BIM and cloud computing-based prefabricated building engineering information data management method

Abstract

This invention relates to the field of engineering management technology, specifically to a method for managing information data in prefabricated building projects based on BIM and cloud computing. The method includes: determining the scope of component information for the design, production, transportation, construction, and operation and maintenance phases based on BIM model data; wherein the BIM model data includes the geometric parameters and attribute information of building components at each phase; performing layered analysis of the component information based on the cloud computing platform and the scope of component information to obtain component information layers for each phase; assigning corresponding target identification codes to each component information layer to obtain a standardized component information database; wherein the target identification code is a unique code representing the data structure of the building component. This invention can promptly detect deviations between the model and the site before or during construction, avoiding rework caused by problems only being exposed after installation in traditional methods, reducing on-site rectification workload, and improving the project acceptance rate.

Description

Technical Field

[0001] This invention relates to the field of engineering management technology, specifically to a method for managing information and data of prefabricated building projects based on BIM and cloud computing. Background Technology

[0002] Currently, in traditional prefabricated construction project management, BIM models in the design phase, component information in the production phase, logistics data in the transportation phase, and on-site inspection data in the construction phase are usually stored in different systems or documents, without a unified association matrix or cloud-based index table. During construction, when discrepancies are found between the light steel integrated housing modules and the actual site conditions, such as deviations in bolt connection node positions or conflicts in component elevations, hoisting has often already been completed, necessitating chiseling, reinforcement, or rework. This reactive approach not only increases material waste and labor costs but may also affect structural safety and project schedule. Furthermore, traditional methods lack real-time conflict detection and quality assessment mechanisms, making it impossible to simultaneously compare model and on-site data before or during construction, leading to delayed problem identification and increased on-site rectification workload.

[0003] Furthermore, in traditional methods, the installation sequence of components is usually predetermined based on the construction organization design or the experience of site management personnel, and is rarely dynamically adjusted once determined. Actual deviations that occur during construction, such as temporary storage yard occupation, interference within the tower crane's slewing range, and changes in the order of component arrival, often cannot be reflected in the installation sequence in a timely manner, leading to frequent secondary handling, waiting, or temporary changes on site, reducing construction efficiency. At the same time, traditional methods do not record structural monitoring data such as strain, temperature, and vibration during construction, nor do they transmit this data back for BIM model updates. Therefore, the model remains in its initial design state and cannot reflect the true structural response, such as the impact of low temperatures on bolted connection nodes. Subsequent similar working conditions still use the original parameters, posing potential quality risks. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for managing information data of prefabricated building projects based on BIM and cloud computing, comprising:

[0005] Obtain BIM model data for the entire lifecycle of prefabricated building projects, and determine the scope of component information for the design, production, transportation, construction, and operation and maintenance phases based on the BIM model data; wherein, the BIM model data includes the geometric parameters and attribute information of building components at each stage;

[0006] Based on the cloud computing platform and the scope of component information, the component information is parsed in layers to obtain component information layers at each stage; corresponding target identification codes are assigned to each component information layer to obtain a standardized component information database; among them, the target identification code is a unique code that represents the data structure of building components;

[0007] A target association matrix is ​​established based on a standardized component information database. The target association matrix is ​​used to record the association between component identifiers in the standardized component information database and cloud storage addresses. The target association matrix is ​​used to perform multi-source data fusion processing on BIM model data and construction site data to obtain target project data.

[0008] The target project data is input into the cloud-based collaborative management engine, which then performs conflict detection and quality assessment based on the data to obtain project evaluation results. The optimal component installation sequence is determined based on the project evaluation results, and the installation of the optimal component installation sequence is simulated to obtain simulation results.

[0009] The optimal component installation sequence is optimized based on the simulation results to obtain the target component installation sequence, and the on-site construction of prefabricated buildings is guided based on the target component installation sequence; the actual installation data and structural monitoring data during the construction process are recorded, and the cloud-based collaborative management engine is updated based on the actual installation data and structural monitoring data.

[0010] Preferably, BIM model data for the entire lifecycle of prefabricated building projects is obtained, and the scope of component information for the design, production, transportation, construction, and operation and maintenance phases is determined based on the BIM model data, including:

[0011] Perform full lifecycle data initialization operations so that when the project starts, global control flags are set to control the calling of component information management modules at each stage, and BIM model data for the entire lifecycle of prefabricated building projects are obtained.

[0012] Determine the number of data records corresponding to the number of component information at each stage in the BIM model data;

[0013] The scope of component information for each stage is determined based on the number of data records, the size of a single record, and the starting number of the component information for each stage in the BIM model data.

[0014] Preferably, a target association matrix is ​​established based on a standardized component information database, including:

[0015] The cloud access index of each stage of component information layer in the standardized component information database is used as the starting row of the matrix, and an initial association matrix is ​​created using the starting row of the matrix.

[0016] The starting number of each stage of component information in the BIM model data is used as the starting component identifier, and the starting component identifier is incremented based on the number of each stage of component information in the BIM model data to obtain the component identifier sequence.

[0017] The component identifier sequence is used as the matrix index, and the target association matrix is ​​established based on the initial association matrix.

[0018] Preferably, before inputting the target project data into the cloud-based collaborative management engine, the following steps are also included:

[0019] Data space for each construction node is allocated based on the target association matrix, and corresponding target node identifiers are set; wherein, the target node identifier points to the registered project root node;

[0020] Define the data synchronization granularity for each construction node and bind it to a set of operation functions for cloud data; the set of operation functions includes conflict handling functions.

[0021] Define the target interaction interface for the management side, so as to obtain the data query requests sent by the management side using the target interaction interface.

