injection on the component geometric entities, and form a set of component objects with semantic annotation.

[0071] It should be noted that the attribute fields are called from the standardized component data table, the corresponding component geometric entities are parameter bound, the category information in the standardized component data table is matched with the geometric features of the component geometric entities, and semantic annotations are injected into the component geometric entities in combination with the component's function, material and purpose, so that each component geometric entity has complete geometric attributes and semantic information, and finally a set of component objects with semantic annotations is formed.

[0072] S1.6 performs spatial location calculation, adjacency relationship identification, and hierarchical structure organization on the set of component objects, and outputs a BIM three-dimensional component information model.

[0073] It should be noted that spatial location calculation involves reading the coordinate information of the geometric entities of the components in the component object set, determining the center point position, orientation, and rotation state of each component geometric entity in three-dimensional space, marking the specific position of the component in the overall building space, ensuring that the spatial positioning between the components is accurate and consistent, and providing a basis for the identification of adjacent relationships and the organization of hierarchical structures.

[0074] Adjacent Relationship Identification: The boundary surfaces, contact surfaces, and spatial distances of the geometric entities of each component in the component object set are analyzed to identify the close proximity, hierarchical, and dependency relationships between components. Based on the component category and functional attributes, possible connection or constraint relationships are determined, thereby establishing an adjacency relationship table between components in the component object set.

[0075] Hierarchical structure organization: Based on component category, functional attributes, and architectural space hierarchy, components in the component object collection are grouped and arranged according to parent-child relationship or partition hierarchy to form a multi-level structure, where the component nodes of the upper level are the parent items and the component nodes of the lower level are the child items, and the level number and association relationship are marked; realizing the hierarchical management and logical organization of the component object collection, providing support for the complete generation of BIM 3D component information model.

[0076] The system integrates a set of component objects that have completed spatial location calculation, adjacency relationship identification, and hierarchical structure organization. It then encapsulates each component geometric entity and its bound attributes, categories, semantic annotations, spatial locations, and hierarchical relationships to form a set of architectural information with complete geometric structure, semantic information, and hierarchical organization.

[0077] S2 involves mapping the BIM 3D component information model to the construction schedule data to create a one-to-one correspondence, thus constructing a BIM 4D component schedule model. For details, please refer to [link / reference needed]. Figure 3 .

[0078] S2.1 Decompose and structure the work items in the construction schedule data, extract task identifiers, work categories, start and end times and constraints, and summarize them to form a construction task list.

[0079] It should be noted that the process items in the construction schedule data are decomposed and structured. By reading each process item in the construction schedule data, the information such as task name, task number, execution process and construction stage in the process item is split into independent fields, including process category field, start and end time field and constraint condition field.

[0080] The process category field is categorized and labeled, such as distinguishing between civil engineering, installation, and decoration; the start and end time fields are formatted and standardized to ensure that the start and end times of all processes are represented under the same time base; the constraint field is extracted, including information such as prerequisite processes, construction sequence constraints, and resource limitations; the split task identifiers, process categories, start and end times, and constraints are summarized and organized in a unified format to form a construction task list.

[0081] It should be noted that the process entries in the construction schedule data are obtained by reading and parsing the construction schedule file. Each process entry contains information on the construction stage, task name, process number, execution process, and related constraints. By decomposing the entries in the text or spreadsheet, the detailed information of each process is extracted into independent entries, forming a set of basic process entries that can be used for structured processing of the construction task list.

[0082] S2.2 Match the process categories and time attributes in the construction task list with the component categories, identifiers, and spatial locations in the BIM 3D component information model to create a component index table.

[0083] It should be noted that the process category and start / end time attributes in the construction task list are matched with the component category, component identifier, and spatial location in the BIM 3D component information model. By determining the correspondence between the process category in the construction task list and the component category in the BIM 3D component information model, component identifiers with the same category are associated with the construction task. At the same time, the construction sequence and area constraints are determined based on the spatial location of the components in the BIM 3D component information model. The matching results are then compiled into a component index table.

[0084] It should also be noted that the process category and start and end time attributes in the construction task list are obtained by decomposing and structuring the process items in the construction schedule data. Specifically, this includes extracting the process category information of each process item, such as civil engineering, installation or decoration, as well as extracting the start and end time fields of the process items and representing the time fields in a unified format, ultimately forming a structured attribute that can be used for component matching and schedule management.