[0022] Preferably, the target project data is obtained by multi-source data fusion processing of BIM model data and construction site collected data using a target correlation matrix, including:

[0023] Obtain multi-source data retrieval requests, and based on the multi-source data retrieval requests and the target association matrix, determine the corresponding component number and cloud storage path; where the component number is a unique identifier for the component information, and the cloud storage path is the storage location of the cloud data;

[0024] A cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths;

[0025] Based on the multi-source data retrieval request, the corresponding target storage path is selected from the cloud index table so that BIM model data and construction site data can be obtained from the location corresponding to the target storage path for subsequent multi-source data fusion processing.

[0026] Preferably, based on multi-source data retrieval requests and the target association matrix, the corresponding cloud storage path is determined, including:

[0027] Based on the number offset of the target component corresponding to the multi-source data retrieval request, the corresponding target component identifier is determined;

[0028] Filter the target storage addresses corresponding to the target component identifiers from the target association matrix;

[0029] Use the target storage address to determine the corresponding cloud storage path.

[0030] Preferably, a cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths, including:

[0031] Determine the range of component numbers corresponding to multi-source data retrieval requests;

[0032] If the number range and the corresponding cloud storage path meet the preset batch synchronization conditions, then a cloud index table is constructed.

[0033] Preferably, after determining the number range of component numbers corresponding to the multi-source data retrieval request, the method further includes:

[0034] Determine the data space size of the component information and the starting component number from the number range;

[0035] Determine whether the data space size of the component information meets the preset batch synchronization conditions, and determine whether the starting component number meets the preset batch synchronization conditions;

[0036] Determine the cloud storage path corresponding to the starting component number from the target association matrix;

[0037] Determine whether the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions;

[0038] If the data space size of the component information meets the preset batch synchronization conditions, and the starting component number meets the preset batch synchronization conditions, and the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions, then it is determined that the number range and the cloud storage path corresponding to the number range meet the preset batch synchronization conditions.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] (1) This invention establishes a target association matrix and a cloud index table to structurally associate and batch synchronize BIM model data of each stage of design, production, transportation, construction and operation with real-time data collected on the construction site, such as installation elevation and vibration monitoring. The cloud collaborative management engine uses the fused target project data to perform conflict detection and quality assessment, which can promptly detect the deviation between the model and the site before or during construction, avoiding the rework caused by the exposure of problems after installation in the traditional way, reducing the amount of on-site rectification work and improving the project acceptance rate.

[0041] (2) This invention records the actual installation data and structural monitoring data during the construction process and sends them back to the cloud collaborative management engine for model updates, so that the BIM model can reflect the real structural response, such as the changes of bolt connection nodes in low temperature environment; at the same time, based on the engineering evaluation results, the optimal component installation sequence is determined by genetic algorithm, and potential collisions such as interference of scaffold wall attachments within the slewing radius of tower crane are discovered by installation simulation. After optimization, the target component installation sequence is obtained; this sequence guides on-site hoisting and stacking, avoids secondary handling and temporary adjustments, can shorten the construction cycle of a single standard floor, and improve the feasibility and safety of the component installation sequence. Attached Figure Description

[0042] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating the steps for determining the range of component information at each stage in one embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the steps for establishing a target correlation matrix in one embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1, please refer to Figure 1 This invention provides a technical solution: a method for managing information data of prefabricated building projects based on BIM and cloud computing, comprising:

[0047] S1. Obtain BIM model data for the entire lifecycle of prefabricated building projects, and determine the scope of component information for the design, production, transportation, construction, and operation and maintenance stages based on the BIM model data; wherein, the BIM model data includes the geometric parameters and attribute information of building components at each stage.

[0048] S2. Based on the cloud computing platform and the scope of component information, the component information is parsed in layers to obtain component information layers at each stage; corresponding target identification codes are assigned to each component information layer to obtain a standardized component information database; among them, the target identification code is a unique code that represents the data structure of building components;

[0049] S3. Establish a target association matrix based on the standardized component information database; the target association matrix is ​​used to record the association between component identifiers in the standardized component information database and cloud storage addresses; the target association matrix is ​​used to perform multi-source data fusion processing on BIM model data and construction site collected data to obtain target project data;

[0050] S4. Input the target project data into the cloud-based collaborative management engine so that the cloud-based collaborative management engine can perform conflict detection and quality assessment based on the target project data to obtain the project assessment results; determine the optimal component installation sequence based on the project assessment results, and perform installation simulation on the optimal component installation sequence to obtain the simulation results;

[0051] S5. Optimize the optimal component installation sequence based on the simulation results to obtain the target component installation sequence, and guide the on-site construction of prefabricated buildings based on the target component installation sequence; record the actual installation data and structural monitoring data during the construction process, and update the model of the cloud-based collaborative management engine based on the actual installation data and structural monitoring data.

[0052] It should be noted that the residential project adopts a prefabricated light steel structure, with a total construction area of ​​approximately 50,000 square meters, including 3 high-rise residential buildings and 1 supporting commercial building. The project team first collected complete BIM model data for the design, production, transportation, construction, and operation and maintenance phases during the project initiation phase. This data covers the geometric parameters of building components at each stage, such as the length, width, thickness, steel strength grade, light steel keel composite wall panel, light steel keel specifications and layout, as well as attribute information such as component type, manufacturer, manufacturing date, transportation route, and installation location coordinates.

[0053] Based on a cloud computing platform, the project team performed layered analysis of the BIM model data according to the defined scope of component information. In the design phase, the component information layer includes the component's 3D geometry, spatial location, structural stress parameters, and material properties. In the production phase, the component information layer adds component fabrication drawings, production batches, mold parameters, and quality inspection reports. In the transportation phase, the component information layer supplements the information with transport vehicle information, delivery time, estimated arrival time, and real-time GPS trajectory. In the construction phase, the component information layer records the hoisting sequence, temporary support settings, bolt connection nodes, and measured deviations. In the operation and maintenance phase, the component information layer presets maintenance cycles, inspection records, and equipment asset numbers.

[0054] Each layer of information is assigned a target identification code, which follows the project's unified data dictionary specifications. For example, it uses a combination of floor number, component type, and serial number to ensure that each building component has a unique structured code throughout its entire life cycle. These codes, together with the corresponding layer information, constitute a standardized component information database.

[0055] The target association matrix uses the component identification code as the row index and the cloud storage address as the column index, recording the storage paths of model files, acceptance forms, inspection images, and sensor data generated at different stages for each type of component. For example, the identification code of a precast exterior wall panel corresponds to the address of the RVT model file in the design stage, the address of the quality certificate PDF in the production stage, the address of the temperature and humidity data uploaded by the vehicle terminal in the transportation stage, the address of the laser scanning point cloud data in the construction stage, and the address of the deformation monitoring data in the operation and maintenance stage.

[0056] Using this association matrix, the team performed multi-source data fusion processing on the original BIM model data and real-time data collected at the construction site. The site data included the placement of light steel integrated housing modules obtained through drone oblique photography, the trajectory of tower crane hooks obtained through UWB positioning technology, the worker operation time recorded by smart safety helmets, and the location of embedded parts verified by steel keel detectors. After connecting these data with the storage addresses in the association matrix, target project data covering both the virtual model and the actual state was automatically generated.

[0057] The target project data is input into a collaborative management engine deployed on Alibaba Cloud. Based on the IFC standard and the CDE public data environment framework, the engine performs conflict detection and quality assessment on the data. For example, the engine found a 12mm elevation conflict between the installation elevation marked in the BIM model of a precast landing slab in a stairwell and the actual finished surface of the heavy steel structure on site. Simultaneously, the engine assessed that the bolt connection node of a precast composite beam on the third floor showed less than 80% fullness in ultrasonic testing, classifying it as a quality risk. Based on these results, the engine uses a genetic algorithm to generate a set of feasible component installation sequences, from which the optimal sequence is selected. This sequence prioritizes the installation of shear wall panels, which have a greater impact on verticality, followed by the installation of composite beams, light steel keel composite floor slabs, air conditioning panels, and balcony panels in sequence. The installation of the conflicting landing slab is postponed until the adjacent vertical components are adjusted.

[0058] The project's technical lead used a VR visualization terminal to simulate the optimal component installation sequence. During the simulation, it was discovered that installing a precast column in a certain corridor section in the original order would cause a secondary collision with the temporarily stacked electromechanical pipeline supports on the transport channel. Simultaneously, the simulation showed that when the tower crane was lifting a certain heaviest exterior wall panel, there were already installed cantilevered scaffolding wall attachments within its swing radius, posing a risk of scraping. Based on these simulation results, the team optimized the sequence: advancing the installation of the electromechanical pipeline supports before the column's lifting, changing the exterior wall panel's lifting path to a detour, and adjusting the tower crane's lifting sequence to avoid the wall attachments; ultimately, the target component installation sequence was obtained.

[0059] The on-site construction management platform pushes the installation sequence of target components to the handheld terminals of the work teams and the smart tablets in the tower crane cabs. Each component has a QR code sprayed on its prefabricated surface. After scanning the code, the corresponding installation steps, technical briefing videos, allowable deviation values, and acceptance standards can be displayed. During construction, quality inspectors upload the actual installation data in real time using laser rangefinders and total stations. The structural health monitoring system continuously records the stress and deformation of components through pre-embedded strain gauges and inclinometers. These actual installation data, such as the verticality deviation of each wall panel, the elevation error of each beam, and structural monitoring data, are all transmitted back to the cloud-based collaborative management engine.

[0060] The engine uses this new data to update the existing BIM model, correct the actual geometric pose of the components, and retrain the weight parameters in the quality assessment algorithm.

[0061] In one optional embodiment, BIM model data of the entire lifecycle of the prefabricated building project is acquired, and the scope of component information for the design, production, transportation, construction, and operation and maintenance phases is determined based on the BIM model data, including:

[0062] S101. Perform full lifecycle data initialization operations so that when the project starts, a global control flag is set to control the calling of the component information management module at each stage, and the BIM model data of the prefabricated building project throughout its entire lifecycle is obtained.

[0063] S102. Determine the number of data records corresponding to the number of component information at each stage in the BIM model data;

[0064] S103. Determine the scope of component information for each stage based on the number of data records, the size of a single record, and the starting number of the component information for each stage in the BIM model data.

[0065] It should be noted that prefabricated housing projects perform full lifecycle data initialization during the startup phase. A new project instance is created in the cloud-based collaborative management platform, and a global control flag is set. This global control flag is a binary bitmask variable named GlobalCtrlFlag, with an initial value of 0x1F. The least significant bit is the control bit for the design phase, the next least significant bit is the control bit for the production phase, and so on, corresponding to the transportation, construction, and operation and maintenance phases, respectively. When a bit is 1, it indicates that the component information management module for the corresponding phase is activated, allowing calls to the relevant data read / write and service interfaces. All phases are activated during project initialization, hence the flag is 0x1F.

[0066] The BIM model data for the entire lifecycle of the prefabricated building project is obtained from the enterprise-level BIM database. This data is obtained by parsing the IFC file and the associated component-level attribute table, which contains complete records of all 50,000 building components from design to operation and maintenance. Each component record contains at least geometric parameters such as length, width, height, volume, and center of gravity coordinates, as well as attribute information such as material strength, production batch, installation date, and maintenance cycle.