[0085] S2.3 Define the one-to-one correspondence between the process category field in the construction task list table and the component category field in the component index table, establish a category mapping rule table, and filter it in combination with spatial location and construction zoning information to form a mapping relationship between components and tasks.

[0086] It should be noted that the process categories in the construction task list table are matched item by item with the component categories in the component index table to generate a preliminary category mapping rule table.

[0087] The spatial location and construction zone information of each component object in the BIM 3D component information model are read. The preliminary category mapping rule table is filtered to remove matching entries that do not conform to the spatial location or construction zone. The matching results are verified and sorted to finally form a mapping relationship between components and tasks, so that each component object can be completely and accurately matched to a specific construction task.

[0088] S2.4 uses mapping relationships to bind component objects to construction tasks in the time dimension, and outputs a BIM four-dimensional component progress model.

[0089] It should be noted that by utilizing the mapping relationship between components and tasks, the component objects in each BIM 3D component information model are associated with the corresponding construction tasks in the construction task list according to the mapping relationship. In the time dimension, the start and end times of the construction tasks are bound to the component objects, and the start time, end time and construction stage status of each component object in the construction process are recorded. At the same time, information such as component category, component identifier and spatial location are retained to obtain the bound component object set.

[0090] The bound set of component objects is sorted according to component category and construction task order. The spatial location, component identifier, construction task identifier and corresponding start and end time of each component object are summarized to form a component object record table arranged according to the construction timeline.

[0091] During the aggregation process, we check whether there are any time overlaps or conflicts in the construction tasks of each component object, and mark and adjust the component objects with conflicts. At the same time, we supplement the constraints and zoning information of the construction tasks accordingly, and integrate the time, space and task information of all component objects into a complete BIM four-dimensional component progress model.

[0092] S3 integrates and maps component cost budget data with the BIM 4D component schedule model to construct a BIM 5D component information model. For details, please refer to [link / reference]. Figure 4 .

[0093] S3.1 cleans, corrects anomalies, and standardizes fields for component cost budget data, and assigns a unique identifier to each cost data entry that is consistent with the component object in the BIM four-dimensional component schedule model, outputting a structured multidimensional cost data table.

[0094] It should be noted that duplicate entries, incomplete entries, and unreasonable values ​​in the component cost budget data should be screened, duplicate entries should be deleted and incomplete entries should be completed, and unreasonable values ​​should be corrected by comparing with the reference market price range or historical budget information; the field names and field units of the component cost budget data should be standardized to ensure that the fields such as material costs, labor costs, machinery costs and management costs are consistent in terms of format and measurement units.

[0095] After completing field standardization, each component cost budget data entry in the component cost budget data is assigned a unique identifier that is completely consistent with the component object in the BIM four-dimensional component schedule model. The unique identifier is then bound to the corresponding component category, component identifier, spatial location, construction task, and cost information, and all entries are organized into a structured multidimensional cost data table.

[0096] S3.2 Combine the structured multidimensional cost data table with the component index table, match each cost data entry with the corresponding component object, and combine this with the construction task list to form a dynamic cost mapping matrix.

[0097] It should be noted that, based on the unique identifier of each component cost budget data entry in the structured multidimensional cost data table, it is compared one by one with the component category, component identifier, and spatial location in the component index table to ensure that each component cost budget data entry can accurately correspond to the component object in the component index table.

[0098] After matching the unique identifier with the component object, the material costs, labor costs, machinery costs and management costs included in the component cost budget data entries in the structured multidimensional cost data table are then bound to the corresponding component object.

[0099] The matched component cost budget data entries are combined with the construction task list. By using the task identifier, process category, and start and end time in the construction task list, the construction process information of the component object is synchronously associated with the component cost budget data entries.

[0100] After completing the multi-dimensional integration, the matching results are organized into a dynamic cost mapping matrix according to the multi-dimensional correspondence between component objects, construction tasks, and component cost budget data.

[0101] S3.3 Injects cost constraints into the component object, analyzes the construction path, cost accumulation and budget deviation, and outputs the component cost analysis results.

[0102] It should be noted that the material costs, labor costs, machinery costs, and management costs corresponding to the component objects in the structured multidimensional cost data table are used as cost constraints and bound to the unique identifier of the component objects to ensure that the component objects are consistent in terms of construction progress and cost.