[0067] The number of component information items at each stage was counted, and the corresponding number of data records was determined. During the design phase, a total of 16,500 design records were generated for components such as light steel keel composite wall panels, light steel keel composite floor slabs, light steel stair modules, prefabricated balconies, and prefabricated air conditioning panels. During the production phase, processing details and quality inspection reports were generated for each light steel integrated housing module, resulting in 16,500 production records, corresponding one-to-one with the design phase records. During the transportation phase, 5,600 transportation records were obtained after splitting the shipment batches, as some components were shipped together. During the construction phase, 23,000 construction records were generated based on the hoisting and acceptance procedures for each component. During the operation and maintenance phase, annual inspection and maintenance plans were preset for each component, resulting in 50,000 operation and maintenance records, covering all components.

[0068] The information range for each stage of a component is determined based on the number of data records, the size of a single record, and the starting number of the component information for each stage. A unified coding system for all components is pre-agreed upon: project code 2024NP plus stage code DPTCO corresponding to design, production, transportation, construction, and operation / maintenance respectively, followed by a 6-digit serial number. The starting number for the design stage is 2024NPD000001, and the average data size of a single design record is 2.5 kilobytes. The design stage has 16,500 records; therefore, the information range for the design stage is from number 2024NPD000001 to 2024NPD016500.

[0069] The production phase starts with number 2024NPP000001, containing 16,500 records, each 3.2 kilobytes in size, with information ranging up to 2024NPP016500; the transportation phase starts with number 2024NPT000001, containing 5,600 records, each 1.8 kilobytes in size, with information ranging up to 2024NPT005600; the construction phase starts with number 2024NPC000001, containing 23,000 records, each 4.1 kilobytes in size, with information ranging up to 2024NPC023000; and the maintenance phase starts with number 2024NPO000001, containing 50,000 records, each 2.0 kilobytes in size, with information ranging up to 2024NPO050000.

[0070] After initialization, the starting numbers, number of records, and corresponding storage space partitions are written to the project configuration database, and the global control flags are updated to the ready state for each stage. After that, when any module calls the component information, it can quickly locate the stage and storage location based on the coding range. For example, if the number 2024NPC012345 falls within the construction stage, the construction management module will be automatically called for processing. This completes the data initialization for the entire lifecycle and the determination of the component information range.

[0071] In an optional embodiment, establishing a target association matrix based on a standardized component information database includes:

[0072] S301. Use the cloud access index of each stage of component information layer in the standardized component information database as the starting row of the matrix, and use the starting row of the matrix to create an initial association matrix;

[0073] S302. Take the starting number of the component information of each stage in the BIM model data as the starting component identifier, and increment the starting component identifier based on the number of component information of each stage in the BIM model data to obtain the component identifier sequence.

[0074] S303. Use the component identifier sequence as a matrix index and establish the target association matrix based on the initial association matrix.

[0075] It should be noted that the project team extracted the cloud access indexes of the component information layer at each stage from the standardized component information database. These indexes are Uniform Resource Identifiers that point to specific data objects in cloud storage. The cloud access indexes are arranged in the order of the stages and used as the starting row of the matrix, thus creating an initial association matrix. This initial matrix currently has only one row, containing the access entry points for the five stages, which will be used to establish a mapping with the component identifiers later.

[0076] Determine the starting number of the component information for each stage; based on the previous data initialization results, the starting component identifier for the design stage is 2024NPD000001, the starting identifier for the production stage is 2024NPP000001, the starting identifier for the transportation stage is 2024NPT000001, the starting identifier for the construction stage is 2024NPC000001, and the starting identifier for the operation and maintenance stage is 2024NPO000001; based on the number of data records for each stage, increment each starting component identifier to obtain a complete component identifier sequence;

[0077] For example, there are 16,500 records in the design phase. Therefore, the component identification sequence in the design phase is a series of consecutive integer numbers from 2024NPD000001 to 2024NPD016500; the production phase is from 2024NPP000001 to 2024NPP016500; the transportation phase is from 2024NPT000001 to 2024NPT005600; the construction phase is from 2024NPC000001 to 2024NPC023000; and the operation and maintenance phase is from 2024NPO000001 to 2024NPO050000. The component identification sequences of all phases are combined to form a complete identification list covering 50,000 components of the entire project.

[0078] The above component identifier sequence is used as the row index of the matrix, and the target association matrix is ​​built based on the previously created initial association matrix. The specific operation is as follows: the initial association matrix has only one row, namely the cloud access index row. The content of this row is copied for each component identifier, but the corresponding specific storage address is automatically filled according to the stage to which the component belongs. For example, for component identifier 2024NPD000101, which belongs to the design stage, its row record in the target association matrix is ​​the cloud access index address of the design stage. At the same time, the row also includes the path to the specific BIM model sub-file of the component, such as the precise location after splicing the component ID under the same index. For component identifier 2024NPC012345, which belongs to the construction stage, its corresponding row points to the cloud access index of the construction stage.

[0079] The final generated target association matrix is ​​a two-dimensional structure with the total number of rows (50,000) representing all component identifiers and the number of columns representing the number of storage attribute fields for each stage. Each cell in the matrix stores the mapping relationship from the component identifier to the corresponding stage's cloud storage location. This target association matrix is ​​deployed in a NoSQL database on the cloud platform, supporting the rapid retrieval of data storage locations for any stage by component identifier, thereby achieving unified indexing and efficient retrieval of data throughout the entire lifecycle.

[0080] In an optional embodiment, before inputting the target engineering data into the cloud-based collaborative management engine, the following method is further included:

[0081] Data space for each construction node is allocated based on the target association matrix, and corresponding target node identifiers are set; wherein, the target node identifier points to the registered project root node;

[0082] Define the data synchronization granularity for each construction node and bind it to a set of operation functions for cloud data; the set of operation functions includes conflict handling functions.

[0083] Define the target interaction interface for the management side, so as to obtain the data query requests sent by the management side using the target interaction interface.

[0084] It should be noted that after the project enters the construction phase, the cloud-based collaborative management platform allocates independent data space to each construction node based on the established target association matrix. Construction nodes are divided according to floors and flow sections. For example, the standard floors of Building 1 are divided into two construction nodes, the East Section and the West Section, totaling thirty nodes. The platform sets a target node identifier for each construction node, which adopts a unified format: the project root node identifier is Proj2024NP, and the construction node identifier extends from the root node. For example, the East Section node identifier is Proj2024NP.B1.E, and the West Section node identifier is Proj2024NP.B1.W, where B1 represents Building 1. All construction node identifiers have been registered with the cloud registration center when the project starts, ensuring that each node can be addressed from the root node.

[0085] The platform defines the data synchronization granularity for each construction node. Based on the characteristics of prefabricated building construction, the synchronization granularity is divided into three levels: component level, process level, and parameter level. The component level uses a single light steel integrated housing module as the unit; for example, after a light steel keel composite wall panel is hoisted, its position and attitude data are synchronized as a whole. The process level uses specific processes such as hoisting, node connection, and acceptance as the unit; the quality inspection record of each process is synchronized upon completion. The parameter level uses a single monitoring parameter as the unit; for example, the reading of a pre-embedded strain gauge is synchronized every five seconds. The eastern construction node, due to its large tower crane coverage area and fast construction pace, adopts the component-level synchronization granularity to reduce the number of network interactions. The western construction node, due to its proximity to densely populated equipment areas and the need for precise quality control, adopts the process-level synchronization granularity.

[0086] Each construction node is initialized with a set of operation functions for cloud data. This set includes data upload, data download, data query, and conflict handling functions. Taking the conflict handling function as an example, when the actual installation elevation of a composite slab uploaded by a handheld terminal on site is inconsistent with the preset elevation in the BIM model, the conflict handling function will execute the following logic: First, it reads the latest construction stage data of the component in the target association matrix. Then, it compares the site timestamp with the cloud timestamp. If the site timestamp is updated, the site data is adopted and the component is marked as needing review, while the quality manager is notified. If the cloud timestamp is updated, the site upload is rejected and the correct elevation value is sent to the terminal. This conflict handling function also supports an optimistic locking mechanism when multiple terminals upload simultaneously to ensure data consistency.

[0087] The platform defines the target interaction interface for the management end; this interface adopts a RESTful style and is deployed on the gateway layer of the cloud-based collaborative management engine, with the address:

[0088] https: / / api.collabengine.cn / v1 / project / data; The management interface, i.e., the desktop application or web console used by the project manager or BIM coordinator, sends data query requests through this interface; for example, if the project manager needs to query all component records for bolted connection nodes with hole diameters below 90% in the eastern section of Building 1, he constructs an HTTP GET request through the management interface with the following path:

[0089] The request body of / nodes / Proj2024NP.B1.E / queries contains the query condition of comparing aperture detection values ​​with thresholds. After receiving the request, the target interaction interface first verifies the identity token of the management end, then locates the corresponding data space according to the target node identifier, and then calls the data query function in the operation function set to retrieve relevant records from the time series database of that node.

[0090] The search results are returned in JSON format, including component identification, inspection time, measured fullness value, and recommended handling measures. The interface also supports asynchronous query mode. When the query scope involves multiple construction nodes, the interface returns a task ID, which the management end can poll to obtain the final result. In addition, the interface provides a subscription function, allowing the management end to register data change events of interest. For example, when any construction node has a conflict and triggers the conflict handling function, the interface automatically pushes an alarm message to the management end's message queue. In this way, the management end can be informed of on-site anomalies in real time without actively polling.

[0091] In an optional embodiment, multi-source data fusion processing is performed on BIM model data and construction site collected data using a target correlation matrix to obtain target project data, including:

[0092] Obtain multi-source data retrieval requests, and based on the multi-source data retrieval requests and the target association matrix, determine the corresponding component number and cloud storage path; where the component number is a unique identifier for the component information, and the cloud storage path is the storage location of the cloud data;

[0093] A cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths;

[0094] Based on the multi-source data retrieval request, the corresponding target storage path is selected from the cloud index table so that BIM model data and construction site data can be obtained from the location corresponding to the target storage path for subsequent multi-source data fusion processing.

[0095] It should be noted that the project quality administrator needs to retrieve the bolt connection node inspection data of a certain precast exterior wall panel in the eastern section of Building 1 during the construction phase, as well as the corresponding steel keel layout model in the design phase, in order to analyze whether there are potential quality problems with the component. The administrator submitted a multi-source data retrieval request through the target interaction interface of the management terminal. The request content includes the floor information where the component is located, the component type, and the range of data stages requested. The format of the request is a JSON object, which specifies that the component location is between axis A and B on the fifth floor of Building 1 in the eastern section, the component name is precast exterior wall panel, and the data stages to be retrieved include the design phase and the construction phase.

[0096] After receiving the multi-source data retrieval request, the cloud-based collaborative management platform first parses the location conditions in the request and converts them into standardized component numbers. Based on pre-established mapping rules between floor areas and component codes, the platform determines the component number of the exterior wall panel to be 2024NPC050123, where NPC represents a unified numbering prefix for the construction phase, but the actual component has an independent numbering system at different stages. The target association matrix records the numbering mapping relationship of the component from design to operation and maintenance. The platform queries the target association matrix and finds that component number 2024NPD050123 corresponds to the design phase, number 2024NPP050123 corresponds to the production phase, number 2024NPT050123 corresponds to the transportation phase, number 2024NPC050123 corresponds to the construction phase, and number 2024NPO050123 corresponds to the operation and maintenance phase.