[0103] Extract the process categories, start and end times, and constraints from the construction task list, and combine them with the component object categories, identifiers, and spatial locations in the BIM four-dimensional component progress model to progressively analyze the construction path, identify the construction sequence of different component objects in the time dimension, and the construction area distribution of component objects in the spatial dimension.

[0104] Based on the cost constraints corresponding to the component objects, the cost values ​​of each component object are cumulatively calculated according to the construction path to generate the cost accumulation results for different construction stages.

[0105] By comparing the phased cost accumulation results of component objects under the construction path with the target cost value in the budget constraints, the difference between the actual cost and the budget target is analyzed, the overspending or saving status at each time node is identified, and the component cost analysis results including the construction path, cost accumulation and budget deviation are output in combination with the construction path.

[0106] It should also be noted that budget constraints can be obtained from the planned schedule and phased budget targets in the construction organization design, structured multidimensional cost data table, and task list. These include the target cost value and allowable deviation range at each time node, which serve as a benchmark for comparing the cumulative cost results.

[0107] S3.4, in the time dimension, binds the dynamic cost mapping matrix with the component cost analysis results and the BIM four-dimensional component schedule model through geometric, attribute, time and cost multi-dimensional attributes, and outputs the BIM five-dimensional component information model.

[0108] It should be noted that the dynamic cost mapping matrix is ​​correlated with the component cost analysis results one by one. The material cost, labor cost, machinery cost and management cost in the dynamic cost mapping matrix are matched with the construction path, cost accumulation and budget deviation in the component cost analysis results through the unique identifier of the component object, so that the cost constraints are consistent with the actual performance of the construction process.

[0109] The completed dynamic cost mapping matrix and component cost analysis results are then used to perform multi-dimensional matching of geometric attributes, construction task attributes, and time attributes of component objects in the BIM four-dimensional component schedule model.

[0110] In the geometric dimension, by comparing the spatial coordinates, geometric shape, and size parameters of component objects in the BIM four-dimensional component progress model with the corresponding entries in the dynamic cost mapping matrix and component cost analysis results, the spatial location, shape characteristics, cost information, and construction path of component objects in the geometric dimension are kept consistent.

[0111] In terms of attributes, by uniformly binding the category information, material information, and functional information of component objects in the BIM four-dimensional component progress model with the corresponding category descriptions and cost constraints in the dynamic cost mapping matrix and component cost analysis results, the component objects have a complete description in the attribute dimension.

[0112] In the time dimension, by reading the start and end times and construction stage information in the BIM four-dimensional component progress model, the cost value changes in the dynamic cost mapping matrix and the cost accumulation and budget deviation in the component cost analysis results are synchronized according to the time series, so that the start time, end time and construction stage status of each component object in the construction progress are consistent with the cost evolution process.

[0113] In terms of cost, by binding the dynamic cost mapping matrix and the cost items in the component cost analysis results to the component objects in the BIM four-dimensional component schedule model one by one, a unified component description with geometric information, attribute information, time information and cost information is formed, and finally the BIM five-dimensional component information model is output.

[0114] S4 utilizes component-level temporal evolution logic and cost dynamic adjustment mechanism to perform multi-scheme construction simulation on the BIM five-dimensional component information model and outputs dynamic BIM five-dimensional component state sequence.

[0115] S4.1 performs operational behavior modeling for each component in the BIM five-dimensional component information model and outputs a component behavior database.

[0116] It should be noted that, based on the geometric, attribute, time, and cost information of components in the BIM five-dimensional component information model, the construction procedures, methods, and steps of the components are analyzed item by item. The work actions, sequence, and duration of the work involved in the construction process are decomposed and classified according to the different situations of the component objects in terms of spatial location, component category, construction stage, and cost constraints.

[0117] By establishing a correspondence between work actions and the geometric and temporal attributes of components in the BIM five-dimensional component information model, the spatial operation actions, construction procedure execution time periods, and resource consumption of each component are mapped, so that the work behavior of each component can form a unified expression in the geometric, temporal, and cost dimensions.

[0118] The results of the above-mentioned operational behavior modeling are organized one by one by component to form a component behavior database containing component operation actions, operation sequence, operation duration and resource consumption.

[0119] S4.2, based on the component behavior database and multi-dimensional uncertainty factor sampling, generates multiple construction simulation scenarios.