[0097] In addition, each row of the target association matrix also records the cloud storage path of the component at different stages, for example, the path during the design stage is:

[0098] cloud: / / project2024 / design / 2024NPD050123.rvt;

[0099] The construction phase path is as follows:

[0100] cloud: / / project2024 / construction / inspection / 2024NPC050123_grout.json;

[0101] The cloud-based index table is a distributed hash table with a key-value pair structure. The key is the component number, and the value is a list of storage paths for all stages of the component's lifecycle. The construction process is automatically executed by the data initialization module: it iterates through each row of the target association matrix, extracts the component number and its corresponding storage path, and merges different stage paths for the same component number into a single record. For example, for component 2024NPD050123, the index table will generate a record with the key 2024NPD050123 and the value an array containing paths for the five stages: design, production, transportation, construction, and maintenance. The same format is used for other component numbers. This index table is stored in a Redis cache cluster on the cloud platform, supporting millisecond-level queries.

[0102] After receiving a multi-source data retrieval request, the platform locates component numbers 2024NPD050123 and 2024NPC050123 based on the component location information specified in the request. It then filters the corresponding target storage paths from the cloud index table. Since the request requires data from both the design and construction phases, the platform only extracts the design and construction phase paths, ignoring paths from the production, transportation, and maintenance phases. After filtering, two target storage paths are obtained: the design phase path points to the component's BIM model sub-file, and the construction phase path points to the component's aperture detection JSON file. The platform reads data from these two paths using the cloud storage SDK: from the design phase path, it obtains BIM model data such as the 3D layout of the steel keel, steel strength grade, and embedded part locations of the exterior wall panel; from the construction phase path, it obtains raw ultrasonic waveforms of the bolt connection nodes, fullness calculation values, inspection personnel, and timestamps collected at the construction site.

[0103] The two types of data acquired are sent to the multi-source data fusion processing module. This module first aligns the spatial coordinates of the geometric information from the design phase with the measured information from the construction phase, and matches the theoretical position of the bolt connection node in the model with the actual position detected by the sensor. Then, the fusion module generates an enhanced data object, which is passed to the conflict detection and quality assessment engine. Finally, the deficiency of the third bolt connection node of the component is highlighted on the management interface.

[0104] In an optional embodiment, determining the corresponding cloud storage path based on the multi-source data retrieval request and the target association matrix includes:

[0105] Based on the number offset of the target component corresponding to the multi-source data retrieval request, the corresponding target component identifier is determined;

[0106] Filter the target storage addresses corresponding to the target component identifiers from the target association matrix;

[0107] Use the target storage address to determine the corresponding cloud storage path.

[0108] It should be noted that the quality inspector at the project site discovered a minor crack on the surface of a light steel keel composite wall panel on the fifth floor west section, requiring the retrieval of vibration monitoring data from the transportation phase of the component's production. The inspector entered the panel's site number via a handheld terminal, which was in the form of a floor plus location code. The terminal automatically converted this into a component number offset. The component numbering for this project follows a unified rule: the starting number in the design phase is 2024NPD000001, and each subsequent component increments according to the design sequence. The composite panel indicated by the inspector is component number 12480 in the overall design phase. The multi-source data retrieval request carried the target component's number offset, i.e., an offset of 12479 steps from the starting number.

[0109] After receiving the request, the cloud-based collaborative management platform first parses the number offset. Knowing that the starting component number in the design phase is 2024NPD000001, the platform adds the offset 12479 to the starting number, obtaining the complete identifier of the target component in the design phase as 2024NPD012480. However, this component has its own independent numbering system in the production, transportation, construction, and operation and maintenance phases, and the platform needs to find these corresponding identifiers. The platform maintains a number mapping table internally, which can look up the component's number in each phase based on the design phase number: the production phase corresponds to 2... 024NPP012480 corresponds to the transportation stage (2024NPT004251), the construction stage (2024NPC018792), and the operation and maintenance stage (2024NPO012480). The transportation stage number differs from the design stage number because the transportation records are renumbered according to merged batches, resulting in a change in offset. Based on the data stage specified in the multi-source data retrieval request as the production and transportation stages, the platform ultimately determines the target component identifiers to be queried as 2024NPP012480 and 2024NPT004251.

[0110] The platform filters the target storage addresses corresponding to the two target component identifiers from the target association matrix. The target association matrix is ​​a large-scale key-value storage structure, where the row key is the component identifier, and each row contains multiple column families, each corresponding to a storage address at a different stage. The platform first queries the row with the key 2024NPP012480, extracts the storage address field from the production stage column family of that row, and obtains the target storage address as follows:

[0111] The address cloud: / / project2024 / transport / vehicle03 / 2024NPT004251_vibration.csv points to the acceleration time-series data recorded by the vibration sensors of the transport vehicle;

[0112] Finally, the platform uses these two target storage addresses to determine the complete cloud storage path; cloud storage services require the path format to be the bucket name plus the object key; the platform resolves the storage address to the bucket project2024 and the corresponding object key; the complete cloud storage path for production stage data is:

[0113] The complete path to the transport phase data is: https: / / storage.example.com / project2024 / production / batch05 / 2024NPP012480_heatmap.json.

[0114] https: / / storage.example.com / project2024 / transport / vehicle03 / 2024NPT004251_vibration.csv; The platform reads data from these two paths via HTTP GET requests and returns the temperature records and vibration data to the inspector's handheld terminal. The terminal overlays and displays the peak impact acceleration during transportation on the screen to help determine the cause of the cracks.

[0115] In an optional embodiment, a cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths, including:

[0116] Determine the range of component numbers corresponding to multi-source data retrieval requests;

[0117] If the number range and the corresponding cloud storage path meet the preset batch synchronization conditions, then a cloud index table is constructed.