[0120] It should be noted that, based on the component operation actions, operation sequence, operation duration and resource consumption contained in the component behavior database, the operation logic of the construction process of each component is extracted to ensure that the component construction process has a complete description in the geometric, time and cost dimensions.

[0121] Based on this, and combined with the sampling results of multidimensional uncertain factors, the fluctuations in the construction environment, changes in resource supply, and differences in work efficiency in the sampling results of multidimensional uncertain factors are matched item by item with the component operation actions, operation sequence, operation duration, and resource consumption in the component behavior database. By randomly selecting and combining the sampled values, a component construction execution sequence under different uncertain conditions is formed.

[0122] For the construction execution sequence formed by the component behavior database and the sampling results of multidimensional uncertain factors, under the condition of satisfying the logical constraints of construction procedures and spatial location constraints, different uncertain factor values ​​are matched and combined with component operation actions, duration and resource consumption in various ways to generate several construction processes with differences in operation sequence, duration and resource allocation, thereby obtaining multiple sets of construction schemes with differentiated construction characteristics, and specifying the start and end time of component construction, construction path and cost consumption in each set of construction schemes.

[0123] Multiple construction schemes are mapped into the simulation environment to form a multi-scheme construction simulation scenario that includes multi-dimensional operational logic and multi-dimensional uncertain conditions.

[0124] It should also be noted that multidimensional uncertainty sampling refers to distributed sampling of the main uncertainties affecting the progress during the construction process; among which the main uncertainties include schedule fluctuations, resource prices, and equipment failures.

[0125] The specific steps are as follows: Establish a probability distribution of the duration of construction tasks based on the fluctuation of the construction period, and perform multiple samplings in the probability distribution of the duration of construction tasks to generate different values ​​of the construction period.

[0126] Establish probability distributions for material costs and labor costs based on resource prices, and then randomly sample from these probability distributions to form different cost levels.

[0127] Based on equipment failures, a probability distribution of equipment operational reliability is established, and sampling is performed within this probability distribution to obtain possible downtime situations that may occur during construction. The above sampling results constitute a sampling result set that includes three types of uncertain factors: project schedule fluctuations, resource prices, and equipment failures.

[0128] S4.3 treats each component as an independent agent to execute the operation logic according to the parameters of the multi-scheme construction simulation scenario, and exchanges states through the event bus to output the simulation trajectory set.

[0129] It should be noted that the operation actions, operation sequence and operation duration corresponding to each component are extracted from the component behavior database, the construction stage and resource constraint information are obtained from the construction task list, and the parameters are combined with the schedule fluctuation, resource price and equipment failure status in the multi-dimensional uncertainty factor sampling results to form a complete parameter configuration for the multi-scheme construction simulation scenario.

[0130] Based on the complete parameter configuration of the multi-scheme construction simulation scenario, the operation actions, operation sequence, operation duration and resource consumption of the component corresponding to the component are loaded into the independent agent (independent intelligent agent) from the component behavior database.

[0131] During the execution of job logic by independent agents, state exchange between independent agents is realized through the event bus. Specifically, when each independent agent completes a job action or enters a new job stage, it publishes the job status, resource usage, and progress information to the event bus, and other independent agents can obtain updated information through the event bus, thereby realizing dynamic sharing of job progress and resource status in a multi-agent environment.

[0132] By continuously recording the changes in the operational status of all independent agents, a simulation trajectory reflecting the evolution of the component construction process is generated. The simulation trajectory includes elements such as the start time, end time, duration, resource consumption curve, and progress status changes of the component.

[0133] The simulation trajectories generated by all independent agents are summarized and organized to output a complete set of simulation trajectories.

[0134] It should also be noted that the event bus refers to a communication channel used to achieve information transmission and state synchronization in a multi-agent environment. By publishing the work status, resource usage, and progress information of each independent agent to the event bus during the construction simulation process, and having other independent agents subscribe to and obtain the information, state exchange and collaboration between agents can be achieved. The event bus can be built using existing distributed message middleware or publish-subscribe mechanisms, such as using message queue software or event-driven architecture, to transmit and centrally manage the state information of independent agents in the form of event messages, thereby obtaining the event bus in the construction simulation process.

[0135] It should be noted that in existing technologies, construction simulation is usually carried out in a centralized or simplified agent-based manner. The operation logic of components or work units is mostly executed in a sequential or batch manner. Status information is centrally stored or synchronized through simple message exchange. The simulation process has limited ability to respond to construction uncertainties, insufficient ability to simulate multiple construction schemes and cost-schedule joint analysis, and lacks dynamic collaboration between components and binding of full-dimensional attributes.

[0136] This invention treats each component in the BIM five-dimensional component information model as an independent intelligent agent, executes operation logic according to the parameters of a multi-scheme construction simulation scenario, and realizes real-time state exchange between intelligent agents through an event bus. During the process, the geometric information of the component, construction tasks, time schedule and cost constraints are considered simultaneously to form a complete simulation trajectory set that can reflect the evolution of the component's operation state under multi-scheme construction conditions.

[0137] This invention achieves full-dimensional, distributed collaborative simulation of component operations through multi-dimensional attribute combination, multi-scheme construction simulation, and dynamic state exchange via event bus. The simulation trajectory set simultaneously records geometric position, time progress, cost accumulation, and construction path information, significantly improving the granularity of construction simulation, dynamic response capability, and analysis completeness compared to existing technologies.

[0138] S4.4 performs dynamic cost correction, deviation analysis, and comparison of construction task completion status between the simulation trajectory set and actual on-site observation data, and outputs component cost and schedule analysis results.

[0139] It should be noted that the time progress, cost accumulation, construction path and operation status information of each component object in the simulation trajectory set under the multi-scheme construction simulation scenario are extracted, and the construction progress, resource consumption and completion status of the corresponding component object in the actual on-site observation data are also extracted.

[0140] The cumulative cost data in the simulation trajectory set is compared item by item with the actual cost information in the actual field observation data to calculate the cost deviation, expressed as follows:

[0141] ;

[0142] In the formula, It is a component object At the time point Cost deviation; It is a component object At the time point The cumulative cost of the simulation trajectory set, such as the sum of material costs, labor costs, machinery costs and management costs; It is a component object At the time point Actual cost information from actual on-site observation data, such as the sum of actual material costs, labor costs, machinery costs, and management fees; It is the index of the component object. It is an index of a time node.

[0143] The cost distribution of component objects is dynamically corrected based on the deviation. The completion status of construction tasks in the simulation trajectory set is compared with the actual task completion status observed on site to identify construction tasks that are delayed, advanced, or have abnormal sequence.

[0144] After completing cost correction and status comparison, the construction progress, cost accumulation, budget deviation and construction task completion status of each component object are integrated to generate component cost and schedule analysis results that reflect the multi-dimensional construction execution.

[0145] It should also be noted that the actual on-site observation data was obtained by deploying sensors at the construction site, collecting construction equipment operation records, construction personnel operation logs, and construction quality inspection records. At the same time, the actual construction progress, resource consumption, construction status, and completion status of each component object were compiled by combining construction monitoring videos, drone inspections, and on-site measurement results.

[0146] S4.5 binds the component cost and schedule analysis results with the BIM five-dimensional component information model in the time dimension with multiple attributes, generates a component state sequence according to the construction sequence, and outputs a dynamic BIM five-dimensional component state sequence.

[0147] It should be noted that the construction progress, cost accumulation, deviation, and task completion status of each component object in the component cost and schedule analysis results should be matched one-to-one with the geometric attributes, spatial location, and construction task information of the corresponding component object in the BIM five-dimensional component information model.

[0148] Using the construction sequence as a constraint, the time progress of the component object is combined with the corresponding dynamic cost change curve to generate a component status record arranged in time sequence.

[0149] The status of component objects is updated at each time point, including the construction completion rate, real-time cost, cumulative deviation, and spatial location changes, so that the component objects in the BIM five-dimensional component information model can dynamically reflect the evolution of multi-dimensional information during the construction process.

[0150] By integrating the time-series state information of all component objects under the construction sequence, a dynamic BIM five-dimensional component state sequence can be formed, which can dynamically display the changes in construction progress and cost.

[0151] S5 inputs the dynamic BIM five-dimensional component status sequence into the data collaborative processing flow. Using the multi-dimensional information consistency verification mechanism, it performs spatiotemporal consistency judgment and semantic conflict detection on the modification information of different types of components, and outputs a set of component modification opinions.

[0152] It should be noted that existing technologies mainly discover conflicts through geometric collision detection, comparison of progress and cost software, and static attribute checks based on rule bases. These methods are often based on a single dimension, and the results rely on manual review. They lack the ability to automatically identify and process multidimensional spatiotemporal consistency and semantic conflicts.