[0118] It should be noted that during the mid-stage of prefabricated building construction, the project's technical manager needed to obtain the construction and acceptance data of all light steel keel composite wall panels in Building 1 in batches to analyze the overall deviation trend of hoisting verticality. The technical manager submitted a multi-source data retrieval request through the management terminal, specifying that the query object was all light steel keel composite wall panels in Building 1, covering floors from the first to the tenth floor. Parsing the request, the corresponding component number range was determined. According to the coding rules of light steel keel composite wall panels, the wall panel numbers on the first floor of Building 1 start from 2024NPC010001, with approximately 200 wall panels per floor, until the last wall panel on the tenth floor is numbered 2024NPC020000. Therefore, the component number range corresponding to this request is from 2024NPC010001 to 2024NPC020000, totaling 10,000 components.

[0119] The system determines whether the number range and the corresponding cloud storage path meet the preset batch synchronization conditions. The preset batch synchronization conditions for the project include two sub-conditions: first, the number of components within the number range exceeds 2000; second, the total data size of these components under the cloud storage path exceeds 50 megabytes. The number of components counted is 10000, far exceeding the 2000 threshold. Then, the system queries the construction stage storage path corresponding to each component number in the target association matrix one by one, and accumulates the file sizes pointed to by these paths. Through cloud storage metadata pre-reading, it is found that the average size of the construction acceptance data file of each component is 25 kilobytes, and the total size of 10000 components is approximately 250 megabytes, which also exceeds the 50 megabyte threshold. Since both sub-conditions are met, the retrieval request is determined to meet the preset batch synchronization conditions.

[0120] Since the batch synchronization conditions are met, the operation of building the cloud index table is triggered. The building process is as follows: traverse each component number within the number range from 2024NPC010001 to 2024NPC020000, and extract the cloud storage address of the construction stage corresponding to each component from the target association matrix. For example, the address of component 2024NPC010001 is:

[0121] The address of component 2024NPC010002 is: cloud: / / project2024 / construction / acceptance / 2024NPC010001.json.

[0122] cloud: / / project2024 / construction / acceptance / 2024NPC010002.json, and so on; these key-value pairs are written in JSON format to a new index table file named batch_idx_floor1to10.json, and stored in the temporary index directory of the cloud platform; the structure of the index table is an array, and each element contains a component number field and a storage path field;

[0123] Once the index table is built, its cloud storage path is returned to the management system. When the technical lead performs multi-source data fusion processing, they only need to download one index table file to obtain the storage paths of all 10,000 components in batches, without having to query the target association matrix one by one, which greatly improves data retrieval efficiency. At the same time, a 24-hour validity period is set for the index table, and it will be automatically deleted after the validity period expires to save storage space.

[0124] In an optional embodiment, after determining the number range of the component number corresponding to the multi-source data retrieval request, the method further includes:

[0125] Determine the data space size of the component information and the starting component number from the number range;

[0126] Determine whether the data space size of the component information meets the preset batch synchronization conditions, and determine whether the starting component number meets the preset batch synchronization conditions;

[0127] Determine the cloud storage path corresponding to the starting component number from the target association matrix;

[0128] Determine whether the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions;

[0129] If the data space size of the component information meets the preset batch synchronization conditions, and the starting component number meets the preset batch synchronization conditions, and the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions, then it is determined that the number range and the cloud storage path corresponding to the number range meet the preset batch synchronization conditions.

[0130] It should be noted that as the project entered the later stages of main structure construction, the data analyst needed to obtain complete data on the production, transportation, and construction of all light steel staircase modules in Building 2 in batches for component quality traceability analysis. The analyst submitted a multi-source data retrieval request through the management terminal, specifying to query all light steel staircase modules in Building 2, covering floors from the first to the fifteenth floor. The data space size of the component information and the starting component number were determined from this number range.

[0131] The coding rule for the light steel staircase modules of Building 2 is as follows: the starting number in the design phase is 2024NPD030001, each staircase occupies one number, and there are a total of 30 staircase components. The average data size of each component in a single phase is 120 kilobytes, and the total data volume of the three phases is 30 multiplied by 120 kilobytes multiplied by 3, which equals 10,800 kilobytes, or approximately 10.8 megabytes; the data space size is determined to be 10.8 megabytes, and the starting component number is 2024NPD030001;

[0132] Determine whether the data space size of the component information meets the preset batch synchronization conditions; the size threshold in the preset batch synchronization conditions of the project is that the data space is greater than 5 megabytes; 10.8 megabytes is greater than 5 megabytes, therefore the data space size condition is met;

[0133] The system checks if the starting component number meets the preset batch synchronization conditions. These conditions require the starting component number to have an offset alignment: the last six digits of the starting component number must be 000001, meaning it must be the number of the first component in a certain phase. The last six digits of 2024NPD030001 are 030001, not 000001. The check reveals that the component corresponding to this starting component number is not the first component in this phase, thus failing the starting component number condition. However, the preset batch synchronization conditions also allow another scenario: the last three digits of the starting component number must be 001 to pass, because the floor starting component number is allowed to be based on the first component of the floor. The last three digits of 030001 are 001, which meets the allowed rule. Therefore, the starting component number condition passes.

[0134] Determine the cloud storage path corresponding to the starting component number from the target association matrix; for the starting component number 2024NPD030001, query the target association matrix to obtain its cloud storage path for the design phase:

[0135] The production storage path is: cloud: / / project2024 / design / stair / 2024NPD030001.rvt.

[0136] The storage path for the transportation phase is: cloud: / / project2024 / production / stair / 2024NPP030001.json.

[0137] cloud: / / project2024 / transport / stair / 2024NPT009801.csv; The production stage path is selected as the verification object because batch synchronization mainly focuses on the actual data of production and transportation.