[0153] This invention takes the dynamic BIM five-dimensional component state sequence as input, and through multi-dimensional joint comparison of geometry, attributes, time and cost, it uses timestamp alignment, semantic mapping and cross-dimensional diagnosis to identify spatiotemporal and semantic conflicts, and combines automated merging and replacement strategies to generate a component modification opinion set, so as to achieve dynamic feedback throughout the process.

[0154] The advantages of this invention lie in the combination of multi-dimensional joint verification, dynamic temporal judgment, semantic disambiguation and automated suggestion generation. Compared with existing technologies, it can more comprehensively discover cross-dimensional conflicts, reduce manual intervention, improve the accuracy and efficiency of modification suggestions, and achieve rapid optimization and reliable correction of the construction process through closed-loop integration.

[0155] S5.1 inputs the dynamic BIM five-dimensional component state sequence into the data collaborative processing flow, and performs hierarchical deconstruction of component geometric attributes, time dimension and cost parameters in the distributed memory cache layer to generate a multi-dimensional component state cache set.

[0156] It should be noted that the dynamic BIM five-dimensional component state sequence is deconstructed in a distributed memory cache layer, the component geometric attributes are split and stored according to spatial coordinates, shape and size parameters, the time dimension is indexed according to timestamp order, and the cost parameters are mapped and stored according to the cumulative cost value and cost deviation in the component cost and schedule analysis results.

[0157] By establishing a multidimensional correspondence between geometric attributes, time dimension, and cost parameters through a distributed memory caching layer, we can ensure that each attribute remains consistent in both the spatiotemporal and cost dimensions.

[0158] The geometric attributes, time dimension, and cost parameters after hierarchical deconstruction and mapping are combined in a unified manner to output a multidimensional state cache set of components.

[0159] S5.2 Based on the multi-dimensional state cache set of components and combined with feedback from the construction site, extract different types of component modification information, perform spatiotemporal consistency judgment on the component modification information, and output a preliminary consistency result set.

[0160] It should be noted that component modification information is extracted from the component multidimensional state cache set according to component identifier, including spatial coordinates and geometry, current construction stage timestamp and time series, cumulative cost and cost deviation, and the most recent construction status update. The on-site observation records, on-site change instructions, quality inspection records and resource usage reports in the construction site feedback are matched item by item with the component modification information extracted from the component multidimensional state cache set.

[0161] The extracted component modification information is classified into four categories: geometric changes, schedule adjustments, cost adjustments, and status updates. Spatiotemporal consistency judgment is performed on each type of component modification information. The spatiotemporal consistency judgment includes timestamp alignment and time window matching, spatial location tolerance comparison, construction sequence and pre-constraint verification, and cost-schedule correlation constraint check.

[0162] The spatiotemporal consistency judgment results are summarized by component identifier and output as a preliminary consistency result set.

[0163] S5.3 inputs the preliminary consistency result set into the semantic parsing engine, performs semantic hierarchical mapping, and uses a cross-dimensional semantic comparison mechanism to identify logical conflicts, constraint contradictions and resource overlap issues, and outputs semantic conflict detection results.

[0164] It should be noted that semantic hierarchical mapping is performed on each component modification information in the preliminary consistency result set, specifically including the mapping of attribute names to standard codes, the normalization of term synonyms and entity links, and the annotation of hierarchical relationships between component categories and attributes.

[0165] By using a cross-dimensional semantic comparison mechanism, the mapped geometric change descriptions, time change descriptions, and cost change descriptions are compared item by item. Based on rule-based semantic verification and ontology knowledge base detection of logical consistency, logical conflicts such as violation of prerequisite relationships, constraint contradictions such as space occupancy conflicts, and resource overlap such as the same resource being occupied by multiple tasks in overlapping time windows are identified.

[0166] For each conflict item, supporting evidence is collected, including the time window in which the conflict was triggered, the identifiers of the components involved, the differences between conflict attribute fields and related values, and the conflict items are classified and sorted according to conflict type and scope of impact, and the semantic conflict detection results are output.

[0167] S5.4 performs multi-role interactive deduction on the semantic conflict detection results, and automatically merges and replaces conflicting entries to generate a set of component modification suggestions.