[0138] Determine whether the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions. The preset batch synchronization conditions require that the read latency of the object storage bucket where the storage path is located is less than 200 milliseconds, and that a batch prefetch flag file exists in the parent directory of the path. Perform latency probing on the storage bucket and find that the average read latency is 85 milliseconds, which is less than 200 milliseconds. At the same time, check whether a flag file named batch_prefetch exists in the cloud: / / project2024 / production / stair / directory. The check result is that it exists, indicating that batch prefetch optimization has been enabled in this directory. Therefore, the cloud storage path conditions are also met.

[0139] Since the data space size, the starting component number, and the cloud storage path corresponding to the starting component are all satisfied, it is determined that the number range from 2024NPD030001 to 2024NPD030030 and the cloud storage paths corresponding to these numbers all meet the preset batch synchronization conditions. Subsequently, the construction of a cloud index table is triggered, and all storage paths of the 30 stair components in the three stages are summarized into an index table file for analysts to download in batches, thereby achieving efficient multi-source data fusion preparation.

[0140] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for managing information and data in prefabricated building projects based on BIM and cloud computing, characterized in that, include: Obtain BIM model data for the entire lifecycle of prefabricated building projects, and determine the scope of component information for the design, production, transportation, construction, and operation and maintenance phases based on the BIM model data; wherein, the BIM model data includes the geometric parameters and attribute information of building components at each stage; Based on the cloud computing platform and the scope of component information, the component information is parsed in layers to obtain component information layers at each stage; corresponding target identification codes are assigned to each component information layer to obtain a standardized component information database; among them, the target identification code is a unique code that represents the data structure of building components; A target association matrix is ​​established based on a standardized component information database. The target association matrix is ​​used to record the association between component identifiers in the standardized component information database and cloud storage addresses. The target association matrix is ​​used to perform multi-source data fusion processing on BIM model data and construction site data to obtain target project data. The target project data is input into the cloud-based collaborative management engine, which then performs conflict detection and quality assessment based on the data to obtain project evaluation results. The optimal component installation sequence is determined based on the project evaluation results, and the installation of the optimal component installation sequence is simulated to obtain simulation results. The optimal component installation sequence is optimized based on the simulation results to obtain the target component installation sequence, and the on-site construction of prefabricated buildings is guided based on the target component installation sequence; the actual installation data and structural monitoring data during the construction process are recorded, and the cloud-based collaborative management engine is updated based on the actual installation data and structural monitoring data.

2. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 1, characterized in that, Obtain BIM model data for the entire lifecycle of prefabricated building projects, and based on the BIM model data, determine the scope of component information for the design, production, transportation, construction, and operation and maintenance phases, including: Perform full lifecycle data initialization operations so that when the project starts, global control flags are set to control the calling of component information management modules at each stage, and BIM model data for the entire lifecycle of prefabricated building projects are obtained. Determine the number of data records corresponding to the number of component information at each stage in the BIM model data; The scope of component information for each stage is determined based on the number of data records, the size of a single record, and the starting number of the component information for each stage in the BIM model data.

3. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 2, characterized in that, Establish a target association matrix based on a standardized component information database, including: The cloud access index of each stage of component information layer in the standardized component information database is used as the starting row of the matrix, and an initial association matrix is ​​created using the starting row of the matrix. The starting number of each stage of component information in the BIM model data is used as the starting component identifier, and the starting component identifier is incremented based on the number of each stage of component information in the BIM model data to obtain the component identifier sequence. The component identifier sequence is used as the matrix index, and the target association matrix is ​​established based on the initial association matrix.

4. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 3, characterized in that, Before inputting the target project data into the cloud-based collaborative management engine, the following steps are also included: Data space for each construction node is allocated based on the target association matrix, and corresponding target node identifiers are set; wherein, the target node identifier points to the registered project root node; Define the data synchronization granularity for each construction node and bind it to a set of operation functions for cloud data; the set of operation functions includes conflict handling functions. Define the target interaction interface for the management side, so as to obtain the data query requests sent by the management side using the target interaction interface.

5. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 4, characterized in that, By using the target correlation matrix to perform multi-source data fusion processing on BIM model data and construction site data, the target project data is obtained, including: Obtain multi-source data retrieval requests, and based on the multi-source data retrieval requests and the target association matrix, determine the corresponding component number and cloud storage path; where the component number is a unique identifier for the component information, and the cloud storage path is the storage location of the cloud data; A cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths; Based on the multi-source data retrieval request, the corresponding target storage path is selected from the cloud index table so that BIM model data and construction site data can be obtained from the location corresponding to the target storage path for subsequent multi-source data fusion processing.

6. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 5, characterized in that, Based on multi-source data retrieval requests and target association matrices, the corresponding cloud storage paths are determined, including: Based on the number offset of the target component corresponding to the multi-source data retrieval request, the corresponding target component identifier is determined; Filter the target storage addresses corresponding to the target component identifiers from the target association matrix; Use the target storage address to determine the corresponding cloud storage path.

7. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 6, characterized in that, A cloud index table is constructed based on the mapping relationship between component numbers and cloud storage paths, including: Determine the range of component numbers corresponding to multi-source data retrieval requests; If the number range and the corresponding cloud storage path meet the preset batch synchronization conditions, then a cloud index table is constructed.

8. The method for managing information data of prefabricated building projects based on BIM and cloud computing according to claim 7, characterized in that, After determining the number range of component numbers corresponding to the multi-source data retrieval request, the process also includes: Determine the data space size of the component information and the starting component number from the number range; Determine whether the data space size of the component information meets the preset batch synchronization conditions, and determine whether the starting component number meets the preset batch synchronization conditions; Determine the cloud storage path corresponding to the starting component number from the target association matrix; Determine whether the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions; If the data space size of the component information meets the preset batch synchronization conditions, and the starting component number meets the preset batch synchronization conditions, and the cloud storage path corresponding to the starting component number meets the preset batch synchronization conditions, then it is determined that the number range and the cloud storage path corresponding to the number range meet the preset batch synchronization conditions.

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