[0168] It should be noted that, based on the logical conflicts, constraint contradictions, and resource overlaps involved in the conflict entries, the semantic conflict detection results are divided into different conflict entries.

[0169] By matching each type of conflict item with the responsible parties involved in the construction process, for example, the schedule conflict item in the semantic conflict detection results corresponds to the construction scheduling role, the resource conflict item in the semantic conflict detection results corresponds to the equipment management role and the material supply role, and the constraint conflict item in the semantic conflict detection results corresponds to the quality supervision role.

[0170] Using the timing, resources, and constraints of conflict items as inference variables, the processing paths that multiple roles may take in actual construction operations are mapped to the inference scenario, thereby forming a multi-role inference environment that covers the logic of different roles. Dynamic reasoning is carried out through the operation paths that different roles may take in the construction process to analyze the feasibility and impact of conflict items in terms of timing execution, resource allocation, and constraints.

[0171] During the simulation, conflicting items are compared according to the role logic to identify cases of logical repetition, overlapping conditions, and conflicting constraints. An automated merging operation is then triggered to merge conflicting items of the same nature or with similar impact into a unified item. At the same time, conflicting items that cannot be satisfied simultaneously are replaced, and alternative solutions that are more in line with the construction sequence and resource coordination are retained. After completing the multi-role interactive simulation and the automated merging and replacement of conflicting items, a set of component modification opinions is output.

[0172] S6, based on the component modification feedback set, iteratively update the BIM five-dimensional component information model and output the updated BIM five-dimensional component information model.

[0173] It should be noted that the component modification suggestion set should be mapped to the component entities involved in the BIM five-dimensional component information model one by one, and the geometric attributes, construction progress and cost execution status that need to be adjusted should be identified based on the content of the component modification suggestion set.

[0174] At the geometric attribute level, the shape, size, and spatial location of the components are iteratively updated; at the construction progress level, the construction time nodes and task completion percentages of the components are iteratively adjusted; and at the cost execution status level, the budgeted cost and actual cost information of the components are gradually corrected.

[0175] While ensuring consistency in geometric properties, construction progress, and cost execution status, the adjustment results are reintegrated to form an updated BIM five-dimensional component information model that includes the latest status.

[0176] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the data collaboration method based on BIM3D4D5D technology proposed in the above embodiment.

[0177] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0178] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the data collaboration method based on BIM3D4D5D technology as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0179] In summary, this invention achieves dynamic prediction and optimization of component status, progress, and cost during construction by performing component-level temporal evolution and multi-scheme construction simulation on the BIM five-dimensional component information model. At the same time, the dynamic state sequence is input into the multi-dimensional information consistency verification process to detect spatiotemporal and semantic conflicts in component modification information, thereby achieving efficient collaborative updating of component information. This improves the foresight of construction schemes, the scientific nature of management decisions, and the data integrity and accuracy of the BIM five-dimensional model.

[0180] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data collaboration method based on BIM 3D 4D 5D technology, characterized in that: include: Collect heterogeneous data from multiple sources, preprocess it, and construct a BIM 3D component information model; By mapping the BIM 3D component information model to the construction schedule data in a one-to-one correspondence, a BIM 4D component schedule model is constructed. By integrating and mapping component cost budget data with the BIM four-dimensional component schedule model, a BIM five-dimensional component information model is constructed. By utilizing component-level temporal evolution logic and a dynamic cost adjustment mechanism, multi-scheme construction simulations are performed on the BIM five-dimensional component information model, outputting a dynamic BIM five-dimensional component state sequence. The specific steps are as follows: Model the operational behavior of each component in the BIM five-dimensional component information model and output a component behavior database. Based on the component behavior database and multi-dimensional uncertainty factor sampling, multiple construction simulation scenarios are generated. Each component is treated as an independent agent to execute the operation logic according to the parameters of the multi-scheme construction simulation scenario, and the state is exchanged through the event bus to output the simulation trajectory set; Dynamic cost correction, deviation analysis, and comparison of construction task completion status are performed on the simulated trajectory set and actual on-site observation data to output component cost and schedule analysis results. The component cost and schedule analysis results are bound to the BIM five-dimensional component information model in the time dimension with multiple attributes, and a component status sequence is generated according to the construction sequence, outputting a dynamic BIM five-dimensional component status sequence. The dynamic BIM five-dimensional component status sequence is input into the data collaborative processing flow. Using a multi-dimensional information consistency verification mechanism, spatiotemporal consistency judgment and semantic conflict detection are performed on the modification information of different types of components, and a component modification opinion set is output. The specific steps are as follows: The dynamic BIM five-dimensional component state sequence is input into the data collaborative processing flow, and the component geometric attributes, time dimension and cost parameters are deconstructed in a layered manner in the distributed memory cache layer to generate a multi-dimensional component state cache set. Based on the multidimensional state cache set of components and combined with feedback from the construction site, different types of component modification information are extracted, and the spatiotemporal consistency of the component modification information is judged to output a preliminary consistency result set. The initial consistency result set is input into the semantic parsing engine for semantic hierarchical mapping, and a cross-dimensional semantic comparison mechanism is used to identify logical conflicts, constraint contradictions and resource overlaps, and output semantic conflict detection results. The semantic conflict detection results are subjected to multi-role interactive deduction, and conflicting entries are automatically merged and replaced to generate a set of component modification opinions; The BIM five-dimensional component information model is iteratively updated based on the component modification feedback set, and the updated BIM five-dimensional component information model is output.

2. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 1, characterized in that: The multi-source heterogeneous data includes building geometry and attribute data, construction schedule data, and component cost budget data.

3. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 1, characterized in that: The preprocessing process involves the following steps: Clean and correct anomalies in the building geometry and attribute data, and output a preliminary normalized dataset; Based on the initially normalized dataset, perform unified conversion and standardization of format and fields to output a structured standard data table; The structured standard data table is standardized in terms of units and timestamps, and combined with semantic mapping and entity disambiguation, a standardized component data table is output.

4. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 1, characterized in that: The specific steps for constructing the BIM 3D component information model are as follows: Import the geometric information from the standardized component data table into the modeling engine and perform coordinate analysis, shape reconstruction and dimension calibration to generate the corresponding component geometric entity; The attribute fields are retrieved from the standardized component data table to perform parameter binding, category matching, and semantic injection on the component geometric entities, forming a collection of component objects with semantic annotations; The system performs spatial location calculations, adjacency relationship identification, and hierarchical structure organization on a collection of component objects, and outputs a BIM three-dimensional component information model.

5. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 4, characterized in that: The specific steps for mapping the BIM 3D component information model to the construction schedule data to construct the BIM 4D component schedule model are as follows: The work items in the construction schedule data are decomposed and structured to extract task identifiers, work categories, start and end times and constraints, and then summarized to form a construction task list. Match the process categories and time attributes in the construction task list with the component categories, identifiers, and spatial locations in the BIM 3D component information model to create a component index table; Define a one-to-one correspondence between the process category field in the construction task list table and the component category field in the component index table, establish a category mapping rule table, and filter by combining spatial location and construction zoning information to form a mapping relationship between components and tasks; By using mapping relationships, component objects are bound to construction tasks in the time dimension, and a BIM four-dimensional component progress model is output.

6. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 5, characterized in that: The specific steps for integrating and mapping component cost budget data with the BIM four-dimensional component schedule model to construct a BIM five-dimensional component information model are as follows: The component cost budget data is cleaned, anomalies are corrected, and fields are standardized. Each cost data entry is assigned a unique identifier consistent with the component object in the BIM four-dimensional component schedule model, and a structured multidimensional cost data table is output. By combining the structured multidimensional cost data table with the component index table, each cost data entry is matched with the corresponding component object, and combined with the construction task list, a dynamic cost mapping matrix is ​​formed. Inject cost constraints into the component object, analyze the construction path, cost accumulation and budget deviation, and output the component cost analysis results; In the time dimension, the dynamic cost mapping matrix is ​​bound to the component cost analysis results and the BIM four-dimensional component schedule model in terms of geometry, attributes, time and cost, and outputs the BIM five-dimensional component information model.

7. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 1, characterized in that: The multidimensional uncertainty factor sampling refers to the distributed sampling of the main uncertainties affecting the progress during the construction process; the main uncertainties include schedule fluctuations, resource prices, and equipment failures.

8. The data collaboration method based on BIM 3D 4D 5D technology as described in claim 1, characterized in that: The updated BIM five-dimensional component information model is obtained by applying the component modification suggestion set to the BIM five-dimensional component information model and iteratively adjusting the geometric attributes, construction progress, and cost execution status of each component in the BIM five-dimensional component information model.

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